LuxLogAnalysis.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import glob\n",
    "import json\n",
    "import pandas as pd\n",
    "import datetime\n",
    "import numpy as np\n",
    "import scipy.stats as sp\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "598\n"
     ]
    }
   ],
   "source": [
    "# gets every log from directory\n",
    "all_logs = glob.glob(\"logs/*.json\")\n",
    "print(len(all_logs))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# formats all logs properly \n",
    "# key = session ID\n",
    "# value = json w/ same format as what was originally held in nb metadata\n",
    "formatted_logs = {}\n",
    "\n",
    "for log in all_logs:\n",
    "    f = open(log, 'r')\n",
    "    if log == 'logs/0330c0848a8873feb20b5b8af2dc3ae6undefined.json':\n",
    "        print(log)\n",
    "        print(f.read())\n",
    "    # merges body sent from various HTTP Posts\n",
    "#     print(f.read())\n",
    "    entries = f.read().split('}{')\n",
    "#     print(entries[0])\n",
    "    for i in range(len(entries)):\n",
    "        entries[i] = '{' + entries[i] + '}'\n",
    "    entries[0] = entries[0][1:]\n",
    "    entries[-1] = entries[-1][:-1]\n",
    "#     print(entries[0])\n",
    "    formatted_log = json.loads(entries[0])\n",
    "    for i in range(1,len(entries)):\n",
    "        j = json.loads(entries[i])\n",
    "        history = j['history']\n",
    "        formatted_log['history'].extend(history)\n",
    "    \n",
    "    # get time user started ipynb\n",
    "    formatted_log['startTime'] = formatted_log['history'][0]['time']\n",
    "    formatted_log['endTime'] = formatted_log['history'][len(formatted_log['history'])-1]['time']\n",
    "    \n",
    "    # separates user hash from file name (sessionID)\n",
    "    names = log.split('_')\n",
    "    formatted_log['userHash'] = names[0][5:]\n",
    "    \n",
    "    # excluding Jerry and Doris and potentially others\n",
    "    exclude_list = ['bca87887a1cc89312f7d073fd007ea68', '1a735d0ee6a6f9d7fdab573b50851da7']\n",
    "    if names[0][5:] not in exclude_list:\n",
    "        formatted_logs[names[1][:-5]] = formatted_log\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "477"
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     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(formatted_logs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.DataFrame(formatted_logs).transpose().sort_values('userHash')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
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      "text/plain": [
       "                              nbName  \\\n",
       "czxswqqjy  1-specifying-intent.ipynb   \n",
       "6b6nhxtxh            cars_demo.ipynb   \n",
       "8pzgwyy22  1-specifying-intent.ipynb   \n",
       "ug7hr39o8            cars_demo.ipynb   \n",
       "97vczg8cn            cars_demo.ipynb   \n",
       "...                              ...   \n",
       "f1ukitdr9     1-Specify-Intent.ipynb   \n",
       "ku8fm3jm6          2-Quick-Vis.ipynb   \n",
       "o995ki32j            cars_demo.ipynb   \n",
       "jiklvm3yu            cars_demo.ipynb   \n",
       "ru4jx8w5v            cars_demo.ipynb   \n",
       "\n",
       "                                                     history  \\\n",
       "czxswqqjy  [{'type': 'executeCodeCell', 'time': '2020-12-...   \n",
       "6b6nhxtxh  [{'type': 'executeCodeCell', 'time': '2020-12-...   \n",
       "8pzgwyy22  [{'type': 'executeCodeCell', 'time': '2020-12-...   \n",
       "ug7hr39o8  [{'type': 'executeCodeCell', 'time': '2020-11-...   \n",
       "97vczg8cn  [{'type': 'executeCodeCell', 'time': '2020-12-...   \n",
       "...                                                      ...   \n",
       "f1ukitdr9  [{'type': 'executeCodeCell', 'time': '2020-11-...   \n",
       "ku8fm3jm6  [{'type': 'executeCodeCell', 'time': '2020-11-...   \n",
       "o995ki32j  [{'type': 'executeCodeCell', 'time': '2020-12-...   \n",
       "jiklvm3yu  [{'type': 'executeCodeCell', 'time': '2020-12-...   \n",
       "ru4jx8w5v  [{'type': 'executeCodeCell', 'time': '2020-11-...   \n",
       "\n",
       "                          startTime                   endTime  \\\n",
       "czxswqqjy  2020-12-27T02:41:41.804Z  2020-12-27T03:01:14.187Z   \n",
       "6b6nhxtxh  2020-12-27T01:20:56.927Z  2020-12-27T01:47:49.232Z   \n",
       "8pzgwyy22  2020-12-27T03:02:30.953Z  2020-12-27T03:12:26.956Z   \n",
       "ug7hr39o8  2020-11-30T05:36:43.016Z  2020-11-30T06:11:08.975Z   \n",
       "97vczg8cn  2020-12-03T02:55:46.498Z  2020-12-03T03:12:19.732Z   \n",
       "...                             ...                       ...   \n",
       "f1ukitdr9  2020-11-04T21:34:36.099Z  2020-11-04T21:47:49.607Z   \n",
       "ku8fm3jm6  2020-11-04T21:56:31.543Z  2020-11-04T22:14:47.763Z   \n",
       "o995ki32j  2020-12-01T06:17:35.472Z  2020-12-01T06:34:09.014Z   \n",
       "jiklvm3yu  2020-12-25T16:37:15.015Z  2020-12-25T17:06:12.739Z   \n",
       "ru4jx8w5v  2020-11-30T00:37:42.834Z  2020-11-30T00:39:07.991Z   \n",
       "\n",
       "                                   userHash  \n",
       "czxswqqjy  003724cc5e41a981d3fad0ca3f21888d  \n",
       "6b6nhxtxh  003724cc5e41a981d3fad0ca3f21888d  \n",
       "8pzgwyy22  003724cc5e41a981d3fad0ca3f21888d  \n",
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       "97vczg8cn  006b045214badd8a59f7f11c1eae488c  \n",
       "...                                     ...  \n",
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       "jiklvm3yu  feba0e7c024be16c21b46a6257a10367  \n",
       "ru4jx8w5v  fff0c89988ecc9587435c7f48dc586ee  \n",
       "\n",
       "[477 rows x 5 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "310"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Number of unique IPs data was sent from\n",
    "df['userHash'].nunique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Creates userGroups based on when notebook was first used\n",
    "def groupDate(x):\n",
    "    if x['startTime'].date() <= datetime.date(2020, 11, 1):\n",
    "        return 'RiseCamp'\n",
    "    elif x['startTime'].date() > datetime.date(2020, 11, 3) and x['startTime'].date() <= datetime.date(2020, 11, 5):\n",
    "        return 'USF'\n",
    "    else:\n",
    "        return 'Other'\n",
    "df['startTime'] = pd.to_datetime(df['startTime'])\n",
    "df['endTime'] = pd.to_datetime(df['endTime'])\n",
    "df['userGroup'] = df.apply(groupDate, axis=1)\n",
    "df = df.sort_values('startTime')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
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       "      <td>Other</td>\n",
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       "      <td>...</td>\n",
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       "      <th>2b21sgaq4</th>\n",
       "      <td>2-Quick-Vis.ipynb</td>\n",
       "      <td>[{'type': 'executeCodeCell', 'time': '2020-11-...</td>\n",
       "      <td>2020-11-04 21:52:03.810000+00:00</td>\n",
       "      <td>2020-11-04 22:06:27.297000+00:00</td>\n",
       "      <td>c970dc2661f9bafca1c9f7c959e5fcde</td>\n",
       "      <td>USF</td>\n",
       "    </tr>\n",
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       "      <th>m2xc6ec08</th>\n",
       "      <td>2-Quick-Vis.ipynb</td>\n",
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       "      <td>52dacd9e8e026c076c886a89c4a84b9a</td>\n",
       "      <td>USF</td>\n",
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       "      <th>q7onw2fq2</th>\n",
       "      <td>2-Quick-Vis.ipynb</td>\n",
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       "      <td>aea7f8e69322a459e5639238c290c57e</td>\n",
       "      <td>USF</td>\n",
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       "      <td>2020-11-04 21:58:30.376000+00:00</td>\n",
       "      <td>52dacd9e8e026c076c886a89c4a84b9a</td>\n",
       "      <td>USF</td>\n",
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       "      <td>2020-11-04 22:15:29.253000+00:00</td>\n",
       "      <td>2020-11-04 22:28:06.164000+00:00</td>\n",
       "      <td>d7d71e1ad6fd34704dd32b5de3e65d34</td>\n",
       "      <td>USF</td>\n",
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       "                            nbName  \\\n",
       "4wirrulrw         0-overview.ipynb   \n",
       "6rnmory2o          cars_demo.ipynb   \n",
       "7dtoihteg          cars_demo.ipynb   \n",
       "24wybqbtc   4-chart-settings.ipynb   \n",
       "qrog5bnzu          cars_demo.ipynb   \n",
       "...                            ...   \n",
       "2b21sgaq4        2-Quick-Vis.ipynb   \n",
       "m2xc6ec08        2-Quick-Vis.ipynb   \n",
       "q7onw2fq2        2-Quick-Vis.ipynb   \n",
       "2gqzflfxq    3-Export-Widget.ipynb   \n",
       "mnf9invp3  4-Data-Playground.ipynb   \n",
       "\n",
       "                                                     history  \\\n",
       "4wirrulrw  [{'type': 'executeCodeCell', 'time': '2020-12-...   \n",
       "6rnmory2o  [{'type': 'executeCodeCell', 'time': '2020-12-...   \n",
       "7dtoihteg  [{'type': 'executeCodeCell', 'time': '2020-12-...   \n",
       "24wybqbtc  [{'type': 'executeCodeCell', 'time': '2020-12-...   \n",
       "qrog5bnzu  [{'type': 'selectCodeCell', 'time': '2020-12-0...   \n",
       "...                                                      ...   \n",
       "2b21sgaq4  [{'type': 'executeCodeCell', 'time': '2020-11-...   \n",
       "m2xc6ec08  [{'type': 'executeCodeCell', 'time': '2020-11-...   \n",
       "q7onw2fq2  [{'type': 'executeCodeCell', 'time': '2020-11-...   \n",
       "2gqzflfxq  [{'type': 'executeCodeCell', 'time': '2020-11-...   \n",
       "mnf9invp3  [{'type': 'executeCodeCell', 'time': '2020-11-...   \n",
       "\n",
       "                                 startTime                          endTime  \\\n",
       "4wirrulrw 2020-12-01 15:34:34.845000+00:00 2020-12-01 15:37:52.896000+00:00   \n",
       "6rnmory2o 2020-12-08 22:03:54.602000+00:00 2020-12-08 22:04:01.618000+00:00   \n",
       "7dtoihteg 2020-12-08 06:49:10.098000+00:00 2020-12-08 06:51:09.017000+00:00   \n",
       "24wybqbtc 2020-12-07 20:18:02.166000+00:00 2020-12-07 20:18:02.166000+00:00   \n",
       "qrog5bnzu 2020-12-07 20:14:28.310000+00:00 2020-12-07 20:14:33.180000+00:00   \n",
       "...                                    ...                              ...   \n",
       "2b21sgaq4 2020-11-04 21:52:03.810000+00:00 2020-11-04 22:06:27.297000+00:00   \n",
       "m2xc6ec08 2020-11-04 21:52:17.359000+00:00 2020-11-04 22:01:28.290000+00:00   \n",
       "q7onw2fq2 2020-11-04 21:53:17.697000+00:00 2020-11-04 22:06:48.111000+00:00   \n",
       "2gqzflfxq 2020-11-04 21:58:24.082000+00:00 2020-11-04 21:58:30.376000+00:00   \n",
       "mnf9invp3 2020-11-04 22:15:29.253000+00:00 2020-11-04 22:28:06.164000+00:00   \n",
       "\n",
       "                                   userHash userGroup  \n",
       "4wirrulrw  200919f12a8ed20669ce2166d9c24530     Other  \n",
       "6rnmory2o  324cf58e404d277827091ccaf56bba99     Other  \n",
       "7dtoihteg  77f91d3c5bf083e9853822321610ffeb     Other  \n",
       "24wybqbtc  1f3de656a45930f32a169f2ce8df6934     Other  \n",
       "qrog5bnzu  43325b9982f800c4b34f9b1be0dc39b2     Other  \n",
       "...                                     ...       ...  \n",
       "2b21sgaq4  c970dc2661f9bafca1c9f7c959e5fcde       USF  \n",
       "m2xc6ec08  52dacd9e8e026c076c886a89c4a84b9a       USF  \n",
       "q7onw2fq2  aea7f8e69322a459e5639238c290c57e       USF  \n",
       "2gqzflfxq  52dacd9e8e026c076c886a89c4a84b9a       USF  \n",
       "mnf9invp3  d7d71e1ad6fd34704dd32b5de3e65d34       USF  \n",
       "\n",
       "[477 rows x 6 columns]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.sort_values('userGroup')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'2-Quick-Vis.ipynb': 6,\n",
       " 'cars_demo.ipynb': 276,\n",
       " '1-Specify-Intent.ipynb': 4,\n",
       " '1-specifying-intent.ipynb': 13,\n",
       " '3-widget-vis-export.ipynb': 7,\n",
       " '4-chart-settings.ipynb': 6,\n",
       " '5-datetime.ipynb': 10,\n",
       " '7-formal-definition.ipynb': 4,\n",
       " '0-overview.ipynb': 24,\n",
       " '6-index-group.ipynb': 5,\n",
       " 'college_demo.ipynb': 6,\n",
       " '.ipynb': 1,\n",
       " '4-Data-Playground.ipynb': 3,\n",
       " '3-Export-Widget.ipynb': 2,\n",
       " '2-constructing-vis-and-vislist.ipynb': 5,\n",
       " '8-custom-action.ipynb': 1,\n",
       " 'exploratory_demo.ipynb': 3,\n",
       " 'penguin_demo.ipynb': 4,\n",
       " 'employee_demo.ipynb': 8}"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nb_map = {}\n",
    "nbs = df[df['userGroup'] == 'Other']\n",
    "for nb in nbs['nbName']: \n",
    "    if nb in nb_map:\n",
    "        nb_map[nb] += 1\n",
    "    else:\n",
    "        nb_map[nb] = 1\n",
    "nb_map"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "388"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(nbs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "diff = nbs['endTime'] - nbs['startTime']\n",
    "diff = [x.total_seconds()%3600 / 60 for x in diff]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "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": [
    "plt.hist(diff)\n",
    "plt.xlabel(\"time in minutes\")\n",
    "plt.ylabel('counts')\n",
    "plt.title('time spent in notebooks from new users')\n",
    "plt.show()\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "def has_0(nbNames):\n",
    "    for nbName in nbNames:\n",
    "        if '0' in nbName:\n",
    "            return 1\n",
    "    return 0\n",
    "def has_1(nbNames):\n",
    "    for nbName in nbNames:\n",
    "        if '1' in nbName:\n",
    "            return 1\n",
    "    return 0\n",
    "def has_2(nbNames):\n",
    "    for nbName in nbNames:\n",
    "        if '2' in nbName:\n",
    "            return 1\n",
    "    return 0\n",
    "def has_3(nbNames):\n",
    "    for nbName in nbNames:\n",
    "        if '3' in nbName:\n",
    "            return 1\n",
    "    return 0\n",
    "def has_4(nbNames):\n",
    "    for nbName in nbNames:\n",
    "        if '4' in nbName:\n",
    "            return 1\n",
    "    return 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>userGroup</th>\n",
       "      <th>startTime</th>\n",
       "      <th>endTime</th>\n",
       "      <th>tried_0</th>\n",
       "      <th>tried_1</th>\n",
       "      <th>tried_2</th>\n",
       "      <th>tried_3</th>\n",
       "      <th>tried_4</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>userHash</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>e0157f975b2aecbe652c4706db61a112</th>\n",
       "      <td>RiseCamp</td>\n",
       "      <td>2020-10-30 20:50:55.802000+00:00</td>\n",
       "      <td>2020-11-13 03:27:19.899000+00:00</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46c3fa396f1071a92372dc40078b42f0</th>\n",
       "      <td>RiseCamp</td>\n",
       "      <td>2020-10-30 21:23:52.207000+00:00</td>\n",
       "      <td>2020-10-30 22:28:26.332000+00:00</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>a8c4ac0672f0751b58b4738ef4488877</th>\n",
       "      <td>RiseCamp</td>\n",
       "      <td>2020-10-30 21:36:27.644000+00:00</td>\n",
       "      <td>2020-10-30 22:29:35.339000+00:00</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8ad0735d692b120e07de75cb96440fa3</th>\n",
       "      <td>RiseCamp</td>\n",
       "      <td>2020-10-30 21:40:35.290000+00:00</td>\n",
       "      <td>2020-11-02 18:28:19.854000+00:00</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3ea0843857adef1f9fd1734eb9c0a562</th>\n",
       "      <td>RiseCamp</td>\n",
       "      <td>2020-10-30 21:44:23.298000+00:00</td>\n",
       "      <td>2020-10-30 22:29:35.556000+00:00</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3b6617e83c75584d167fbe0bc90211b0</th>\n",
       "      <td>Other</td>\n",
       "      <td>2021-02-20 17:17:16.629000+00:00</td>\n",
       "      <td>2021-02-20 17:18:15.736000+00:00</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>dd9a7bec7682e860a03e46de3fd87bb6</th>\n",
       "      <td>Other</td>\n",
       "      <td>2021-02-22 01:06:51.151000+00:00</td>\n",
       "      <td>2021-02-22 01:20:55.006000+00:00</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1e42ea0b9d7d5e8a333801eb24931209</th>\n",
       "      <td>Other</td>\n",
       "      <td>2021-02-22 09:27:02.064000+00:00</td>\n",
       "      <td>2021-02-22 09:35:29.254000+00:00</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>94337423cf215d0c8800821680053cd0</th>\n",
       "      <td>Other</td>\n",
       "      <td>2021-02-22 20:57:46.136000+00:00</td>\n",
       "      <td>2021-02-22 21:17:40.871000+00:00</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>e409892210f9569708ee3b2beda05310</th>\n",
       "      <td>Other</td>\n",
       "      <td>2021-02-23 01:28:03.204000+00:00</td>\n",
       "      <td>2021-02-23 01:28:30.317000+00:00</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>310 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                 userGroup                        startTime  \\\n",
       "userHash                                                                      \n",
       "e0157f975b2aecbe652c4706db61a112  RiseCamp 2020-10-30 20:50:55.802000+00:00   \n",
       "46c3fa396f1071a92372dc40078b42f0  RiseCamp 2020-10-30 21:23:52.207000+00:00   \n",
       "a8c4ac0672f0751b58b4738ef4488877  RiseCamp 2020-10-30 21:36:27.644000+00:00   \n",
       "8ad0735d692b120e07de75cb96440fa3  RiseCamp 2020-10-30 21:40:35.290000+00:00   \n",
       "3ea0843857adef1f9fd1734eb9c0a562  RiseCamp 2020-10-30 21:44:23.298000+00:00   \n",
       "...                                    ...                              ...   \n",
       "3b6617e83c75584d167fbe0bc90211b0     Other 2021-02-20 17:17:16.629000+00:00   \n",
       "dd9a7bec7682e860a03e46de3fd87bb6     Other 2021-02-22 01:06:51.151000+00:00   \n",
       "1e42ea0b9d7d5e8a333801eb24931209     Other 2021-02-22 09:27:02.064000+00:00   \n",
       "94337423cf215d0c8800821680053cd0     Other 2021-02-22 20:57:46.136000+00:00   \n",
       "e409892210f9569708ee3b2beda05310     Other 2021-02-23 01:28:03.204000+00:00   \n",
       "\n",
       "                                                          endTime  tried_0  \\\n",
       "userHash                                                                     \n",
       "e0157f975b2aecbe652c4706db61a112 2020-11-13 03:27:19.899000+00:00        1   \n",
       "46c3fa396f1071a92372dc40078b42f0 2020-10-30 22:28:26.332000+00:00        0   \n",
       "a8c4ac0672f0751b58b4738ef4488877 2020-10-30 22:29:35.339000+00:00        0   \n",
       "8ad0735d692b120e07de75cb96440fa3 2020-11-02 18:28:19.854000+00:00        1   \n",
       "3ea0843857adef1f9fd1734eb9c0a562 2020-10-30 22:29:35.556000+00:00        0   \n",
       "...                                                           ...      ...   \n",
       "3b6617e83c75584d167fbe0bc90211b0 2021-02-20 17:18:15.736000+00:00        0   \n",
       "dd9a7bec7682e860a03e46de3fd87bb6 2021-02-22 01:20:55.006000+00:00        0   \n",
       "1e42ea0b9d7d5e8a333801eb24931209 2021-02-22 09:35:29.254000+00:00        0   \n",
       "94337423cf215d0c8800821680053cd0 2021-02-22 21:17:40.871000+00:00        0   \n",
       "e409892210f9569708ee3b2beda05310 2021-02-23 01:28:30.317000+00:00        0   \n",
       "\n",
       "                                  tried_1  tried_2  tried_3  tried_4  \n",
       "userHash                                                              \n",
       "e0157f975b2aecbe652c4706db61a112        1        1        1        1  \n",
       "46c3fa396f1071a92372dc40078b42f0        1        1        1        0  \n",
       "a8c4ac0672f0751b58b4738ef4488877        1        1        1        1  \n",
       "8ad0735d692b120e07de75cb96440fa3        1        0        1        1  \n",
       "3ea0843857adef1f9fd1734eb9c0a562        1        1        1        1  \n",
       "...                                   ...      ...      ...      ...  \n",
       "3b6617e83c75584d167fbe0bc90211b0        0        0        0        0  \n",
       "dd9a7bec7682e860a03e46de3fd87bb6        0        0        0        0  \n",
       "1e42ea0b9d7d5e8a333801eb24931209        0        0        0        0  \n",
       "94337423cf215d0c8800821680053cd0        0        0        0        0  \n",
       "e409892210f9569708ee3b2beda05310        0        0        0        0  \n",
       "\n",
       "[310 rows x 8 columns]"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Groups users by hash and computes information as to whether user attempt notebook\n",
    "grouped_df = df.groupby('userHash').agg(userGroup=('userGroup', sp.mode), startTime=('startTime', 'min'), endTime=('endTime', 'max'), \n",
    "                                        tried_0=('nbName', has_0), tried_1=('nbName', has_1), tried_2=('nbName', has_2),\n",
    "                                        tried_3=('nbName', has_3), tried_4=('nbName', has_4))\n",
    "grouped_df['userGroup'] = grouped_df['userGroup'].apply(lambda x: x[0][0])\n",
    "grouped_df.sort_values('startTime')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
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       "\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>startTime</th>\n",
       "      <th>endTime</th>\n",
       "      <th>tried_0</th>\n",
       "      <th>tried_1</th>\n",
       "      <th>tried_2</th>\n",
       "      <th>tried_3</th>\n",
       "      <th>tried_4</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>userGroup</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Other</th>\n",
       "      <td>288</td>\n",
       "      <td>288</td>\n",
       "      <td>288</td>\n",
       "      <td>288</td>\n",
       "      <td>288</td>\n",
       "      <td>288</td>\n",
       "      <td>288</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RiseCamp</th>\n",
       "      <td>11</td>\n",
       "      <td>11</td>\n",
       "      <td>11</td>\n",
       "      <td>11</td>\n",
       "      <td>11</td>\n",
       "      <td>11</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>USF</th>\n",
       "      <td>11</td>\n",
       "      <td>11</td>\n",
       "      <td>11</td>\n",
       "      <td>11</td>\n",
       "      <td>11</td>\n",
       "      <td>11</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           startTime  endTime  tried_0  tried_1  tried_2  tried_3  tried_4\n",
       "userGroup                                                                 \n",
       "Other            288      288      288      288      288      288      288\n",
       "RiseCamp          11       11       11       11       11       11       11\n",
       "USF               11       11       11       11       11       11       11"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grouped_df.groupby(\"userGroup\").count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Plots user attemps at tutorial notebooks\n",
    "def plot_nb_progression(df, userGroup, n):\n",
    "    nbNames = ['1-Specify-Intent.ipynb', '2-Quick-Vis.ipynb', '3-widget-vis-export.ipynb', '4-Data-Playground.ipynb'] \n",
    "    counts = [sum(df['tried_1']),sum(df['tried_2']),sum(df['tried_3']),sum(df['tried_4'])]\n",
    "    plt.bar(nbNames , counts)\n",
    "    plt.xticks(nbNames, rotation=45)\n",
    "    plt.title(str(userGroup) + ' Attempts of Tutorial Notebooks n=' + str(n))\n",
    "    plt.ylabel('Counts')\n",
    "    plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "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": [
    "plot_nb_progression(grouped_df, 'Overall', len(grouped_df))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "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": [
    "riseCamp_df = grouped_df[grouped_df['userGroup']=='RiseCamp']\n",
    "plot_nb_progression(riseCamp_df, 'RiseCamp', len(riseCamp_df))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "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": [
    "usf_df = grouped_df[grouped_df['userGroup']=='USF']\n",
    "plot_nb_progression(usf_df, 'USF', len(usf_df))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "# playground_df = df[df['nbName'] == '4-Data-Playground.ipynb']\n",
    "playground_df = df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[{'type': 'executeCodeCell', 'time': '2021-02-20T03:33:20.938Z', 'code': 'import pandas as pd\\nimport lux\\n# Collecting basic usage statistics for Lux (For more information, see: https://tinyurl.com/logging-consent)\\nlux.logger = True # Remove this line if you do not want your interactions recorded', 'id': 'b0695c50052d40c28816ae63ef8c77b0', 'order_count': 5}, {'type': 'completeCodeCell', 'time': '2021-02-20T03:33:28.274Z', 'code': \"df = pd.read_csv('../data/penguins.csv')\", 'id': '163895b51f364d2d8ef3cea6df058628', 'isPrintPandasDf': False, 'isPrintLuxDf': False, 'isPrintVis': False, 'isPrintVisCollection': False, 'order_count': 6}, {'type': 'selectCodeCell', 'time': '2021-02-20T03:33:31.100Z', 'code': 'df.info()', 'id': 'c324a5349916472286ad64b2a8ef4886', 'order_count': 8}, {'type': 'executeCodeCell', 'time': '2021-02-20T03:33:31.552Z', 'code': 'df.info()', 'id': 'c324a5349916472286ad64b2a8ef4886', 'order_count': 8}, {'type': 'completeCodeCell', 'time': '2021-02-20T03:33:31.840Z', 'code': 'df.info()', 'id': 'c324a5349916472286ad64b2a8ef4886', 'isPrintPandasDf': False, 'isPrintLuxDf': False, 'isPrintVis': False, 'isPrintVisCollection': False, 'order_count': 8}, {'type': 'selectMarkdownCell', 'time': '2021-02-20T03:33:37.818Z', 'code': \"Lux's nice thing is that it can be used as it is with the pandas dataframe and doesn’t require any modifications to the existing syntax. For instance, if you drop any column or row, the recommendations are regenerated based on the updated dataframe. All the nice functionalities that we get from pandas like dropping columns, importing CSVs, etc., are also preserved. Let’s get an overview of the data set.\", 'id': '21a283a397c24bfd84c9a6fe08abafe7', 'order_count': 7}, {'type': 'completeCodeCell', 'time': '2021-02-20T03:33:24.523Z', 'code': 'import pandas as pd\\nimport lux\\n# Collecting basic usage statistics for Lux (For more information, see: https://tinyurl.com/logging-consent)\\nlux.logger = True # Remove this line if you do not want your interactions recorded', 'id': 'b0695c50052d40c28816ae63ef8c77b0', 'isPrintPandasDf': False, 'isPrintLuxDf': False, 'isPrintVis': False, 'isPrintVisCollection': False, 'order_count': 5}, {'type': 'selectCodeCell', 'time': '2021-02-20T03:33:26.935Z', 'code': \"df = pd.read_csv('../data/penguins.csv')\", 'id': '163895b51f364d2d8ef3cea6df058628', 'order_count': 6}, {'type': 'executeCodeCell', 'time': '2021-02-20T03:33:27.984Z', 'code': \"df = pd.read_csv('../data/penguins.csv')\", 'id': '163895b51f364d2d8ef3cea6df058628', 'order_count': 6}, {'type': 'selectCodeCell', 'time': '2021-02-20T03:35:37.091Z', 'code': 'df.info()', 'id': 'c324a5349916472286ad64b2a8ef4886', 'order_count': 8}, {'type': 'selectCodeCell', 'time': '2021-02-20T03:35:38.268Z', 'code': 'df = df.dropna()', 'id': 'e337babcaf1d4c66a3598a663bdcb137', 'order_count': 10}, {'type': 'executeCodeCell', 'time': '2021-02-20T03:35:39.264Z', 'code': 'df = df.dropna()', 'id': 'e337babcaf1d4c66a3598a663bdcb137', 'order_count': 10}, {'type': 'completeCodeCell', 'time': '2021-02-20T03:35:39.531Z', 'code': 'df = df.dropna()', 'id': 'e337babcaf1d4c66a3598a663bdcb137', 'isPrintPandasDf': False, 'isPrintLuxDf': False, 'isPrintVis': False, 'isPrintVisCollection': False, 'order_count': 10}, {'type': 'selectCodeCell', 'time': '2021-02-20T03:35:40.539Z', 'code': 'df', 'id': 'dab18896139140b097a1222be7253274', 'order_count': 13}, {'type': 'executeCodeCell', 'time': '2021-02-20T03:35:41.160Z', 'code': 'df', 'id': 'dab18896139140b097a1222be7253274', 'order_count': 13}, {'type': 'completeCodeCell', 'time': '2021-02-20T03:35:41.558Z', 'code': 'df', 'id': 'dab18896139140b097a1222be7253274', 'isPrintPandasDf': True, 'isPrintLuxDf': False, 'isPrintVis': False, 'isPrintVisCollection': False, 'order_count': 13}, {'type': 'editCodeCell', 'time': '2021-02-20T03:35:43.528Z', 'code': 'df', 'id': 'dab18896139140b097a1222be7253274', 'order_count': 13}, {'type': 'selectMarkdownCell', 'time': '2021-02-20T03:35:45.129Z', 'code': 'The recommendations in lux are organized by three different tabs, which represent potential next steps that users can take in their exploration. From the visualisations we infer that there are three different species of penguins — Adelie, Chinstrap, and Gentoo. There are also three different islands — Torgersen, Biscoe, and Dream; and both male and female species have been included in the dataset.', 'id': 'cbe40b9caf664e9c8bdfd599e239e767', 'order_count': 14}, {'type': 'selectMarkdownCell', 'time': '2021-02-20T03:35:47.026Z', 'code': 'The recommendations in lux are organized by three different tabs, which represent potential next steps that users can take in their exploration. From the visualisations we infer that there are three different species of penguins — Adelie, Chinstrap, and Gentoo. There are also three different islands — Torgersen, Biscoe, and Dream; and both male and female species have been included in the dataset.', 'id': 'cbe40b9caf664e9c8bdfd599e239e767', 'order_count': 14}, {'type': 'selectMarkdownCell', 'time': '2021-02-20T03:35:47.521Z', 'code': 'The recommendations in lux are organized by three different tabs, which represent potential next steps that users can take in their exploration. From the visualisations we infer that there are three different species of penguins — Adelie, Chinstrap, and Gentoo. There are also three different islands — Torgersen, Biscoe, and Dream; and both male and female species have been included in the dataset.', 'id': 'cbe40b9caf664e9c8bdfd599e239e767', 'order_count': 14}, {'type': 'selectMarkdownCell', 'time': '2021-02-20T03:35:49.209Z', 'code': 'The recommendations in lux are organized by three different tabs, which represent potential next steps that users can take in their exploration. From the visualisations we infer that there are three different species of penguins — Adelie, Chinstrap, and Gentoo. There are also three different islands — Torgersen, Biscoe, and Dream; and both male and female species have been included in the dataset.', 'id': 'cbe40b9caf664e9c8bdfd599e239e767', 'order_count': 14}, {'type': 'selectMarkdownCell', 'time': '2021-02-20T03:35:54.073Z', 'code': 'The recommendations in lux are organized by three different tabs, which represent potential next steps that users can take in their exploration. From the visualisations we infer that there are three different species of penguins — Adelie, Chinstrap, and Gentoo. There are also three different islands — Torgersen, Biscoe, and Dream; and both male and female species have been included in the dataset.', 'id': 'cbe40b9caf664e9c8bdfd599e239e767', 'order_count': 14}, {'time': '2021-02-20T03:35:57.961Z', 'code': 'The recommendations in lux are organized by three different tabs, which represent potential next steps that users can take in their exploration. From the visualisations we infer that there are three different species of penguins — Adelie, Chinstrap, and Gentoo. There are also three different islands — Torgersen, Biscoe, and Dream; and both male and female species have been included in the dataset.', 'id': '661198b1d3ee4b2f80bfeed17e454200', 'order_count': 14}, {'type': 'selectMarkdownCell', 'time': '2021-02-20T03:35:58.697Z', 'code': \"## Intent-based recommendations\\n\\nBeyond the basic recommendations, we can also specify our analysis intent. Let's say that we want to find out how the culmen length varies with the species. We can set the intent here as `[‘culmen_length_mm’,’species’].`When we print out the data frame again, we can see that the recommendations are steered to what is relevant to the intent that we’ve specified.\", 'id': 'b86d3334a58a4214bf702bb03c6745ea', 'order_count': 15}, {'time': '2021-02-20T03:35:59.489Z', 'code': 'The recommendations in lux are organized by three different tabs, which represent potential next steps that users can take in their exploration. From the visualisations we infer that there are three different species of penguins — Adelie, Chinstrap, and Gentoo. There are also three different islands — Torgersen, Biscoe, and Dream; and both male and female species have been included in the dataset.', 'id': '661198b1d3ee4b2f80bfeed17e454200', 'order_count': 14}, {'type': 'selectCodeCell', 'time': '2021-02-20T03:36:13.428Z', 'code': \"df.intent = ['culmen_length_mm','species']\\ndf\", 'id': 'f1d1a0b844764e0984cb0352c461c312', 'order_count': 16}, {'type': 'executeCodeCell', 'time': '2021-02-20T03:36:14.480Z', 'code': \"df.intent = ['culmen_length_mm','species']\\ndf\", 'id': 'f1d1a0b844764e0984cb0352c461c312', 'order_count': 16}, {'type': 'completeCodeCell', 'time': '2021-02-20T03:36:17.462Z', 'code': \"df.intent = ['culmen_length_mm','species']\\ndf\", 'id': 'f1d1a0b844764e0984cb0352c461c312', 'isPrintPandasDf': True, 'isPrintLuxDf': False, 'isPrintVis': False, 'isPrintVisCollection': False, 'order_count': 16}, {'type': 'selectMarkdownCell', 'time': '2021-02-20T03:36:19.825Z', 'code': 'On the left-hand side in the image below, what we see is `Current Visualization` corresponding to the attributes that we have selected. On the right-hand side, we have `Enhance` i.e. what happens when we add an attribute to the current selection. We also have the `Filter` tab which adds filter while fixing the selected variable.\\n\\nIf you closely look at the correlations within species, culmen length and depth are positively correlated. This is a classic example of [**Simpson’s paradox**](https://en.wikipedia.org/wiki/Simpson%27s_paradox).\\n\\n![](https://miro.medium.com/max/254/1*bN1pTPMGUB8g7EpQurQbsQ.png)\\n\\nFinally, you can get a pretty clear separation between all three species by looking at flipper length versus culmen length.\\n\\n\\n\\n![Image for post](https://miro.medium.com/max/258/1*1VeJ6DCycXM67Eg6l41vdg.png)\\n', 'id': 'f149d2c8e93d49a9b68002bddc73d3a0', 'order_count': 17}, {'type': 'selectMarkdownCell', 'time': '2021-02-20T03:36:21.369Z', 'code': 'On the left-hand side in the image below, what we see is `Current Visualization` corresponding to the attributes that we have selected. On the right-hand side, we have `Enhance` i.e. what happens when we add an attribute to the current selection. We also have the `Filter` tab which adds filter while fixing the selected variable.\\n\\nIf you closely look at the correlations within species, culmen length and depth are positively correlated. This is a classic example of [**Simpson’s paradox**](https://en.wikipedia.org/wiki/Simpson%27s_paradox).\\n\\n![](https://miro.medium.com/max/254/1*bN1pTPMGUB8g7EpQurQbsQ.png)\\n\\nFinally, you can get a pretty clear separation between all three species by looking at flipper length versus culmen length.\\n\\n\\n\\n![Image for post](https://miro.medium.com/max/258/1*1VeJ6DCycXM67Eg6l41vdg.png)\\n', 'id': 'f149d2c8e93d49a9b68002bddc73d3a0', 'order_count': 17}, {'type': 'selectCodeCell', 'time': '2021-02-20T03:36:23.149Z', 'code': \"df.intent = ['culmen_length_mm','species']\\ndf\", 'id': 'f1d1a0b844764e0984cb0352c461c312', 'order_count': 16}, {'type': 'selectCodeCell', 'time': '2021-02-20T03:36:48.716Z', 'code': \"df.save_as_html('file.html')\", 'id': 'f1f7bf598db645438656a248f17d1d7a', 'order_count': 19}, {'type': 'selectCodeCell', 'time': '2021-02-20T03:36:49.427Z', 'code': 'df.recommendation', 'id': 'c99fbeba6f824cf7949af7ca948bcbd0', 'order_count': 21}, {'type': 'selectMarkdownCell', 'time': '2021-02-20T03:45:49.461Z', 'code': 'The recommendations in lux are organized by three different tabs, which represent potential next steps that users can take in their exploration. From the visualisations we infer that there are three different species of penguins — Adelie, Chinstrap, and Gentoo. There are also three different islands — Torgersen, Biscoe, and Dream; and both male and female species have been included in the dataset.', 'id': '4ef99ba7458f444e9f97e068ae813fcf', 'order_count': 14}, {'type': 'editMarkdownCell', 'time': '2021-02-20T03:45:49.466Z', 'code': 'The recommendations in lux are organized by three different tabs, which represent potential next steps that users can take in their exploration. From the visualisations we infer that there are three different species of penguins — Adelie, Chinstrap, and Gentoo. There are also three different islands — Torgersen, Biscoe, and Dream; and both male and female species have been included in the dataset.', 'id': '4ef99ba7458f444e9f97e068ae813fcf', 'order_count': 14}, {'type': 'selectMarkdownCell', 'time': '2021-02-20T03:45:56.288Z', 'code': \"## Intent-based recommendations\\n\\nBeyond the basic recommendations, we can also specify our analysis intent. Let's say that we want to find out how the culmen length varies with the species. We can set the intent here as `[‘culmen_length_mm’,’species’].`When we print out the data frame again, we can see that the recommendations are steered to what is relevant to the intent that we’ve specified.\", 'id': 'b86d3334a58a4214bf702bb03c6745ea', 'order_count': 15}, {'type': 'kernelKilled', 'time': '2021-02-20T03:46:28.216Z'}, {'type': 'selectMarkdownCell', 'time': '2021-02-20T03:49:07.944Z', 'code': \"## Intent-based recommendations\\n\\nBeyond the basic recommendations, we can also specify our analysis intent. Let's say that we want to find out how the culmen length varies with the species. We can set the intent here as `[‘culmen_length_mm’,’species’].`When we print out the data frame again, we can see that the recommendations are steered to what is relevant to the intent that we’ve specified.\", 'id': 'b86d3334a58a4214bf702bb03c6745ea', 'order_count': 15}, {'type': 'selectMarkdownCell', 'time': '2021-02-20T03:49:08.241Z', 'code': \"## Intent-based recommendations\\n\\nBeyond the basic recommendations, we can also specify our analysis intent. Let's say that we want to find out how the culmen length varies with the species. We can set the intent here as `[‘culmen_length_mm’,’species’].`When we print out the data frame again, we can see that the recommendations are steered to what is relevant to the intent that we’ve specified.\", 'id': 'b86d3334a58a4214bf702bb03c6745ea', 'order_count': 15}, {'type': 'selectMarkdownCell', 'time': '2021-02-20T03:49:11.639Z', 'code': \"## Intent-based recommendations\\n\\nBeyond the basic recommendations, we can also specify our analysis intent. Let's say that we want to find out how the culmen length varies with the species. We can set the intent here as `[‘culmen_length_mm’,’species’].`When we print out the data frame again, we can see that the recommendations are steered to what is relevant to the intent that we’ve specified.\", 'id': 'b86d3334a58a4214bf702bb03c6745ea', 'order_count': 15}, {'type': 'selectMarkdownCell', 'time': '2021-02-20T03:49:14.136Z', 'code': \"## Intent-based recommendations\\n\\nBeyond the basic recommendations, we can also specify our analysis intent. Let's say that we want to find out how the culmen length varies with the species. We can set the intent here as `[‘culmen_length_mm’,’species’].`When we print out the data frame again, we can see that the recommendations are steered to what is relevant to the intent that we’ve specified.\", 'id': 'b86d3334a58a4214bf702bb03c6745ea', 'order_count': 15}, {'type': 'selectMarkdownCell', 'time': '2021-02-20T03:49:15.752Z', 'code': \"## Intent-based recommendations\\n\\nBeyond the basic recommendations, we can also specify our analysis intent. Let's say that we want to find out how the culmen length varies with the species. We can set the intent here as `[‘culmen_length_mm’,’species’].`When we print out the data frame again, we can see that the recommendations are steered to what is relevant to the intent that we’ve specified.\", 'id': 'b86d3334a58a4214bf702bb03c6745ea', 'order_count': 15}, {'type': 'selectCodeCell', 'time': '2021-02-20T03:53:47.810Z', 'code': \"df.intent = ['culmen_length_mm','species']\\ndf\", 'id': 'f1d1a0b844764e0984cb0352c461c312', 'order_count': 16}, {'type': 'editCodeCell', 'time': '2021-02-20T03:53:48.770Z', 'code': \"df.intent = ['culmen_length_mm','species']\\ndf\", 'id': 'f1d1a0b844764e0984cb0352c461c312', 'order_count': 16}, {'type': 'editCodeCell', 'time': '2021-02-20T03:57:02.038Z', 'code': \"df.intent = ['culmen_length_mm','species']\\ndf\", 'id': 'f1d1a0b844764e0984cb0352c461c312', 'order_count': 16}, {'type': 'selectMarkdownCell', 'time': '2021-02-20T03:57:02.094Z', 'code': 'On the left-hand side in the image below, what we see is `Current Visualization` corresponding to the attributes that we have selected. On the right-hand side, we have `Enhance` i.e. what happens when we add an attribute to the current selection. We also have the `Filter` tab which adds filter while fixing the selected variable.\\n\\nIf you closely look at the correlations within species, culmen length and depth are positively correlated. This is a classic example of [**Simpson’s paradox**](https://en.wikipedia.org/wiki/Simpson%27s_paradox).\\n\\n![](https://miro.medium.com/max/254/1*bN1pTPMGUB8g7EpQurQbsQ.png)\\n\\nFinally, you can get a pretty clear separation between all three species by looking at flipper length versus culmen length.\\n\\n\\n\\n![Image for post](https://miro.medium.com/max/258/1*1VeJ6DCycXM67Eg6l41vdg.png)\\n', 'id': 'f149d2c8e93d49a9b68002bddc73d3a0', 'order_count': 17}, {'type': 'selectMarkdownCell', 'time': '2021-02-20T03:57:02.591Z', 'code': 'On the left-hand side in the image below, what we see is `Current Visualization` corresponding to the attributes that we have selected. On the right-hand side, we have `Enhance` i.e. what happens when we add an attribute to the current selection. We also have the `Filter` tab which adds filter while fixing the selected variable.\\n\\nIf you closely look at the correlations within species, culmen length and depth are positively correlated. This is a classic example of [**Simpson’s paradox**](https://en.wikipedia.org/wiki/Simpson%27s_paradox).\\n\\n![](https://miro.medium.com/max/254/1*bN1pTPMGUB8g7EpQurQbsQ.png)\\n\\nFinally, you can get a pretty clear separation between all three species by looking at flipper length versus culmen length.\\n\\n\\n\\n![Image for post](https://miro.medium.com/max/258/1*1VeJ6DCycXM67Eg6l41vdg.png)\\n', 'id': 'f149d2c8e93d49a9b68002bddc73d3a0', 'order_count': 17}, {'type': 'selectMarkdownCell', 'time': '2021-02-20T03:57:02.767Z', 'code': 'On the left-hand side in the image below, what we see is `Current Visualization` corresponding to the attributes that we have selected. On the right-hand side, we have `Enhance` i.e. what happens when we add an attribute to the current selection. We also have the `Filter` tab which adds filter while fixing the selected variable.\\n\\nIf you closely look at the correlations within species, culmen length and depth are positively correlated. This is a classic example of [**Simpson’s paradox**](https://en.wikipedia.org/wiki/Simpson%27s_paradox).\\n\\n![](https://miro.medium.com/max/254/1*bN1pTPMGUB8g7EpQurQbsQ.png)\\n\\nFinally, you can get a pretty clear separation between all three species by looking at flipper length versus culmen length.\\n\\n\\n\\n![Image for post](https://miro.medium.com/max/258/1*1VeJ6DCycXM67Eg6l41vdg.png)\\n', 'id': 'f149d2c8e93d49a9b68002bddc73d3a0', 'order_count': 17}, {'type': 'selectMarkdownCell', 'time': '2021-02-20T03:57:03.463Z', 'code': 'On the left-hand side in the image below, what we see is `Current Visualization` corresponding to the attributes that we have selected. On the right-hand side, we have `Enhance` i.e. what happens when we add an attribute to the current selection. We also have the `Filter` tab which adds filter while fixing the selected variable.\\n\\nIf you closely look at the correlations within species, culmen length and depth are positively correlated. This is a classic example of [**Simpson’s paradox**](https://en.wikipedia.org/wiki/Simpson%27s_paradox).\\n\\n![](https://miro.medium.com/max/254/1*bN1pTPMGUB8g7EpQurQbsQ.png)\\n\\nFinally, you can get a pretty clear separation between all three species by looking at flipper length versus culmen length.\\n\\n\\n\\n![Image for post](https://miro.medium.com/max/258/1*1VeJ6DCycXM67Eg6l41vdg.png)\\n', 'id': 'f149d2c8e93d49a9b68002bddc73d3a0', 'order_count': 17}, {'type': 'selectMarkdownCell', 'time': '2021-02-20T03:57:03.686Z', 'code': 'On the left-hand side in the image below, what we see is `Current Visualization` corresponding to the attributes that we have selected. On the right-hand side, we have `Enhance` i.e. what happens when we add an attribute to the current selection. We also have the `Filter` tab which adds filter while fixing the selected variable.\\n\\nIf you closely look at the correlations within species, culmen length and depth are positively correlated. This is a classic example of [**Simpson’s paradox**](https://en.wikipedia.org/wiki/Simpson%27s_paradox).\\n\\n![](https://miro.medium.com/max/254/1*bN1pTPMGUB8g7EpQurQbsQ.png)\\n\\nFinally, you can get a pretty clear separation between all three species by looking at flipper length versus culmen length.\\n\\n\\n\\n![Image for post](https://miro.medium.com/max/258/1*1VeJ6DCycXM67Eg6l41vdg.png)\\n', 'id': 'f149d2c8e93d49a9b68002bddc73d3a0', 'order_count': 17}, {'type': 'selectMarkdownCell', 'time': '2021-02-20T03:57:14.574Z', 'code': 'On the left-hand side in the image below, what we see is `Current Visualization` corresponding to the attributes that we have selected. On the right-hand side, we have `Enhance` i.e. what happens when we add an attribute to the current selection. We also have the `Filter` tab which adds filter while fixing the selected variable.\\n\\nIf you closely look at the correlations within species, culmen length and depth are positively correlated. This is a classic example of [**Simpson’s paradox**](https://en.wikipedia.org/wiki/Simpson%27s_paradox).\\n\\n![](https://miro.medium.com/max/254/1*bN1pTPMGUB8g7EpQurQbsQ.png)\\n\\nFinally, you can get a pretty clear separation between all three species by looking at flipper length versus culmen length.\\n\\n\\n\\n![Image for post](https://miro.medium.com/max/258/1*1VeJ6DCycXM67Eg6l41vdg.png)\\n', 'id': 'f149d2c8e93d49a9b68002bddc73d3a0', 'order_count': 17}, {'type': 'selectMarkdownCell', 'time': '2021-02-20T03:57:17.046Z', 'code': 'On the left-hand side in the image below, what we see is `Current Visualization` corresponding to the attributes that we have selected. On the right-hand side, we have `Enhance` i.e. what happens when we add an attribute to the current selection. We also have the `Filter` tab which adds filter while fixing the selected variable.\\n\\nIf you closely look at the correlations within species, culmen length and depth are positively correlated. This is a classic example of [**Simpson’s paradox**](https://en.wikipedia.org/wiki/Simpson%27s_paradox).\\n\\n![](https://miro.medium.com/max/254/1*bN1pTPMGUB8g7EpQurQbsQ.png)\\n\\nFinally, you can get a pretty clear separation between all three species by looking at flipper length versus culmen length.\\n\\n\\n\\n![Image for post](https://miro.medium.com/max/258/1*1VeJ6DCycXM67Eg6l41vdg.png)\\n', 'id': 'f149d2c8e93d49a9b68002bddc73d3a0', 'order_count': 17}, {'type': 'selectMarkdownCell', 'time': '2021-02-20T03:57:27.878Z', 'code': 'On the left-hand side in the image below, what we see is `Current Visualization` corresponding to the attributes that we have selected. On the right-hand side, we have `Enhance` i.e. what happens when we add an attribute to the current selection. We also have the `Filter` tab which adds filter while fixing the selected variable.\\n\\nIf you closely look at the correlations within species, culmen length and depth are positively correlated. This is a classic example of [**Simpson’s paradox**](https://en.wikipedia.org/wiki/Simpson%27s_paradox).\\n\\n![](https://miro.medium.com/max/254/1*bN1pTPMGUB8g7EpQurQbsQ.png)\\n\\nFinally, you can get a pretty clear separation between all three species by looking at flipper length versus culmen length.\\n\\n\\n\\n![Image for post](https://miro.medium.com/max/258/1*1VeJ6DCycXM67Eg6l41vdg.png)\\n', 'id': 'f149d2c8e93d49a9b68002bddc73d3a0', 'order_count': 17}, {'type': 'selectCodeCell', 'time': '2021-02-20T03:57:31.097Z', 'code': \"df.save_as_html('file.html')\", 'id': 'f1f7bf598db645438656a248f17d1d7a', 'order_count': 19}, {'type': 'selectMarkdownCell', 'time': '2021-02-20T03:57:33.335Z', 'code': '## Exporting visualizations from Widget\\n\\nLux also makes it pretty easy to export and share the generated visualizations. The visualizations can be exported into a static HTML as follows:\\n', 'id': '80c829c603d94582861d20af3320a953', 'order_count': 18}, {'type': 'selectCodeCell', 'time': '2021-02-20T06:08:43.368Z', 'code': 'df.recommendation', 'id': 'c99fbeba6f824cf7949af7ca948bcbd0', 'order_count': 21}, {'type': 'editCodeCell', 'time': '2021-02-20T06:08:43.374Z', 'code': 'df.recommendation', 'id': 'c99fbeba6f824cf7949af7ca948bcbd0', 'order_count': 21}, {'type': 'selectCodeCell', 'time': '2021-02-20T06:08:43.432Z', 'code': 'df.recommendation', 'id': 'c99fbeba6f824cf7949af7ca948bcbd0', 'order_count': 21}, {'type': 'selectCodeCell', 'time': '2021-02-20T06:08:43.599Z', 'code': 'df.recommendation', 'id': 'c99fbeba6f824cf7949af7ca948bcbd0', 'order_count': 21}, {'type': 'selectCodeCell', 'time': '2021-02-20T06:08:44.518Z', 'code': 'df.recommendation', 'id': 'c99fbeba6f824cf7949af7ca948bcbd0', 'order_count': 21}, {'type': 'selectCodeCell', 'time': '2021-02-20T06:08:45.542Z', 'code': 'df.recommendation', 'id': 'c99fbeba6f824cf7949af7ca948bcbd0', 'order_count': 21}, {'type': 'selectCodeCell', 'time': '2021-02-20T06:08:47.686Z', 'code': 'df.recommendation', 'id': 'c99fbeba6f824cf7949af7ca948bcbd0', 'order_count': 21}, {'type': 'selectCodeCell', 'time': '2021-02-20T06:08:50.142Z', 'code': 'df.recommendation', 'id': 'c99fbeba6f824cf7949af7ca948bcbd0', 'order_count': 21}, {'type': 'selectCodeCell', 'time': '2021-02-20T06:08:52.997Z', 'code': 'df.recommendation', 'id': 'c99fbeba6f824cf7949af7ca948bcbd0', 'order_count': 21}, {'type': 'selectCodeCell', 'time': '2021-02-20T06:08:54.102Z', 'code': 'df.recommendation', 'id': 'c99fbeba6f824cf7949af7ca948bcbd0', 'order_count': 21}, {'type': 'editCodeCell', 'time': '2021-02-20T06:09:11.797Z', 'code': 'df.recommendation', 'id': 'c99fbeba6f824cf7949af7ca948bcbd0', 'order_count': 21}, {'type': 'selectMarkdownCell', 'time': '2021-02-20T06:09:12.584Z', 'code': '## Exporting visualizations from Widget\\n\\nLux also makes it pretty easy to export and share the generated visualizations. The visualizations can be exported into a static HTML as follows:\\n', 'id': '80c829c603d94582861d20af3320a953', 'order_count': 18}, {'type': 'selectMarkdownCell', 'time': '2021-02-20T06:09:15.387Z', 'code': '## Exporting visualizations from Widget\\n\\nLux also makes it pretty easy to export and share the generated visualizations. The visualizations can be exported into a static HTML as follows:\\n', 'id': '80c829c603d94582861d20af3320a953', 'order_count': 18}, {'type': 'selectMarkdownCell', 'time': '2021-02-20T06:09:18.267Z', 'code': '## Exporting visualizations from Widget\\n\\nLux also makes it pretty easy to export and share the generated visualizations. The visualizations can be exported into a static HTML as follows:\\n', 'id': '80c829c603d94582861d20af3320a953', 'order_count': 18}, {'type': 'selectMarkdownCell', 'time': '2021-02-20T06:09:22.242Z', 'code': 'We can also access the set of [recommendations generated for the](https://lux-api.readthedocs.io/en/latest/source/guide/export.html) data frames via the properties recommendation. The output is a dictionary, keyed by the name of the recommendation category.', 'id': 'dc0924bf916e48aa8ea16194fb05184a', 'order_count': 20}, {'type': 'selectMarkdownCell', 'time': '2021-02-20T06:09:23.468Z', 'code': 'We can also access the set of [recommendations generated for the](https://lux-api.readthedocs.io/en/latest/source/guide/export.html) data frames via the properties recommendation. The output is a dictionary, keyed by the name of the recommendation category.', 'id': 'dc0924bf916e48aa8ea16194fb05184a', 'order_count': 20}, {'type': 'selectMarkdownCell', 'time': '2021-02-20T06:09:24.947Z', 'code': 'We can also access the set of [recommendations generated for the](https://lux-api.readthedocs.io/en/latest/source/guide/export.html) data frames via the properties recommendation. The output is a dictionary, keyed by the name of the recommendation category.', 'id': 'dc0924bf916e48aa8ea16194fb05184a', 'order_count': 20}, {'type': 'selectCodeCell', 'time': '2021-02-20T06:09:30.574Z', 'code': 'df.recommendation', 'id': 'c99fbeba6f824cf7949af7ca948bcbd0', 'order_count': 21}, {'type': 'selectCodeCell', 'time': '2021-02-20T06:09:33.190Z', 'code': 'df.recommendation', 'id': 'c99fbeba6f824cf7949af7ca948bcbd0', 'order_count': 21}, {'type': 'executeCodeCell', 'time': '2021-02-20T06:09:34.133Z', 'code': 'df.recommendation', 'id': 'c99fbeba6f824cf7949af7ca948bcbd0', 'order_count': 21}, {'type': 'kernelRestart', 'time': '2021-02-20T06:09:37.362Z'}, {'type': 'selectCodeCell', 'time': '2021-02-20T06:23:53.710Z', 'code': \"df.save_as_html('file.html')\", 'id': 'f1f7bf598db645438656a248f17d1d7a', 'order_count': 19}, {'type': 'editCodeCell', 'time': '2021-02-20T06:23:53.715Z', 'code': \"df.save_as_html('file.html')\", 'id': 'f1f7bf598db645438656a248f17d1d7a', 'order_count': 19}, {'type': 'selectCodeCell', 'time': '2021-02-20T06:23:53.991Z', 'code': \"df.save_as_html('file.html')\", 'id': 'f1f7bf598db645438656a248f17d1d7a', 'order_count': 19}, {'type': 'selectCodeCell', 'time': '2021-02-20T06:23:54.444Z', 'code': \"df.save_as_html('file.html')\", 'id': 'f1f7bf598db645438656a248f17d1d7a', 'order_count': 19}, {'type': 'selectCodeCell', 'time': '2021-02-20T06:23:54.637Z', 'code': \"df.save_as_html('file.html')\", 'id': 'f1f7bf598db645438656a248f17d1d7a', 'order_count': 19}, {'type': 'editCodeCell', 'time': '2021-02-20T06:26:14.969Z', 'code': \"df.save_as_html('file.html')\", 'id': 'f1f7bf598db645438656a248f17d1d7a', 'order_count': 19}, {'type': 'selectMarkdownCell', 'time': '2021-02-20T06:26:23.969Z', 'code': '## Exporting Visualizations as Code\\n\\nNot only can we export visualization as HTML but also as code. The visualizations can then be exported to code in  [Altair](https://altair-viz.github.io/)  for further edits or as  [Vega-Lite](https://vega.github.io/vega-lite/)  specification. More details can be found in the  [documentation](https://lux-api.readthedocs.io/en/latest/source/guide/export.html).', 'id': 'db6a1298b3a04e93b24b0d2b83229dbc', 'order_count': 22}, {'type': 'selectMarkdownCell', 'time': '2021-02-20T06:26:26.466Z', 'code': '## Exporting Visualizations as Code\\n\\nNot only can we export visualization as HTML but also as code. The visualizations can then be exported to code in  [Altair](https://altair-viz.github.io/)  for further edits or as  [Vega-Lite](https://vega.github.io/vega-lite/)  specification. More details can be found in the  [documentation](https://lux-api.readthedocs.io/en/latest/source/guide/export.html).', 'id': 'db6a1298b3a04e93b24b0d2b83229dbc', 'order_count': 22}, {'type': 'selectCodeCell', 'time': '2021-02-20T06:31:01.907Z', 'code': 'df.recommendation', 'id': 'c99fbeba6f824cf7949af7ca948bcbd0', 'order_count': 21}, {'type': 'editCodeCell', 'time': '2021-02-20T06:31:01.915Z', 'code': 'df.recommendation', 'id': 'c99fbeba6f824cf7949af7ca948bcbd0', 'order_count': 21}, {'type': 'selectCodeCell', 'time': '2021-02-20T06:31:02.157Z', 'code': 'df.recommendation', 'id': 'c99fbeba6f824cf7949af7ca948bcbd0', 'order_count': 21}, {'type': 'selectCodeCell', 'time': '2021-02-20T06:31:02.877Z', 'code': 'df.recommendation', 'id': 'c99fbeba6f824cf7949af7ca948bcbd0', 'order_count': 21}, {'type': 'editCodeCell', 'time': '2021-02-20T06:32:47.605Z', 'code': 'df.recommendation', 'id': 'c99fbeba6f824cf7949af7ca948bcbd0', 'order_count': 21}]\n",
      "[{'type': 'executeCodeCell', 'time': '2021-02-20T03:33:20.938Z', 'code': 'import pandas as pd\\nimport lux\\n# Collecting basic usage statistics for Lux (For more information, see: https://tinyurl.com/logging-consent)\\nlux.logger = True # Remove this line if you do not want your interactions recorded', 'id': 'b0695c50052d40c28816ae63ef8c77b0', 'order_count': 5}, {'type': 'completeCodeCell', 'time': '2021-02-20T03:33:28.274Z', 'code': \"df = pd.read_csv('../data/penguins.csv')\", 'id': '163895b51f364d2d8ef3cea6df058628', 'isPrintPandasDf': False, 'isPrintLuxDf': False, 'isPrintVis': False, 'isPrintVisCollection': False, 'order_count': 6}, {'type': 'selectCodeCell', 'time': '2021-02-20T03:33:31.100Z', 'code': 'df.info()', 'id': 'c324a5349916472286ad64b2a8ef4886', 'order_count': 8}, {'type': 'executeCodeCell', 'time': '2021-02-20T03:33:31.552Z', 'code': 'df.info()', 'id': 'c324a5349916472286ad64b2a8ef4886', 'order_count': 8}, {'type': 'completeCodeCell', 'time': '2021-02-20T03:33:31.840Z', 'code': 'df.info()', 'id': 'c324a5349916472286ad64b2a8ef4886', 'isPrintPandasDf': False, 'isPrintLuxDf': False, 'isPrintVis': False, 'isPrintVisCollection': False, 'order_count': 8}, {'type': 'selectMarkdownCell', 'time': '2021-02-20T03:33:37.818Z', 'code': \"Lux's nice thing is that it can be used as it is with the pandas dataframe and doesn’t require any modifications to the existing syntax. For instance, if you drop any column or row, the recommendations are regenerated based on the updated dataframe. All the nice functionalities that we get from pandas like dropping columns, importing CSVs, etc., are also preserved. Let’s get an overview of the data set.\", 'id': '21a283a397c24bfd84c9a6fe08abafe7', 'order_count': 7}, {'type': 'completeCodeCell', 'time': '2021-02-20T03:33:24.523Z', 'code': 'import pandas as pd\\nimport lux\\n# Collecting basic usage statistics for Lux (For more information, see: https://tinyurl.com/logging-consent)\\nlux.logger = True # Remove this line if you do not want your interactions recorded', 'id': 'b0695c50052d40c28816ae63ef8c77b0', 'isPrintPandasDf': False, 'isPrintLuxDf': False, 'isPrintVis': False, 'isPrintVisCollection': False, 'order_count': 5}, {'type': 'selectCodeCell', 'time': '2021-02-20T03:33:26.935Z', 'code': \"df = pd.read_csv('../data/penguins.csv')\", 'id': '163895b51f364d2d8ef3cea6df058628', 'order_count': 6}, {'type': 'executeCodeCell', 'time': '2021-02-20T03:33:27.984Z', 'code': \"df = pd.read_csv('../data/penguins.csv')\", 'id': '163895b51f364d2d8ef3cea6df058628', 'order_count': 6}, {'type': 'selectCodeCell', 'time': '2021-02-20T03:35:37.091Z', 'code': 'df.info()', 'id': 'c324a5349916472286ad64b2a8ef4886', 'order_count': 8}, {'type': 'selectCodeCell', 'time': '2021-02-20T03:35:38.268Z', 'code': 'df = df.dropna()', 'id': 'e337babcaf1d4c66a3598a663bdcb137', 'order_count': 10}, {'type': 'executeCodeCell', 'time': '2021-02-20T03:35:39.264Z', 'code': 'df = df.dropna()', 'id': 'e337babcaf1d4c66a3598a663bdcb137', 'order_count': 10}, {'type': 'completeCodeCell', 'time': '2021-02-20T03:35:39.531Z', 'code': 'df = df.dropna()', 'id': 'e337babcaf1d4c66a3598a663bdcb137', 'isPrintPandasDf': False, 'isPrintLuxDf': False, 'isPrintVis': False, 'isPrintVisCollection': False, 'order_count': 10}, {'type': 'selectCodeCell', 'time': '2021-02-20T03:35:40.539Z', 'code': 'df', 'id': 'dab18896139140b097a1222be7253274', 'order_count': 13}, {'type': 'executeCodeCell', 'time': '2021-02-20T03:35:41.160Z', 'code': 'df', 'id': 'dab18896139140b097a1222be7253274', 'order_count': 13}, {'type': 'completeCodeCell', 'time': '2021-02-20T03:35:41.558Z', 'code': 'df', 'id': 'dab18896139140b097a1222be7253274', 'isPrintPandasDf': True, 'isPrintLuxDf': False, 'isPrintVis': False, 'isPrintVisCollection': False, 'order_count': 13}, {'type': 'editCodeCell', 'time': '2021-02-20T03:35:43.528Z', 'code': 'df', 'id': 'dab18896139140b097a1222be7253274', 'order_count': 13}, {'type': 'selectMarkdownCell', 'time': '2021-02-20T03:35:45.129Z', 'code': 'The recommendations in lux are organized by three different tabs, which represent potential next steps that users can take in their exploration. From the visualisations we infer that there are three different species of penguins — Adelie, Chinstrap, and Gentoo. There are also three different islands — Torgersen, Biscoe, and Dream; and both male and female species have been included in the dataset.', 'id': 'cbe40b9caf664e9c8bdfd599e239e767', 'order_count': 14}, {'type': 'selectMarkdownCell', 'time': '2021-02-20T03:35:47.026Z', 'code': 'The recommendations in lux are organized by three different tabs, which represent potential next steps that users can take in their exploration. From the visualisations we infer that there are three different species of penguins — Adelie, Chinstrap, and Gentoo. There are also three different islands — Torgersen, Biscoe, and Dream; and both male and female species have been included in the dataset.', 'id': 'cbe40b9caf664e9c8bdfd599e239e767', 'order_count': 14}, {'type': 'selectMarkdownCell', 'time': '2021-02-20T03:35:47.521Z', 'code': 'The recommendations in lux are organized by three different tabs, which represent potential next steps that users can take in their exploration. From the visualisations we infer that there are three different species of penguins — Adelie, Chinstrap, and Gentoo. There are also three different islands — Torgersen, Biscoe, and Dream; and both male and female species have been included in the dataset.', 'id': 'cbe40b9caf664e9c8bdfd599e239e767', 'order_count': 14}, {'type': 'selectMarkdownCell', 'time': '2021-02-20T03:35:49.209Z', 'code': 'The recommendations in lux are organized by three different tabs, which represent potential next steps that users can take in their exploration. From the visualisations we infer that there are three different species of penguins — Adelie, Chinstrap, and Gentoo. There are also three different islands — Torgersen, Biscoe, and Dream; and both male and female species have been included in the dataset.', 'id': 'cbe40b9caf664e9c8bdfd599e239e767', 'order_count': 14}, {'type': 'selectMarkdownCell', 'time': '2021-02-20T03:35:54.073Z', 'code': 'The recommendations in lux are organized by three different tabs, which represent potential next steps that users can take in their exploration. From the visualisations we infer that there are three different species of penguins — Adelie, Chinstrap, and Gentoo. There are also three different islands — Torgersen, Biscoe, and Dream; and both male and female species have been included in the dataset.', 'id': 'cbe40b9caf664e9c8bdfd599e239e767', 'order_count': 14}, {'time': '2021-02-20T03:35:57.961Z', 'code': 'The recommendations in lux are organized by three different tabs, which represent potential next steps that users can take in their exploration. From the visualisations we infer that there are three different species of penguins — Adelie, Chinstrap, and Gentoo. There are also three different islands — Torgersen, Biscoe, and Dream; and both male and female species have been included in the dataset.', 'id': '661198b1d3ee4b2f80bfeed17e454200', 'order_count': 14}, {'type': 'selectMarkdownCell', 'time': '2021-02-20T03:35:58.697Z', 'code': \"## Intent-based recommendations\\n\\nBeyond the basic recommendations, we can also specify our analysis intent. Let's say that we want to find out how the culmen length varies with the species. We can set the intent here as `[‘culmen_length_mm’,’species’].`When we print out the data frame again, we can see that the recommendations are steered to what is relevant to the intent that we’ve specified.\", 'id': 'b86d3334a58a4214bf702bb03c6745ea', 'order_count': 15}, {'time': '2021-02-20T03:35:59.489Z', 'code': 'The recommendations in lux are organized by three different tabs, which represent potential next steps that users can take in their exploration. From the visualisations we infer that there are three different species of penguins — Adelie, Chinstrap, and Gentoo. There are also three different islands — Torgersen, Biscoe, and Dream; and both male and female species have been included in the dataset.', 'id': '661198b1d3ee4b2f80bfeed17e454200', 'order_count': 14}, {'type': 'selectCodeCell', 'time': '2021-02-20T03:36:13.428Z', 'code': \"df.intent = ['culmen_length_mm','species']\\ndf\", 'id': 'f1d1a0b844764e0984cb0352c461c312', 'order_count': 16}, {'type': 'executeCodeCell', 'time': '2021-02-20T03:36:14.480Z', 'code': \"df.intent = ['culmen_length_mm','species']\\ndf\", 'id': 'f1d1a0b844764e0984cb0352c461c312', 'order_count': 16}, {'type': 'completeCodeCell', 'time': '2021-02-20T03:36:17.462Z', 'code': \"df.intent = ['culmen_length_mm','species']\\ndf\", 'id': 'f1d1a0b844764e0984cb0352c461c312', 'isPrintPandasDf': True, 'isPrintLuxDf': False, 'isPrintVis': False, 'isPrintVisCollection': False, 'order_count': 16}, {'type': 'selectMarkdownCell', 'time': '2021-02-20T03:36:19.825Z', 'code': 'On the left-hand side in the image below, what we see is `Current Visualization` corresponding to the attributes that we have selected. On the right-hand side, we have `Enhance` i.e. what happens when we add an attribute to the current selection. We also have the `Filter` tab which adds filter while fixing the selected variable.\\n\\nIf you closely look at the correlations within species, culmen length and depth are positively correlated. This is a classic example of [**Simpson’s paradox**](https://en.wikipedia.org/wiki/Simpson%27s_paradox).\\n\\n![](https://miro.medium.com/max/254/1*bN1pTPMGUB8g7EpQurQbsQ.png)\\n\\nFinally, you can get a pretty clear separation between all three species by looking at flipper length versus culmen length.\\n\\n\\n\\n![Image for post](https://miro.medium.com/max/258/1*1VeJ6DCycXM67Eg6l41vdg.png)\\n', 'id': 'f149d2c8e93d49a9b68002bddc73d3a0', 'order_count': 17}, {'type': 'selectMarkdownCell', 'time': '2021-02-20T03:36:21.369Z', 'code': 'On the left-hand side in the image below, what we see is `Current Visualization` corresponding to the attributes that we have selected. On the right-hand side, we have `Enhance` i.e. what happens when we add an attribute to the current selection. We also have the `Filter` tab which adds filter while fixing the selected variable.\\n\\nIf you closely look at the correlations within species, culmen length and depth are positively correlated. This is a classic example of [**Simpson’s paradox**](https://en.wikipedia.org/wiki/Simpson%27s_paradox).\\n\\n![](https://miro.medium.com/max/254/1*bN1pTPMGUB8g7EpQurQbsQ.png)\\n\\nFinally, you can get a pretty clear separation between all three species by looking at flipper length versus culmen length.\\n\\n\\n\\n![Image for post](https://miro.medium.com/max/258/1*1VeJ6DCycXM67Eg6l41vdg.png)\\n', 'id': 'f149d2c8e93d49a9b68002bddc73d3a0', 'order_count': 17}, {'type': 'selectCodeCell', 'time': '2021-02-20T03:36:23.149Z', 'code': \"df.intent = ['culmen_length_mm','species']\\ndf\", 'id': 'f1d1a0b844764e0984cb0352c461c312', 'order_count': 16}, {'type': 'selectCodeCell', 'time': '2021-02-20T03:36:48.716Z', 'code': \"df.save_as_html('file.html')\", 'id': 'f1f7bf598db645438656a248f17d1d7a', 'order_count': 19}, {'type': 'selectCodeCell', 'time': '2021-02-20T03:36:49.427Z', 'code': 'df.recommendation', 'id': 'c99fbeba6f824cf7949af7ca948bcbd0', 'order_count': 21}, {'type': 'selectMarkdownCell', 'time': '2021-02-20T03:45:49.461Z', 'code': 'The recommendations in lux are organized by three different tabs, which represent potential next steps that users can take in their exploration. From the visualisations we infer that there are three different species of penguins — Adelie, Chinstrap, and Gentoo. There are also three different islands — Torgersen, Biscoe, and Dream; and both male and female species have been included in the dataset.', 'id': '4ef99ba7458f444e9f97e068ae813fcf', 'order_count': 14}, {'type': 'editMarkdownCell', 'time': '2021-02-20T03:45:49.466Z', 'code': 'The recommendations in lux are organized by three different tabs, which represent potential next steps that users can take in their exploration. From the visualisations we infer that there are three different species of penguins — Adelie, Chinstrap, and Gentoo. There are also three different islands — Torgersen, Biscoe, and Dream; and both male and female species have been included in the dataset.', 'id': '4ef99ba7458f444e9f97e068ae813fcf', 'order_count': 14}, {'type': 'selectMarkdownCell', 'time': '2021-02-20T03:45:56.288Z', 'code': \"## Intent-based recommendations\\n\\nBeyond the basic recommendations, we can also specify our analysis intent. Let's say that we want to find out how the culmen length varies with the species. We can set the intent here as `[‘culmen_length_mm’,’species’].`When we print out the data frame again, we can see that the recommendations are steered to what is relevant to the intent that we’ve specified.\", 'id': 'b86d3334a58a4214bf702bb03c6745ea', 'order_count': 15}, {'type': 'kernelKilled', 'time': '2021-02-20T03:46:28.216Z'}, {'type': 'selectMarkdownCell', 'time': '2021-02-20T03:49:07.944Z', 'code': \"## Intent-based recommendations\\n\\nBeyond the basic recommendations, we can also specify our analysis intent. Let's say that we want to find out how the culmen length varies with the species. We can set the intent here as `[‘culmen_length_mm’,’species’].`When we print out the data frame again, we can see that the recommendations are steered to what is relevant to the intent that we’ve specified.\", 'id': 'b86d3334a58a4214bf702bb03c6745ea', 'order_count': 15}, {'type': 'selectMarkdownCell', 'time': '2021-02-20T03:49:08.241Z', 'code': \"## Intent-based recommendations\\n\\nBeyond the basic recommendations, we can also specify our analysis intent. Let's say that we want to find out how the culmen length varies with the species. We can set the intent here as `[‘culmen_length_mm’,’species’].`When we print out the data frame again, we can see that the recommendations are steered to what is relevant to the intent that we’ve specified.\", 'id': 'b86d3334a58a4214bf702bb03c6745ea', 'order_count': 15}, {'type': 'selectMarkdownCell', 'time': '2021-02-20T03:49:11.639Z', 'code': \"## Intent-based recommendations\\n\\nBeyond the basic recommendations, we can also specify our analysis intent. Let's say that we want to find out how the culmen length varies with the species. We can set the intent here as `[‘culmen_length_mm’,’species’].`When we print out the data frame again, we can see that the recommendations are steered to what is relevant to the intent that we’ve specified.\", 'id': 'b86d3334a58a4214bf702bb03c6745ea', 'order_count': 15}, {'type': 'selectMarkdownCell', 'time': '2021-02-20T03:49:14.136Z', 'code': \"## Intent-based recommendations\\n\\nBeyond the basic recommendations, we can also specify our analysis intent. Let's say that we want to find out how the culmen length varies with the species. We can set the intent here as `[‘culmen_length_mm’,’species’].`When we print out the data frame again, we can see that the recommendations are steered to what is relevant to the intent that we’ve specified.\", 'id': 'b86d3334a58a4214bf702bb03c6745ea', 'order_count': 15}, {'type': 'selectMarkdownCell', 'time': '2021-02-20T03:49:15.752Z', 'code': \"## Intent-based recommendations\\n\\nBeyond the basic recommendations, we can also specify our analysis intent. Let's say that we want to find out how the culmen length varies with the species. We can set the intent here as `[‘culmen_length_mm’,’species’].`When we print out the data frame again, we can see that the recommendations are steered to what is relevant to the intent that we’ve specified.\", 'id': 'b86d3334a58a4214bf702bb03c6745ea', 'order_count': 15}, {'type': 'selectCodeCell', 'time': '2021-02-20T03:53:47.810Z', 'code': \"df.intent = ['culmen_length_mm','species']\\ndf\", 'id': 'f1d1a0b844764e0984cb0352c461c312', 'order_count': 16}, {'type': 'editCodeCell', 'time': '2021-02-20T03:53:48.770Z', 'code': \"df.intent = ['culmen_length_mm','species']\\ndf\", 'id': 'f1d1a0b844764e0984cb0352c461c312', 'order_count': 16}, {'type': 'editCodeCell', 'time': '2021-02-20T03:57:02.038Z', 'code': \"df.intent = ['culmen_length_mm','species']\\ndf\", 'id': 'f1d1a0b844764e0984cb0352c461c312', 'order_count': 16}, {'type': 'selectMarkdownCell', 'time': '2021-02-20T03:57:02.094Z', 'code': 'On the left-hand side in the image below, what we see is `Current Visualization` corresponding to the attributes that we have selected. On the right-hand side, we have `Enhance` i.e. what happens when we add an attribute to the current selection. We also have the `Filter` tab which adds filter while fixing the selected variable.\\n\\nIf you closely look at the correlations within species, culmen length and depth are positively correlated. This is a classic example of [**Simpson’s paradox**](https://en.wikipedia.org/wiki/Simpson%27s_paradox).\\n\\n![](https://miro.medium.com/max/254/1*bN1pTPMGUB8g7EpQurQbsQ.png)\\n\\nFinally, you can get a pretty clear separation between all three species by looking at flipper length versus culmen length.\\n\\n\\n\\n![Image for post](https://miro.medium.com/max/258/1*1VeJ6DCycXM67Eg6l41vdg.png)\\n', 'id': 'f149d2c8e93d49a9b68002bddc73d3a0', 'order_count': 17}, {'type': 'selectMarkdownCell', 'time': '2021-02-20T03:57:02.591Z', 'code': 'On the left-hand side in the image below, what we see is `Current Visualization` corresponding to the attributes that we have selected. On the right-hand side, we have `Enhance` i.e. what happens when we add an attribute to the current selection. We also have the `Filter` tab which adds filter while fixing the selected variable.\\n\\nIf you closely look at the correlations within species, culmen length and depth are positively correlated. This is a classic example of [**Simpson’s paradox**](https://en.wikipedia.org/wiki/Simpson%27s_paradox).\\n\\n![](https://miro.medium.com/max/254/1*bN1pTPMGUB8g7EpQurQbsQ.png)\\n\\nFinally, you can get a pretty clear separation between all three species by looking at flipper length versus culmen length.\\n\\n\\n\\n![Image for post](https://miro.medium.com/max/258/1*1VeJ6DCycXM67Eg6l41vdg.png)\\n', 'id': 'f149d2c8e93d49a9b68002bddc73d3a0', 'order_count': 17}, {'type': 'selectMarkdownCell', 'time': '2021-02-20T03:57:02.767Z', 'code': 'On the left-hand side in the image below, what we see is `Current Visualization` corresponding to the attributes that we have selected. On the right-hand side, we have `Enhance` i.e. what happens when we add an attribute to the current selection. We also have the `Filter` tab which adds filter while fixing the selected variable.\\n\\nIf you closely look at the correlations within species, culmen length and depth are positively correlated. This is a classic example of [**Simpson’s paradox**](https://en.wikipedia.org/wiki/Simpson%27s_paradox).\\n\\n![](https://miro.medium.com/max/254/1*bN1pTPMGUB8g7EpQurQbsQ.png)\\n\\nFinally, you can get a pretty clear separation between all three species by looking at flipper length versus culmen length.\\n\\n\\n\\n![Image for post](https://miro.medium.com/max/258/1*1VeJ6DCycXM67Eg6l41vdg.png)\\n', 'id': 'f149d2c8e93d49a9b68002bddc73d3a0', 'order_count': 17}, {'type': 'selectMarkdownCell', 'time': '2021-02-20T03:57:03.463Z', 'code': 'On the left-hand side in the image below, what we see is `Current Visualization` corresponding to the attributes that we have selected. On the right-hand side, we have `Enhance` i.e. what happens when we add an attribute to the current selection. We also have the `Filter` tab which adds filter while fixing the selected variable.\\n\\nIf you closely look at the correlations within species, culmen length and depth are positively correlated. This is a classic example of [**Simpson’s paradox**](https://en.wikipedia.org/wiki/Simpson%27s_paradox).\\n\\n![](https://miro.medium.com/max/254/1*bN1pTPMGUB8g7EpQurQbsQ.png)\\n\\nFinally, you can get a pretty clear separation between all three species by looking at flipper length versus culmen length.\\n\\n\\n\\n![Image for post](https://miro.medium.com/max/258/1*1VeJ6DCycXM67Eg6l41vdg.png)\\n', 'id': 'f149d2c8e93d49a9b68002bddc73d3a0', 'order_count': 17}, {'type': 'selectMarkdownCell', 'time': '2021-02-20T03:57:03.686Z', 'code': 'On the left-hand side in the image below, what we see is `Current Visualization` corresponding to the attributes that we have selected. On the right-hand side, we have `Enhance` i.e. what happens when we add an attribute to the current selection. We also have the `Filter` tab which adds filter while fixing the selected variable.\\n\\nIf you closely look at the correlations within species, culmen length and depth are positively correlated. This is a classic example of [**Simpson’s paradox**](https://en.wikipedia.org/wiki/Simpson%27s_paradox).\\n\\n![](https://miro.medium.com/max/254/1*bN1pTPMGUB8g7EpQurQbsQ.png)\\n\\nFinally, you can get a pretty clear separation between all three species by looking at flipper length versus culmen length.\\n\\n\\n\\n![Image for post](https://miro.medium.com/max/258/1*1VeJ6DCycXM67Eg6l41vdg.png)\\n', 'id': 'f149d2c8e93d49a9b68002bddc73d3a0', 'order_count': 17}, {'type': 'selectMarkdownCell', 'time': '2021-02-20T03:57:14.574Z', 'code': 'On the left-hand side in the image below, what we see is `Current Visualization` corresponding to the attributes that we have selected. On the right-hand side, we have `Enhance` i.e. what happens when we add an attribute to the current selection. We also have the `Filter` tab which adds filter while fixing the selected variable.\\n\\nIf you closely look at the correlations within species, culmen length and depth are positively correlated. This is a classic example of [**Simpson’s paradox**](https://en.wikipedia.org/wiki/Simpson%27s_paradox).\\n\\n![](https://miro.medium.com/max/254/1*bN1pTPMGUB8g7EpQurQbsQ.png)\\n\\nFinally, you can get a pretty clear separation between all three species by looking at flipper length versus culmen length.\\n\\n\\n\\n![Image for post](https://miro.medium.com/max/258/1*1VeJ6DCycXM67Eg6l41vdg.png)\\n', 'id': 'f149d2c8e93d49a9b68002bddc73d3a0', 'order_count': 17}, {'type': 'selectMarkdownCell', 'time': '2021-02-20T03:57:17.046Z', 'code': 'On the left-hand side in the image below, what we see is `Current Visualization` corresponding to the attributes that we have selected. On the right-hand side, we have `Enhance` i.e. what happens when we add an attribute to the current selection. We also have the `Filter` tab which adds filter while fixing the selected variable.\\n\\nIf you closely look at the correlations within species, culmen length and depth are positively correlated. This is a classic example of [**Simpson’s paradox**](https://en.wikipedia.org/wiki/Simpson%27s_paradox).\\n\\n![](https://miro.medium.com/max/254/1*bN1pTPMGUB8g7EpQurQbsQ.png)\\n\\nFinally, you can get a pretty clear separation between all three species by looking at flipper length versus culmen length.\\n\\n\\n\\n![Image for post](https://miro.medium.com/max/258/1*1VeJ6DCycXM67Eg6l41vdg.png)\\n', 'id': 'f149d2c8e93d49a9b68002bddc73d3a0', 'order_count': 17}, {'type': 'selectMarkdownCell', 'time': '2021-02-20T03:57:27.878Z', 'code': 'On the left-hand side in the image below, what we see is `Current Visualization` corresponding to the attributes that we have selected. On the right-hand side, we have `Enhance` i.e. what happens when we add an attribute to the current selection. We also have the `Filter` tab which adds filter while fixing the selected variable.\\n\\nIf you closely look at the correlations within species, culmen length and depth are positively correlated. This is a classic example of [**Simpson’s paradox**](https://en.wikipedia.org/wiki/Simpson%27s_paradox).\\n\\n![](https://miro.medium.com/max/254/1*bN1pTPMGUB8g7EpQurQbsQ.png)\\n\\nFinally, you can get a pretty clear separation between all three species by looking at flipper length versus culmen length.\\n\\n\\n\\n![Image for post](https://miro.medium.com/max/258/1*1VeJ6DCycXM67Eg6l41vdg.png)\\n', 'id': 'f149d2c8e93d49a9b68002bddc73d3a0', 'order_count': 17}, {'type': 'selectCodeCell', 'time': '2021-02-20T03:57:31.097Z', 'code': \"df.save_as_html('file.html')\", 'id': 'f1f7bf598db645438656a248f17d1d7a', 'order_count': 19}, {'type': 'selectMarkdownCell', 'time': '2021-02-20T03:57:33.335Z', 'code': '## Exporting visualizations from Widget\\n\\nLux also makes it pretty easy to export and share the generated visualizations. The visualizations can be exported into a static HTML as follows:\\n', 'id': '80c829c603d94582861d20af3320a953', 'order_count': 18}, {'type': 'selectCodeCell', 'time': '2021-02-20T06:08:43.368Z', 'code': 'df.recommendation', 'id': 'c99fbeba6f824cf7949af7ca948bcbd0', 'order_count': 21}, {'type': 'editCodeCell', 'time': '2021-02-20T06:08:43.374Z', 'code': 'df.recommendation', 'id': 'c99fbeba6f824cf7949af7ca948bcbd0', 'order_count': 21}, {'type': 'selectCodeCell', 'time': '2021-02-20T06:08:43.432Z', 'code': 'df.recommendation', 'id': 'c99fbeba6f824cf7949af7ca948bcbd0', 'order_count': 21}, {'type': 'selectCodeCell', 'time': '2021-02-20T06:08:43.599Z', 'code': 'df.recommendation', 'id': 'c99fbeba6f824cf7949af7ca948bcbd0', 'order_count': 21}, {'type': 'selectCodeCell', 'time': '2021-02-20T06:08:44.518Z', 'code': 'df.recommendation', 'id': 'c99fbeba6f824cf7949af7ca948bcbd0', 'order_count': 21}, {'type': 'selectCodeCell', 'time': '2021-02-20T06:08:45.542Z', 'code': 'df.recommendation', 'id': 'c99fbeba6f824cf7949af7ca948bcbd0', 'order_count': 21}, {'type': 'selectCodeCell', 'time': '2021-02-20T06:08:47.686Z', 'code': 'df.recommendation', 'id': 'c99fbeba6f824cf7949af7ca948bcbd0', 'order_count': 21}, {'type': 'selectCodeCell', 'time': '2021-02-20T06:08:50.142Z', 'code': 'df.recommendation', 'id': 'c99fbeba6f824cf7949af7ca948bcbd0', 'order_count': 21}, {'type': 'selectCodeCell', 'time': '2021-02-20T06:08:52.997Z', 'code': 'df.recommendation', 'id': 'c99fbeba6f824cf7949af7ca948bcbd0', 'order_count': 21}, {'type': 'selectCodeCell', 'time': '2021-02-20T06:08:54.102Z', 'code': 'df.recommendation', 'id': 'c99fbeba6f824cf7949af7ca948bcbd0', 'order_count': 21}, {'type': 'editCodeCell', 'time': '2021-02-20T06:09:11.797Z', 'code': 'df.recommendation', 'id': 'c99fbeba6f824cf7949af7ca948bcbd0', 'order_count': 21}, {'type': 'selectMarkdownCell', 'time': '2021-02-20T06:09:12.584Z', 'code': '## Exporting visualizations from Widget\\n\\nLux also makes it pretty easy to export and share the generated visualizations. The visualizations can be exported into a static HTML as follows:\\n', 'id': '80c829c603d94582861d20af3320a953', 'order_count': 18}, {'type': 'selectMarkdownCell', 'time': '2021-02-20T06:09:15.387Z', 'code': '## Exporting visualizations from Widget\\n\\nLux also makes it pretty easy to export and share the generated visualizations. The visualizations can be exported into a static HTML as follows:\\n', 'id': '80c829c603d94582861d20af3320a953', 'order_count': 18}, {'type': 'selectMarkdownCell', 'time': '2021-02-20T06:09:18.267Z', 'code': '## Exporting visualizations from Widget\\n\\nLux also makes it pretty easy to export and share the generated visualizations. The visualizations can be exported into a static HTML as follows:\\n', 'id': '80c829c603d94582861d20af3320a953', 'order_count': 18}, {'type': 'selectMarkdownCell', 'time': '2021-02-20T06:09:22.242Z', 'code': 'We can also access the set of [recommendations generated for the](https://lux-api.readthedocs.io/en/latest/source/guide/export.html) data frames via the properties recommendation. The output is a dictionary, keyed by the name of the recommendation category.', 'id': 'dc0924bf916e48aa8ea16194fb05184a', 'order_count': 20}, {'type': 'selectMarkdownCell', 'time': '2021-02-20T06:09:23.468Z', 'code': 'We can also access the set of [recommendations generated for the](https://lux-api.readthedocs.io/en/latest/source/guide/export.html) data frames via the properties recommendation. The output is a dictionary, keyed by the name of the recommendation category.', 'id': 'dc0924bf916e48aa8ea16194fb05184a', 'order_count': 20}, {'type': 'selectMarkdownCell', 'time': '2021-02-20T06:09:24.947Z', 'code': 'We can also access the set of [recommendations generated for the](https://lux-api.readthedocs.io/en/latest/source/guide/export.html) data frames via the properties recommendation. The output is a dictionary, keyed by the name of the recommendation category.', 'id': 'dc0924bf916e48aa8ea16194fb05184a', 'order_count': 20}, {'type': 'selectCodeCell', 'time': '2021-02-20T06:09:30.574Z', 'code': 'df.recommendation', 'id': 'c99fbeba6f824cf7949af7ca948bcbd0', 'order_count': 21}, {'type': 'selectCodeCell', 'time': '2021-02-20T06:09:33.190Z', 'code': 'df.recommendation', 'id': 'c99fbeba6f824cf7949af7ca948bcbd0', 'order_count': 21}, {'type': 'executeCodeCell', 'time': '2021-02-20T06:09:34.133Z', 'code': 'df.recommendation', 'id': 'c99fbeba6f824cf7949af7ca948bcbd0', 'order_count': 21}, {'type': 'kernelRestart', 'time': '2021-02-20T06:09:37.362Z'}, {'type': 'selectCodeCell', 'time': '2021-02-20T06:23:53.710Z', 'code': \"df.save_as_html('file.html')\", 'id': 'f1f7bf598db645438656a248f17d1d7a', 'order_count': 19}, {'type': 'editCodeCell', 'time': '2021-02-20T06:23:53.715Z', 'code': \"df.save_as_html('file.html')\", 'id': 'f1f7bf598db645438656a248f17d1d7a', 'order_count': 19}, {'type': 'selectCodeCell', 'time': '2021-02-20T06:23:53.991Z', 'code': \"df.save_as_html('file.html')\", 'id': 'f1f7bf598db645438656a248f17d1d7a', 'order_count': 19}, {'type': 'selectCodeCell', 'time': '2021-02-20T06:23:54.444Z', 'code': \"df.save_as_html('file.html')\", 'id': 'f1f7bf598db645438656a248f17d1d7a', 'order_count': 19}, {'type': 'selectCodeCell', 'time': '2021-02-20T06:23:54.637Z', 'code': \"df.save_as_html('file.html')\", 'id': 'f1f7bf598db645438656a248f17d1d7a', 'order_count': 19}, {'type': 'editCodeCell', 'time': '2021-02-20T06:26:14.969Z', 'code': \"df.save_as_html('file.html')\", 'id': 'f1f7bf598db645438656a248f17d1d7a', 'order_count': 19}, {'type': 'selectMarkdownCell', 'time': '2021-02-20T06:26:23.969Z', 'code': '## Exporting Visualizations as Code\\n\\nNot only can we export visualization as HTML but also as code. The visualizations can then be exported to code in  [Altair](https://altair-viz.github.io/)  for further edits or as  [Vega-Lite](https://vega.github.io/vega-lite/)  specification. More details can be found in the  [documentation](https://lux-api.readthedocs.io/en/latest/source/guide/export.html).', 'id': 'db6a1298b3a04e93b24b0d2b83229dbc', 'order_count': 22}, {'type': 'selectMarkdownCell', 'time': '2021-02-20T06:26:26.466Z', 'code': '## Exporting Visualizations as Code\\n\\nNot only can we export visualization as HTML but also as code. The visualizations can then be exported to code in  [Altair](https://altair-viz.github.io/)  for further edits or as  [Vega-Lite](https://vega.github.io/vega-lite/)  specification. More details can be found in the  [documentation](https://lux-api.readthedocs.io/en/latest/source/guide/export.html).', 'id': 'db6a1298b3a04e93b24b0d2b83229dbc', 'order_count': 22}, {'type': 'selectCodeCell', 'time': '2021-02-20T06:31:01.907Z', 'code': 'df.recommendation', 'id': 'c99fbeba6f824cf7949af7ca948bcbd0', 'order_count': 21}, {'type': 'editCodeCell', 'time': '2021-02-20T06:31:01.915Z', 'code': 'df.recommendation', 'id': 'c99fbeba6f824cf7949af7ca948bcbd0', 'order_count': 21}, {'type': 'selectCodeCell', 'time': '2021-02-20T06:31:02.157Z', 'code': 'df.recommendation', 'id': 'c99fbeba6f824cf7949af7ca948bcbd0', 'order_count': 21}, {'type': 'selectCodeCell', 'time': '2021-02-20T06:31:02.877Z', 'code': 'df.recommendation', 'id': 'c99fbeba6f824cf7949af7ca948bcbd0', 'order_count': 21}, {'type': 'editCodeCell', 'time': '2021-02-20T06:32:47.605Z', 'code': 'df.recommendation', 'id': 'c99fbeba6f824cf7949af7ca948bcbd0', 'order_count': 21}]\n"
     ]
    },
    {
     "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": [
    "# takes care of scrolling issue where if they stop scrolling for a moment, it treats as separate scrolls\n",
    "\n",
    "function_freq = {}\n",
    "timing = {}\n",
    "for log in playground_df['history']:\n",
    "    prev = ''\n",
    "    for entry in log:\n",
    "        if 'type' in entry.keys():\n",
    "            if 'stopScroll' not in prev:\n",
    "                if entry['type'] in function_freq.keys():\n",
    "                    function_freq[entry['type']] += 1\n",
    "                else:\n",
    "                    function_freq[entry['type']] = 1\n",
    "            prev = entry['type']\n",
    "        else:\n",
    "            print(log)\n",
    "        \n",
    "function_freq['stopScroll'] = function_freq['startScroll']\n",
    "function_freq['initWidget'] = function_freq['initWidget']/2\n",
    "# del function_freq['selectCodeCell']\n",
    "plt.bar(function_freq.keys(), function_freq.values())\n",
    "plt.xticks(list(function_freq.keys()), rotation=90)\n",
    "plt.title('Log Event Frequency')\n",
    "plt.ylabel('Counts')\n",
    "plt.show()\n",
    "        "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "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": [
    "lux_events = ['initWidget', 'startScroll', 'stopScroll', 'openWarning', 'switchTab', 'clickVis', 'unclickVis', 'toggleBtnClick', 'intentBtnClick', 'closeWarning', 'exportBtnClick']\n",
    "lux_values = []\n",
    "for event in lux_events:\n",
    "    lux_values.append(function_freq[event])\n",
    "    \n",
    "plt.bar(lux_events, lux_values)\n",
    "plt.xticks(lux_events, rotation=90)\n",
    "plt.title('Log Event Frequency')\n",
    "plt.ylabel('Counts')\n",
    "plt.show()\n",
    "          "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "9ixn8fst1   13 days 06:36:24.097000\n",
       "czs122fic    0 days 01:04:34.125000\n",
       "sg8cq2mmb    0 days 00:52:02.763000\n",
       "0qocq99zz    0 days 00:15:17.128000\n",
       "txodzcfin    2 days 20:47:44.451000\n",
       "                      ...          \n",
       "8q8ohu1o0    0 days 00:00:59.107000\n",
       "xyvxlzdlc    0 days 00:14:03.855000\n",
       "kgcp5p4gr    0 days 00:08:27.190000\n",
       "yzcb56im4    0 days 00:19:54.735000\n",
       "upk8v5eo2    0 days 00:00:27.113000\n",
       "Length: 477, dtype: timedelta64[ns]"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "playground_df['endTime'] - playground_df['startTime']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "grouped_df.reset_index().drop(columns=[\"startTime\",\"endTime\"]).to_csv(\"user_groups.csv\",index=None)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "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": [
    "other_df = grouped_df[grouped_df['userGroup']=='Other']\n",
    "plot_nb_progression(other_df, 'Other', len(other_df))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "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>userGroup</th>\n",
       "      <th>startTime</th>\n",
       "      <th>endTime</th>\n",
       "      <th>tried_0</th>\n",
       "      <th>tried_1</th>\n",
       "      <th>tried_2</th>\n",
       "      <th>tried_3</th>\n",
       "      <th>tried_4</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>userHash</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>003724cc5e41a981d3fad0ca3f21888d</th>\n",
       "      <td>Other</td>\n",
       "      <td>2020-12-27 01:20:56.927000+00:00</td>\n",
       "      <td>2020-12-27 03:12:26.956000+00:00</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>003a65e7008e9f7db64a6559970f962c</th>\n",
       "      <td>Other</td>\n",
       "      <td>2020-11-30 05:36:43.016000+00:00</td>\n",
       "      <td>2020-11-30 06:11:08.975000+00:00</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>006b045214badd8a59f7f11c1eae488c</th>\n",
       "      <td>Other</td>\n",
       "      <td>2020-12-03 02:55:46.498000+00:00</td>\n",
       "      <td>2020-12-03 03:12:19.732000+00:00</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>00ae885f453834201f83376bcfddd31a</th>\n",
       "      <td>Other</td>\n",
       "      <td>2020-11-30 07:02:30.883000+00:00</td>\n",
       "      <td>2020-11-30 07:02:53.966000+00:00</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>040b920fcc03cbc13e159b961d57263f</th>\n",
       "      <td>Other</td>\n",
       "      <td>2020-12-01 08:39:38.863000+00:00</td>\n",
       "      <td>2020-12-01 08:53:22.688000+00:00</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>f920d5c664474a7f2b0e4aef22dc1e60</th>\n",
       "      <td>Other</td>\n",
       "      <td>2020-12-24 13:18:46.572000+00:00</td>\n",
       "      <td>2020-12-24 13:41:37.082000+00:00</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>f9f880ad977d9c674f4eddb31fde8bc3</th>\n",
       "      <td>Other</td>\n",
       "      <td>2020-11-30 10:30:26.448000+00:00</td>\n",
       "      <td>2020-11-30 10:31:21.925000+00:00</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>fe50a4ea1e132d4d505b7941f1f48ef0</th>\n",
       "      <td>Other</td>\n",
       "      <td>2020-12-01 06:17:35.472000+00:00</td>\n",
       "      <td>2020-12-01 06:34:09.014000+00:00</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>feba0e7c024be16c21b46a6257a10367</th>\n",
       "      <td>Other</td>\n",
       "      <td>2020-12-25 16:37:15.015000+00:00</td>\n",
       "      <td>2020-12-25 17:06:12.739000+00:00</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>fff0c89988ecc9587435c7f48dc586ee</th>\n",
       "      <td>Other</td>\n",
       "      <td>2020-11-30 00:37:42.834000+00:00</td>\n",
       "      <td>2020-11-30 00:39:07.991000+00:00</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>288 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                 userGroup                        startTime  \\\n",
       "userHash                                                                      \n",
       "003724cc5e41a981d3fad0ca3f21888d     Other 2020-12-27 01:20:56.927000+00:00   \n",
       "003a65e7008e9f7db64a6559970f962c     Other 2020-11-30 05:36:43.016000+00:00   \n",
       "006b045214badd8a59f7f11c1eae488c     Other 2020-12-03 02:55:46.498000+00:00   \n",
       "00ae885f453834201f83376bcfddd31a     Other 2020-11-30 07:02:30.883000+00:00   \n",
       "040b920fcc03cbc13e159b961d57263f     Other 2020-12-01 08:39:38.863000+00:00   \n",
       "...                                    ...                              ...   \n",
       "f920d5c664474a7f2b0e4aef22dc1e60     Other 2020-12-24 13:18:46.572000+00:00   \n",
       "f9f880ad977d9c674f4eddb31fde8bc3     Other 2020-11-30 10:30:26.448000+00:00   \n",
       "fe50a4ea1e132d4d505b7941f1f48ef0     Other 2020-12-01 06:17:35.472000+00:00   \n",
       "feba0e7c024be16c21b46a6257a10367     Other 2020-12-25 16:37:15.015000+00:00   \n",
       "fff0c89988ecc9587435c7f48dc586ee     Other 2020-11-30 00:37:42.834000+00:00   \n",
       "\n",
       "                                                          endTime  tried_0  \\\n",
       "userHash                                                                     \n",
       "003724cc5e41a981d3fad0ca3f21888d 2020-12-27 03:12:26.956000+00:00        0   \n",
       "003a65e7008e9f7db64a6559970f962c 2020-11-30 06:11:08.975000+00:00        0   \n",
       "006b045214badd8a59f7f11c1eae488c 2020-12-03 03:12:19.732000+00:00        0   \n",
       "00ae885f453834201f83376bcfddd31a 2020-11-30 07:02:53.966000+00:00        0   \n",
       "040b920fcc03cbc13e159b961d57263f 2020-12-01 08:53:22.688000+00:00        0   \n",
       "...                                                           ...      ...   \n",
       "f920d5c664474a7f2b0e4aef22dc1e60 2020-12-24 13:41:37.082000+00:00        0   \n",
       "f9f880ad977d9c674f4eddb31fde8bc3 2020-11-30 10:31:21.925000+00:00        0   \n",
       "fe50a4ea1e132d4d505b7941f1f48ef0 2020-12-01 06:34:09.014000+00:00        0   \n",
       "feba0e7c024be16c21b46a6257a10367 2020-12-25 17:06:12.739000+00:00        0   \n",
       "fff0c89988ecc9587435c7f48dc586ee 2020-11-30 00:39:07.991000+00:00        0   \n",
       "\n",
       "                                  tried_1  tried_2  tried_3  tried_4  \n",
       "userHash                                                              \n",
       "003724cc5e41a981d3fad0ca3f21888d        1        0        0        0  \n",
       "003a65e7008e9f7db64a6559970f962c        0        0        0        0  \n",
       "006b045214badd8a59f7f11c1eae488c        0        0        0        0  \n",
       "00ae885f453834201f83376bcfddd31a        0        0        0        0  \n",
       "040b920fcc03cbc13e159b961d57263f        0        0        0        0  \n",
       "...                                   ...      ...      ...      ...  \n",
       "f920d5c664474a7f2b0e4aef22dc1e60        0        0        0        0  \n",
       "f9f880ad977d9c674f4eddb31fde8bc3        0        0        0        0  \n",
       "fe50a4ea1e132d4d505b7941f1f48ef0        0        0        0        0  \n",
       "feba0e7c024be16c21b46a6257a10367        0        0        0        0  \n",
       "fff0c89988ecc9587435c7f48dc586ee        0        0        0        0  \n",
       "\n",
       "[288 rows x 8 columns]"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "other_df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Computing Time until Toggle and Time in Each Tab"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[{'type': 'executeCodeCell',\n",
       "  'time': '2020-10-30T21:23:52.207Z',\n",
       "  'code': '# Collecting basic usage statistics for Lux (For more information, see: https://tinyurl.com/logging-consent)\\nlux.logger = True # Remove this line if you do not want your interactions recorded',\n",
       "  'id': '7ff680bd2c2c46449215062d7823d83d',\n",
       "  'order_count': 3},\n",
       " {'type': 'selectCodeCell',\n",
       "  'time': '2020-10-30T21:23:52.223Z',\n",
       "  'code': 'df = pd.read_csv(\"../data/college.csv\")\\nlux.config.default_display = \"lux\" # Setting default display as Lux',\n",
       "  'id': '45b878b91fd340d488e0684a886af5ec',\n",
       "  'order_count': 4},\n",
       " {'type': 'completeCodeCell',\n",
       "  'time': '2020-10-30T21:23:52.228Z',\n",
       "  'code': '# Collecting basic usage statistics for Lux (For more information, see: https://tinyurl.com/logging-consent)\\nlux.logger = True # Remove this line if you do not want your interactions recorded',\n",
       "  'id': '7ff680bd2c2c46449215062d7823d83d',\n",
       "  'isPrintPandasDf': False,\n",
       "  'isPrintLuxDf': False,\n",
       "  'isPrintVis': False,\n",
       "  'isPrintVisCollection': False,\n",
       "  'order_count': 3},\n",
       " {'type': 'executeCodeCell',\n",
       "  'time': '2020-10-30T21:24:00.347Z',\n",
       "  'code': 'df = pd.read_csv(\"../data/college.csv\")\\nlux.config.default_display = \"lux\" # Setting default display as Lux',\n",
       "  'id': '45b878b91fd340d488e0684a886af5ec',\n",
       "  'order_count': 4},\n",
       " {'type': 'selectMarkdownCell',\n",
       "  'time': '2020-10-30T21:24:00.364Z',\n",
       "  'code': '## Specifying attributes of interest',\n",
       "  'id': '73e17dfd40644ba18b3163eca3963d9e',\n",
       "  'order_count': 5},\n",
       " {'type': 'completeCodeCell',\n",
       "  'time': '2020-10-30T21:24:00.371Z',\n",
       "  'code': 'df = pd.read_csv(\"../data/college.csv\")\\nlux.config.default_display = \"lux\" # Setting default display as Lux',\n",
       "  'id': '45b878b91fd340d488e0684a886af5ec',\n",
       "  'isPrintPandasDf': False,\n",
       "  'isPrintLuxDf': False,\n",
       "  'isPrintVis': False,\n",
       "  'isPrintVisCollection': False,\n",
       "  'order_count': 4},\n",
       " {'type': 'selectMarkdownCell',\n",
       "  'time': '2020-10-30T21:24:02.342Z',\n",
       "  'code': \"You can indicate that you are interested in an attribute, let's say `AverageCost`.\",\n",
       "  'id': '9237cd284d3c4c548f9c13eb96e4830c',\n",
       "  'order_count': 6},\n",
       " {'type': 'selectCodeCell',\n",
       "  'time': '2020-10-30T21:24:02.883Z',\n",
       "  'code': \"df.intent = ['AverageCost']\\ndf\",\n",
       "  'id': '47f1a1c9fdd64cd1915737b3735a7278',\n",
       "  'order_count': 7},\n",
       " {'type': 'selectMarkdownCell',\n",
       "  'time': '2020-10-30T21:24:07.279Z',\n",
       "  'code': \"You can indicate that you are interested in an attribute, let's say `AverageCost`.\",\n",
       "  'id': '9237cd284d3c4c548f9c13eb96e4830c',\n",
       "  'order_count': 6},\n",
       " {'type': 'selectCodeCell',\n",
       "  'time': '2020-10-30T21:24:08.325Z',\n",
       "  'code': '',\n",
       "  'id': '27edb169c58149cdaa60405fc0469055',\n",
       "  'order_count': 7},\n",
       " {'type': 'editCodeCell',\n",
       "  'time': '2020-10-30T21:24:08.855Z',\n",
       "  'code': '',\n",
       "  'id': '27edb169c58149cdaa60405fc0469055',\n",
       "  'order_count': 7},\n",
       " {'type': 'selectCodeCell',\n",
       "  'time': '2020-10-30T21:24:08.982Z',\n",
       "  'code': '',\n",
       "  'id': '27edb169c58149cdaa60405fc0469055',\n",
       "  'order_count': 7},\n",
       " {'type': 'executeCodeCell',\n",
       "  'time': '2020-10-30T21:24:10.068Z',\n",
       "  'code': 'df',\n",
       "  'id': '27edb169c58149cdaa60405fc0469055',\n",
       "  'order_count': 7},\n",
       " {'type': 'selectCodeCell',\n",
       "  'time': '2020-10-30T21:24:10.080Z',\n",
       "  'code': \"df.intent = ['AverageCost']\\ndf\",\n",
       "  'id': '47f1a1c9fdd64cd1915737b3735a7278',\n",
       "  'order_count': 8},\n",
       " {'type': 'completeCodeCell',\n",
       "  'time': '2020-10-30T21:24:11.291Z',\n",
       "  'code': 'df',\n",
       "  'id': '27edb169c58149cdaa60405fc0469055',\n",
       "  'isPrintPandasDf': False,\n",
       "  'isPrintLuxDf': True,\n",
       "  'isPrintVis': False,\n",
       "  'isPrintVisCollection': False,\n",
       "  'order_count': 7},\n",
       " {'type': 'initWidget', 'time': '2020-10-30T21:24:11.324Z', 'param': ''},\n",
       " {'type': 'initWidget', 'time': '2020-10-30T21:24:12.026Z', 'param': ''},\n",
       " {'type': 'selectCodeCell',\n",
       "  'time': '2020-10-30T21:24:12.761Z',\n",
       "  'code': 'df',\n",
       "  'id': '27edb169c58149cdaa60405fc0469055',\n",
       "  'order_count': 7},\n",
       " {'type': 'selectCodeCell',\n",
       "  'time': '2020-10-30T21:24:18.545Z',\n",
       "  'code': 'df',\n",
       "  'id': '27edb169c58149cdaa60405fc0469055',\n",
       "  'order_count': 7},\n",
       " {'type': 'startScroll',\n",
       "  'time': '2020-10-30T21:24:19.447Z',\n",
       "  'param': 'Correlation'},\n",
       " {'type': 'stopScroll',\n",
       "  'time': '2020-10-30T21:24:19.773Z',\n",
       "  'param': 'Correlation'},\n",
       " {'type': 'startScroll',\n",
       "  'time': '2020-10-30T21:24:19.807Z',\n",
       "  'param': 'Correlation'},\n",
       " {'type': 'stopScroll',\n",
       "  'time': '2020-10-30T21:24:20.185Z',\n",
       "  'param': 'Correlation'},\n",
       " {'type': 'startScroll',\n",
       "  'time': '2020-10-30T21:24:20.242Z',\n",
       "  'param': 'Correlation'},\n",
       " {'type': 'stopScroll',\n",
       "  'time': '2020-10-30T21:24:20.603Z',\n",
       "  'param': 'Correlation'},\n",
       " {'type': 'startScroll',\n",
       "  'time': '2020-10-30T21:24:20.724Z',\n",
       "  'param': 'Correlation'},\n",
       " {'type': 'stopScroll',\n",
       "  'time': '2020-10-30T21:24:21.068Z',\n",
       "  'param': 'Correlation'},\n",
       " {'type': 'startScroll',\n",
       "  'time': '2020-10-30T21:24:21.908Z',\n",
       "  'param': 'Correlation'},\n",
       " {'type': 'stopScroll',\n",
       "  'time': '2020-10-30T21:24:22.103Z',\n",
       "  'param': 'Correlation'},\n",
       " {'type': 'startScroll',\n",
       "  'time': '2020-10-30T21:24:22.341Z',\n",
       "  'param': 'Correlation'},\n",
       " {'type': 'stopScroll',\n",
       "  'time': '2020-10-30T21:24:22.670Z',\n",
       "  'param': 'Correlation'},\n",
       " {'type': 'startScroll',\n",
       "  'time': '2020-10-30T21:24:22.924Z',\n",
       "  'param': 'Correlation'},\n",
       " {'type': 'stopScroll',\n",
       "  'time': '2020-10-30T21:24:23.190Z',\n",
       "  'param': 'Correlation'},\n",
       " {'type': 'startScroll',\n",
       "  'time': '2020-10-30T21:24:23.480Z',\n",
       "  'param': 'Correlation'},\n",
       " {'type': 'stopScroll',\n",
       "  'time': '2020-10-30T21:24:23.907Z',\n",
       "  'param': 'Correlation'},\n",
       " {'type': 'startScroll',\n",
       "  'time': '2020-10-30T21:24:24.164Z',\n",
       "  'param': 'Correlation'},\n",
       " {'type': 'stopScroll',\n",
       "  'time': '2020-10-30T21:24:24.490Z',\n",
       "  'param': 'Correlation'},\n",
       " {'type': 'startScroll',\n",
       "  'time': '2020-10-30T21:24:25.079Z',\n",
       "  'param': 'Correlation'},\n",
       " {'type': 'stopScroll',\n",
       "  'time': '2020-10-30T21:24:25.506Z',\n",
       "  'param': 'Correlation'},\n",
       " {'type': 'startScroll',\n",
       "  'time': '2020-10-30T21:24:26.330Z',\n",
       "  'param': 'Correlation'},\n",
       " {'type': 'stopScroll',\n",
       "  'time': '2020-10-30T21:24:26.625Z',\n",
       "  'param': 'Correlation'},\n",
       " {'type': 'startScroll',\n",
       "  'time': '2020-10-30T21:24:27.395Z',\n",
       "  'param': 'Correlation'},\n",
       " {'type': 'stopScroll',\n",
       "  'time': '2020-10-30T21:24:27.906Z',\n",
       "  'param': 'Correlation'},\n",
       " {'type': 'selectCodeCell',\n",
       "  'time': '2020-10-30T21:24:30.010Z',\n",
       "  'code': 'df',\n",
       "  'id': '27edb169c58149cdaa60405fc0469055',\n",
       "  'order_count': 7},\n",
       " {'type': 'switchTab',\n",
       "  'time': '2020-10-30T21:24:30.012Z',\n",
       "  'param': 'Distribution'},\n",
       " {'type': 'startScroll',\n",
       "  'time': '2020-10-30T21:24:32.863Z',\n",
       "  'param': 'Distribution'},\n",
       " {'type': 'stopScroll',\n",
       "  'time': '2020-10-30T21:24:33.099Z',\n",
       "  'param': 'Distribution'},\n",
       " {'type': 'startScroll',\n",
       "  'time': '2020-10-30T21:24:33.218Z',\n",
       "  'param': 'Distribution'},\n",
       " {'type': 'stopScroll',\n",
       "  'time': '2020-10-30T21:24:33.279Z',\n",
       "  'param': 'Distribution'},\n",
       " {'type': 'startScroll',\n",
       "  'time': '2020-10-30T21:24:33.952Z',\n",
       "  'param': 'Distribution'},\n",
       " {'type': 'stopScroll',\n",
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       "  'code': \"df.intent = ['AverageCost']\\ndf\",\n",
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       "  'code': \"Let's say that in addition to `AverageCost`, you are interested in the looking at a list of attributes that are related to different financial measures, such as `Expenditure` or `MedianDebt`, and how they breakdown with respect to `FundingModel`. \\n\\nYou can specify a list of desired attributes separated by the `|` symbol, which indicates an `OR` relationship between the list of attributes. If multiple clauses are specified, Lux automatically create combinations of the specified attributes. \",\n",
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       "  'param': 'Enhance'},\n",
       " {'type': 'startScroll',\n",
       "  'time': '2020-10-30T21:27:16.168Z',\n",
       "  'param': 'Enhance'},\n",
       " {'type': 'stopScroll',\n",
       "  'time': '2020-10-30T21:27:16.480Z',\n",
       "  'param': 'Enhance'},\n",
       " {'type': 'startScroll',\n",
       "  'time': '2020-10-30T21:27:17.168Z',\n",
       "  'param': 'Enhance'},\n",
       " {'type': 'stopScroll',\n",
       "  'time': '2020-10-30T21:27:17.528Z',\n",
       "  'param': 'Enhance'},\n",
       " {'type': 'startScroll',\n",
       "  'time': '2020-10-30T21:27:17.650Z',\n",
       "  'param': 'Enhance'},\n",
       " {'type': 'stopScroll',\n",
       "  'time': '2020-10-30T21:27:18.128Z',\n",
       "  'param': 'Enhance'},\n",
       " {'type': 'startScroll',\n",
       "  'time': '2020-10-30T21:27:19.118Z',\n",
       "  'param': 'Enhance'},\n",
       " {'type': 'stopScroll',\n",
       "  'time': '2020-10-30T21:27:19.396Z',\n",
       "  'param': 'Enhance'},\n",
       " {'type': 'startScroll',\n",
       "  'time': '2020-10-30T21:27:19.583Z',\n",
       "  'param': 'Enhance'},\n",
       " {'type': 'stopScroll',\n",
       "  'time': '2020-10-30T21:27:20.295Z',\n",
       "  'param': 'Enhance'},\n",
       " {'type': 'startScroll',\n",
       "  'time': '2020-10-30T21:27:20.367Z',\n",
       "  'param': 'Enhance'},\n",
       " {'type': 'stopScroll',\n",
       "  'time': '2020-10-30T21:27:21.131Z',\n",
       "  'param': 'Enhance'},\n",
       " {'type': 'startScroll',\n",
       "  'time': '2020-10-30T21:27:21.171Z',\n",
       "  'param': 'Enhance'},\n",
       " {'type': 'stopScroll',\n",
       "  'time': '2020-10-30T21:27:21.465Z',\n",
       "  'param': 'Enhance'},\n",
       " {'type': 'selectCodeCell',\n",
       "  'time': '2020-10-30T21:27:22.506Z',\n",
       "  'code': \"df.intent = ['AverageCost','FundingModel']\\ndf\",\n",
       "  'id': 'e84e29bd0df542e38ab561867fc23876',\n",
       "  'order_count': 10},\n",
       " {'type': 'switchTab', 'time': '2020-10-30T21:27:22.509Z', 'param': 'Filter'},\n",
       " {'type': 'selectCodeCell',\n",
       "  'time': '2020-10-30T21:27:24.152Z',\n",
       "  'code': \"df.intent = ['AverageCost','FundingModel']\\ndf\",\n",
       "  'id': 'e84e29bd0df542e38ab561867fc23876',\n",
       "  'order_count': 10},\n",
       " {'type': 'switchTab', 'time': '2020-10-30T21:27:24.154Z', 'param': 'Filter'},\n",
       " {'type': 'selectCodeCell',\n",
       "  'time': '2020-10-30T21:27:24.970Z',\n",
       "  'code': \"df.intent = ['AverageCost','FundingModel']\\ndf\",\n",
       "  'id': 'e84e29bd0df542e38ab561867fc23876',\n",
       "  'order_count': 10},\n",
       " {'type': 'switchTab',\n",
       "  'time': '2020-10-30T21:27:24.972Z',\n",
       "  'param': 'Generalize'},\n",
       " {'type': 'selectCodeCell',\n",
       "  'time': '2020-10-30T21:27:27.596Z',\n",
       "  'code': \"df.intent = ['AverageCost','FundingModel']\\ndf\",\n",
       "  'id': 'e84e29bd0df542e38ab561867fc23876',\n",
       "  'order_count': 10},\n",
       " {'type': 'switchTab', 'time': '2020-10-30T21:27:27.598Z', 'param': 'Enhance'},\n",
       " {'type': 'selectCodeCell',\n",
       "  'time': '2020-10-30T21:27:30.001Z',\n",
       "  'code': \"df.intent = ['AverageCost','FundingModel']\\ndf\",\n",
       "  'id': 'e84e29bd0df542e38ab561867fc23876',\n",
       "  'order_count': 10},\n",
       " {'type': 'switchTab', 'time': '2020-10-30T21:27:30.003Z', 'param': 'Filter'},\n",
       " {'type': 'selectCodeCell',\n",
       "  'time': '2020-10-30T21:27:32.348Z',\n",
       "  'code': \"df.intent = ['AverageCost','FundingModel']\\ndf\",\n",
       "  'id': 'e84e29bd0df542e38ab561867fc23876',\n",
       "  'order_count': 10},\n",
       " {'type': 'clickVis',\n",
       "  'time': '2020-10-30T21:27:32.350Z',\n",
       "  'param': {'tabTitle': 'Filter',\n",
       "   'index': 0,\n",
       "   'title': 'Region = Southeast',\n",
       "   'mark': 'bar',\n",
       "   'encoding': {'x': {'type': 'quantitative',\n",
       "     'field': 'AverageCost',\n",
       "     'title': 'Mean of AverageCost'},\n",
       "    'y': {'type': 'nominal',\n",
       "     'axis': {'labelOverlap': True},\n",
       "     'field': 'FundingModel'}}}},\n",
       " {'type': 'selectCodeCell',\n",
       "  'time': '2020-10-30T21:27:34.026Z',\n",
       "  'code': \"df.intent = ['AverageCost','FundingModel']\\ndf\",\n",
       "  'id': 'e84e29bd0df542e38ab561867fc23876',\n",
       "  'order_count': 10},\n",
       " {'type': 'unclickVis',\n",
       "  'time': '2020-10-30T21:27:34.029Z',\n",
       "  'param': {'tabTitle': 'Filter',\n",
       "   'index': 0,\n",
       "   'title': 'Region = Southeast',\n",
       "   'mark': 'bar',\n",
       "   'encoding': {'x': {'type': 'quantitative',\n",
       "     'field': 'AverageCost',\n",
       "     'title': 'Mean of AverageCost'},\n",
       "    'y': {'type': 'nominal',\n",
       "     'axis': {'labelOverlap': True},\n",
       "     'field': 'FundingModel'}}}},\n",
       " {'type': 'selectCodeCell',\n",
       "  'time': '2020-10-30T21:27:34.868Z',\n",
       "  'code': \"df.intent = ['AverageCost','FundingModel']\\ndf\",\n",
       "  'id': 'e84e29bd0df542e38ab561867fc23876',\n",
       "  'order_count': 10},\n",
       " {'type': 'clickVis',\n",
       "  'time': '2020-10-30T21:27:34.870Z',\n",
       "  'param': {'tabTitle': 'Filter',\n",
       "   'index': 0,\n",
       "   'title': 'Region = Southeast',\n",
       "   'mark': 'bar',\n",
       "   'encoding': {'x': {'type': 'quantitative',\n",
       "     'field': 'AverageCost',\n",
       "     'title': 'Mean of AverageCost'},\n",
       "    'y': {'type': 'nominal',\n",
       "     'axis': {'labelOverlap': True},\n",
       "     'field': 'FundingModel'}}}},\n",
       " {'type': 'selectCodeCell',\n",
       "  'time': '2020-10-30T21:27:37.640Z',\n",
       "  'code': \"df.intent = ['AverageCost','FundingModel']\\ndf\",\n",
       "  'id': 'e84e29bd0df542e38ab561867fc23876',\n",
       "  'order_count': 10},\n",
       " {'type': 'unclickVis',\n",
       "  'time': '2020-10-30T21:27:37.642Z',\n",
       "  'param': {'tabTitle': 'Filter',\n",
       "   'index': 0,\n",
       "   'title': 'Region = Southeast',\n",
       "   'mark': 'bar',\n",
       "   'encoding': {'x': {'type': 'quantitative',\n",
       "     'field': 'AverageCost',\n",
       "     'title': 'Mean of AverageCost'},\n",
       "    'y': {'type': 'nominal',\n",
       "     'axis': {'labelOverlap': True},\n",
       "     'field': 'FundingModel'}}}},\n",
       " {'type': 'selectCodeCell',\n",
       "  'time': '2020-10-30T21:27:52.665Z',\n",
       "  'code': 'possible_attributes = \"AverageCost|Expenditure|MedianDebt|MedianEarnings\"\\ndf.intent = [possible_attributes,\"FundingModel\"]\\ndf',\n",
       "  'id': '37c71909ecdc4e0c8722023717f82ad3',\n",
       "  'order_count': 12},\n",
       " {'type': 'editCodeCell',\n",
       "  'time': '2020-10-30T21:27:52.673Z',\n",
       "  'code': 'possible_attributes = \"AverageCost|Expenditure|MedianDebt|MedianEarnings\"\\ndf.intent = [possible_attributes,\"FundingModel\"]\\ndf',\n",
       "  'id': '37c71909ecdc4e0c8722023717f82ad3',\n",
       "  'order_count': 12},\n",
       " {'type': 'executeCodeCell',\n",
       "  'time': '2020-10-30T21:28:16.509Z',\n",
       "  'code': 'possible_attributes = \"AverageCost|Expenditure|MedianDebt|MedianEarnings\"\\ndf.intent = [possible_attributes,\"FundingModel\"]\\ndf',\n",
       "  'id': '37c71909ecdc4e0c8722023717f82ad3',\n",
       "  'order_count': 12},\n",
       " {'type': 'selectMarkdownCell',\n",
       "  'time': '2020-10-30T21:28:16.518Z',\n",
       "  'code': 'Alternatively, you could also provide the specification as a list: ',\n",
       "  'id': '77112928c2fd4db68db0189013fb38a7',\n",
       "  'order_count': 13},\n",
       " {'type': 'completeCodeCell',\n",
       "  'time': '2020-10-30T21:28:16.676Z',\n",
       "  'code': 'possible_attributes = \"AverageCost|Expenditure|MedianDebt|MedianEarnings\"\\ndf.intent = [possible_attributes,\"FundingModel\"]\\ndf',\n",
       "  'id': '37c71909ecdc4e0c8722023717f82ad3',\n",
       "  'isPrintPandasDf': False,\n",
       "  'isPrintLuxDf': True,\n",
       "  'isPrintVis': False,\n",
       "  'isPrintVisCollection': False,\n",
       "  'order_count': 12},\n",
       " {'type': 'initWidget', 'time': '2020-10-30T21:28:16.694Z', 'param': ''},\n",
       " {'type': 'initWidget', 'time': '2020-10-30T21:28:16.734Z', 'param': ''},\n",
       " {'type': 'startScroll',\n",
       "  'time': '2020-10-30T21:28:20.379Z',\n",
       "  'param': 'Current Vis'},\n",
       " {'type': 'stopScroll',\n",
       "  'time': '2020-10-30T21:28:20.558Z',\n",
       "  'param': 'Current Vis'},\n",
       " {'type': 'startScroll',\n",
       "  'time': '2020-10-30T21:28:20.596Z',\n",
       "  'param': 'Current Vis'},\n",
       " {'type': 'stopScroll',\n",
       "  'time': '2020-10-30T21:28:20.661Z',\n",
       "  'param': 'Current Vis'},\n",
       " {'type': 'startScroll',\n",
       "  'time': '2020-10-30T21:28:20.680Z',\n",
       "  'param': 'Current Vis'},\n",
       " {'type': 'stopScroll',\n",
       "  'time': '2020-10-30T21:28:20.744Z',\n",
       "  'param': 'Current Vis'},\n",
       " {'type': 'startScroll',\n",
       "  'time': '2020-10-30T21:28:20.780Z',\n",
       "  'param': 'Current Vis'},\n",
       " {'type': 'stopScroll',\n",
       "  'time': '2020-10-30T21:28:20.845Z',\n",
       "  'param': 'Current Vis'},\n",
       " {'type': 'startScroll',\n",
       "  'time': '2020-10-30T21:28:20.912Z',\n",
       "  'param': 'Current Vis'},\n",
       " {'type': 'stopScroll',\n",
       "  'time': '2020-10-30T21:28:20.972Z',\n",
       "  'param': 'Current Vis'},\n",
       " {'type': 'startScroll',\n",
       "  'time': '2020-10-30T21:28:21.212Z',\n",
       "  'param': 'Current Vis'},\n",
       " {'type': 'stopScroll',\n",
       "  'time': '2020-10-30T21:28:21.274Z',\n",
       "  'param': 'Current Vis'},\n",
       " {'type': 'startScroll',\n",
       "  'time': '2020-10-30T21:28:21.296Z',\n",
       "  'param': 'Current Vis'},\n",
       " {'type': 'stopScroll',\n",
       "  'time': '2020-10-30T21:28:21.606Z',\n",
       "  'param': 'Current Vis'},\n",
       " {'type': 'startScroll',\n",
       "  'time': '2020-10-30T21:28:21.645Z',\n",
       "  'param': 'Current Vis'},\n",
       " {'type': 'stopScroll',\n",
       "  'time': '2020-10-30T21:28:22.206Z',\n",
       "  'param': 'Current Vis'},\n",
       " {'type': 'startScroll',\n",
       "  'time': '2020-10-30T21:28:22.602Z',\n",
       "  'param': 'Current Vis'},\n",
       " {'type': 'stopScroll',\n",
       "  'time': '2020-10-30T21:28:22.981Z',\n",
       "  'param': 'Current Vis'},\n",
       " {'type': 'startScroll',\n",
       "  'time': '2020-10-30T21:28:23.036Z',\n",
       "  'param': 'Current Vis'},\n",
       " {'type': 'stopScroll',\n",
       "  'time': '2020-10-30T21:28:23.164Z',\n",
       "  'param': 'Current Vis'},\n",
       " {'type': 'startScroll',\n",
       "  'time': '2020-10-30T21:28:24.153Z',\n",
       "  'param': 'Current Vis'},\n",
       " {'type': 'stopScroll',\n",
       "  'time': '2020-10-30T21:28:24.482Z',\n",
       "  'param': 'Current Vis'},\n",
       " {'type': 'startScroll',\n",
       "  'time': '2020-10-30T21:28:24.669Z',\n",
       "  'param': 'Current Vis'},\n",
       " {'type': 'stopScroll',\n",
       "  'time': '2020-10-30T21:28:24.912Z',\n",
       "  'param': 'Current Vis'},\n",
       " {'type': 'selectCodeCell',\n",
       "  'time': '2020-10-30T21:28:28.069Z',\n",
       "  'code': 'possible_attributes = [\\'AverageCost\\',\\'Expenditure\\',\\'MedianDebt\\',\\'MedianEarnings\\']\\ndf.intent = [possible_attributes,\"FundingModel\"]\\ndf',\n",
       "  'id': 'bc0f26b4fd2b49de8ba49cf58c13f7e2',\n",
       "  'order_count': 14},\n",
       " {'type': 'editCodeCell',\n",
       "  'time': '2020-10-30T21:28:39.767Z',\n",
       "  'code': 'possible_attributes = [\\'AverageCost\\',\\'Expenditure\\',\\'MedianDebt\\',\\'MedianEarnings\\']\\ndf.intent = [possible_attributes,\"FundingModel\"]\\ndf',\n",
       "  'id': 'bc0f26b4fd2b49de8ba49cf58c13f7e2',\n",
       "  'order_count': 14},\n",
       " {'type': 'executeCodeCell',\n",
       "  'time': '2020-10-30T21:28:40.223Z',\n",
       "  'code': 'possible_attributes = [\\'AverageCost\\',\\'Expenditure\\',\\'MedianDebt\\',\\'MedianEarnings\\']\\ndf.intent = [possible_attributes,\"FundingModel\"]\\ndf',\n",
       "  'id': 'bc0f26b4fd2b49de8ba49cf58c13f7e2',\n",
       "  'order_count': 14},\n",
       " {'type': 'selectMarkdownCell',\n",
       "  'time': '2020-10-30T21:28:40.231Z',\n",
       "  'code': '## Specifying values of interest',\n",
       "  'id': 'c9d92caa471b4fbd82c5a8cfc239f8fe',\n",
       "  'order_count': 15},\n",
       " {'type': 'completeCodeCell',\n",
       "  'time': '2020-10-30T21:28:40.389Z',\n",
       "  'code': 'possible_attributes = [\\'AverageCost\\',\\'Expenditure\\',\\'MedianDebt\\',\\'MedianEarnings\\']\\ndf.intent = [possible_attributes,\"FundingModel\"]\\ndf',\n",
       "  'id': 'bc0f26b4fd2b49de8ba49cf58c13f7e2',\n",
       "  'isPrintPandasDf': False,\n",
       "  'isPrintLuxDf': True,\n",
       "  'isPrintVis': False,\n",
       "  'isPrintVisCollection': False,\n",
       "  'order_count': 14},\n",
       " {'type': 'initWidget', 'time': '2020-10-30T21:28:40.410Z', 'param': ''},\n",
       " {'type': 'initWidget', 'time': '2020-10-30T21:28:40.453Z', 'param': ''},\n",
       " {'type': 'selectMarkdownCell',\n",
       "  'time': '2020-10-30T21:28:42.669Z',\n",
       "  'code': 'In Lux, you can also specify particular values corresponding to subsets of the data that you might be interested in. For example, you may be interested in only colleges located in New England. \\n',\n",
       "  'id': '494bde9e0fc548dd9e6d94ee39949d56',\n",
       "  'order_count': 16},\n",
       " {'type': 'selectCodeCell',\n",
       "  'time': '2020-10-30T21:28:49.559Z',\n",
       "  'code': 'df.intent = [\"Region=New England\"]\\ndf',\n",
       "  'id': '4f13e741bc5a4ebf8e52e5bce7abf383',\n",
       "  'order_count': 17},\n",
       " {'type': 'executeCodeCell',\n",
       "  'time': '2020-10-30T21:28:49.882Z',\n",
       "  'code': 'df.intent = [\"Region=New England\"]\\ndf',\n",
       "  'id': '4f13e741bc5a4ebf8e52e5bce7abf383',\n",
       "  'order_count': 17},\n",
       " {'type': 'selectMarkdownCell',\n",
       "  'time': '2020-10-30T21:28:49.888Z',\n",
       "  'code': '### Excercise 1: ',\n",
       "  'id': '8e31298e75ef49f582b1332618160a64',\n",
       "  'order_count': 18},\n",
       " {'type': 'completeCodeCell',\n",
       "  'time': '2020-10-30T21:28:50.734Z',\n",
       "  'code': 'df.intent = [\"Region=New England\"]\\ndf',\n",
       "  'id': '4f13e741bc5a4ebf8e52e5bce7abf383',\n",
       "  'isPrintPandasDf': False,\n",
       "  'isPrintLuxDf': True,\n",
       "  'isPrintVis': False,\n",
       "  'isPrintVisCollection': False,\n",
       "  'order_count': 17},\n",
       " {'type': 'initWidget', 'time': '2020-10-30T21:28:50.758Z', 'param': ''},\n",
       " {'type': 'initWidget', 'time': '2020-10-30T21:28:51.112Z', 'param': ''},\n",
       " {'type': 'selectCodeCell',\n",
       "  'time': '2020-10-30T21:29:11.020Z',\n",
       "  'code': 'df.intent = [\"Region=New England\"]\\ndf',\n",
       "  'id': '4f13e741bc5a4ebf8e52e5bce7abf383',\n",
       "  'order_count': 17},\n",
       " {'type': 'selectCodeCell',\n",
       "  'time': '2020-10-30T21:29:12.766Z',\n",
       "  'code': 'df.intent = [\"Region=New England\"]\\ndf',\n",
       "  'id': '4f13e741bc5a4ebf8e52e5bce7abf383',\n",
       "  'order_count': 17},\n",
       " {'type': 'switchTab',\n",
       "  'time': '2020-10-30T21:29:12.769Z',\n",
       "  'param': 'Distribution'},\n",
       " {'type': 'selectCodeCell',\n",
       "  'time': '2020-10-30T21:29:18.902Z',\n",
       "  'code': 'df.intent = [\"Region=New England\"]\\ndf',\n",
       "  'id': '4f13e741bc5a4ebf8e52e5bce7abf383',\n",
       "  'order_count': 17},\n",
       " {'type': 'switchTab',\n",
       "  'time': '2020-10-30T21:29:18.905Z',\n",
       "  'param': 'Correlation'},\n",
       " {'type': 'selectCodeCell',\n",
       "  'time': '2020-10-30T21:29:22.548Z',\n",
       "  'code': 'df.intent = [\"Region=New England\"]\\ndf',\n",
       "  'id': '4f13e741bc5a4ebf8e52e5bce7abf383',\n",
       "  'order_count': 17},\n",
       " {'type': 'selectCodeCell',\n",
       "  'time': '2020-10-30T21:29:23.648Z',\n",
       "  'code': 'df.intent = [\"Region=New England\"]\\ndf',\n",
       "  'id': '4f13e741bc5a4ebf8e52e5bce7abf383',\n",
       "  'order_count': 17},\n",
       " {'type': 'selectCodeCell',\n",
       "  'time': '2020-10-30T21:29:24.018Z',\n",
       "  'code': 'df.intent = [\"Region=New England\"]\\ndf',\n",
       "  'id': '4f13e741bc5a4ebf8e52e5bce7abf383',\n",
       "  'order_count': 17},\n",
       " {'type': 'selectCodeCell',\n",
       "  'time': '2020-10-30T21:29:25.663Z',\n",
       "  'code': 'df.intent = [\"Region=New England\"]\\ndf',\n",
       "  'id': '4f13e741bc5a4ebf8e52e5bce7abf383',\n",
       "  'order_count': 17},\n",
       " {'type': 'clickVis',\n",
       "  'time': '2020-10-30T21:29:25.666Z',\n",
       "  'param': {'tabTitle': 'Correlation',\n",
       "   'index': 2,\n",
       "   'title': 'Region = New England',\n",
       "   'mark': 'circle',\n",
       "   'encoding': {'x': {'type': 'quantitative',\n",
       "     'field': 'AverageFacultySalary',\n",
       "     'scale': {'domain': [1476, 19862]}},\n",
       "    'y': {'type': 'quantitative',\n",
       "     'field': 'SATAverage',\n",
       "     'scale': {'domain': [666, 1534]}}}}},\n",
       " {'type': 'selectCodeCell',\n",
       "  'time': '2020-10-30T21:29:26.473Z',\n",
       "  'code': 'df.intent = [\"Region=New England\"]\\ndf',\n",
       "  'id': '4f13e741bc5a4ebf8e52e5bce7abf383',\n",
       "  'order_count': 17},\n",
       " {'type': 'startScroll',\n",
       "  'time': '2020-10-30T21:29:26.737Z',\n",
       "  'param': 'Correlation'},\n",
       " {'type': 'stopScroll',\n",
       "  'time': '2020-10-30T21:29:27.084Z',\n",
       "  'param': 'Correlation'},\n",
       " {'type': 'startScroll',\n",
       "  'time': '2020-10-30T21:29:27.337Z',\n",
       "  'param': 'Correlation'},\n",
       " {'type': 'stopScroll',\n",
       "  'time': '2020-10-30T21:29:27.580Z',\n",
       "  'param': 'Correlation'},\n",
       " {'type': 'selectCodeCell',\n",
       "  'time': '2020-10-30T21:29:28.327Z',\n",
       "  'code': 'df.intent = [\"Region=New England\"]\\ndf',\n",
       "  'id': '4f13e741bc5a4ebf8e52e5bce7abf383',\n",
       "  'order_count': 17},\n",
       " {'type': 'unclickVis',\n",
       "  'time': '2020-10-30T21:29:28.329Z',\n",
       "  'param': {'tabTitle': 'Correlation',\n",
       "   'index': 2,\n",
       "   'title': 'Region = New England',\n",
       "   'mark': 'circle',\n",
       "   'encoding': {'x': {'type': 'quantitative',\n",
       "     'field': 'AverageFacultySalary',\n",
       "     'scale': {'domain': [1476, 19862]}},\n",
       "    'y': {'type': 'quantitative',\n",
       "     'field': 'SATAverage',\n",
       "     'scale': {'domain': [666, 1534]}}}}},\n",
       " {'type': 'startScroll',\n",
       "  'time': '2020-10-30T21:29:31.270Z',\n",
       "  'param': 'Correlation'},\n",
       " {'type': 'stopScroll',\n",
       "  'time': '2020-10-30T21:29:31.537Z',\n",
       "  'param': 'Correlation'},\n",
       " {'type': 'selectCodeCell',\n",
       "  'time': '2020-10-30T21:29:37.506Z',\n",
       "  'code': 'df.intent = [\"Region=New England\"]\\ndf',\n",
       "  'id': '4f13e741bc5a4ebf8e52e5bce7abf383',\n",
       "  'order_count': 17},\n",
       " {'type': 'switchTab',\n",
       "  'time': '2020-10-30T21:29:37.509Z',\n",
       "  'param': 'Occurrence'},\n",
       " {'type': 'selectCodeCell',\n",
       "  'time': '2020-10-30T21:29:41.323Z',\n",
       "  'code': 'df.intent = [\"Region=New England\"]\\ndf',\n",
       "  'id': '4f13e741bc5a4ebf8e52e5bce7abf383',\n",
       "  'order_count': 17},\n",
       " {'type': 'switchTab',\n",
       "  'time': '2020-10-30T21:29:41.325Z',\n",
       "  'param': 'Correlation'},\n",
       " {'type': 'selectCodeCell',\n",
       "  'time': '2020-10-30T21:29:48.305Z',\n",
       "  'code': '# Write code to specify the intent here\\n# Hint: Try printing out your dataframe to see if your intent is specified correctly\\n\\n\\n\\n',\n",
       "  'id': 'bd7ba3aab28c4c7f931998398810a72a',\n",
       "  'order_count': 20},\n",
       " {'type': 'editCodeCell',\n",
       "  'time': '2020-10-30T21:29:48.313Z',\n",
       "  'code': '# Write code to specify the intent here\\n# Hint: Try printing out your dataframe to see if your intent is specified correctly\\n\\n\\n\\n',\n",
       "  'id': 'bd7ba3aab28c4c7f931998398810a72a',\n",
       "  'order_count': 20},\n",
       " {'type': 'selectCodeCell',\n",
       "  'time': '2020-10-30T21:30:26.506Z',\n",
       "  'code': 'df.intent = [\"Region=New England\"]\\ndf',\n",
       "  'id': '4f13e741bc5a4ebf8e52e5bce7abf383',\n",
       "  'order_count': 17},\n",
       " {'type': 'editCodeCell',\n",
       "  'time': '2020-10-30T21:30:26.513Z',\n",
       "  'code': 'df.intent = [\"Region=New England\"]\\ndf',\n",
       "  'id': '4f13e741bc5a4ebf8e52e5bce7abf383',\n",
       "  'order_count': 17},\n",
       " {'type': 'selectCodeCell',\n",
       "  'time': '2020-10-30T21:30:27.897Z',\n",
       "  'code': 'df.intent = [\"Region=New England\"]\\ndf',\n",
       "  'id': '4f13e741bc5a4ebf8e52e5bce7abf383',\n",
       "  'order_count': 17},\n",
       " {'type': 'selectCodeCell',\n",
       "  'time': '2020-10-30T21:30:30.466Z',\n",
       "  'code': '# Write code to specify the intent here\\n# Hint: Try printing out your dataframe to see if your intent is specified correctly\\n\\npossible_attributes = \"\"\\n\\n',\n",
       "  'id': 'bd7ba3aab28c4c7f931998398810a72a',\n",
       "  'order_count': 20},\n",
       " {'type': 'editCodeCell',\n",
       "  'time': '2020-10-30T21:30:30.471Z',\n",
       "  'code': '# Write code to specify the intent here\\n# Hint: Try printing out your dataframe to see if your intent is specified correctly\\n\\npossible_attributes = \"\"\\n\\n',\n",
       "  'id': 'bd7ba3aab28c4c7f931998398810a72a',\n",
       "  'order_count': 20},\n",
       " {'type': 'selectMarkdownCell',\n",
       "  'time': '2020-10-30T21:30:34.206Z',\n",
       "  'code': 'You can also specify multiple values of interest using the same `|` notation that we saw earlier. \\n\\nFor example, you might want to compare the `MedianDebt` of students from colleges in `New England`, `Southeast`, and `Far West`.\\nWrite the corresponding code to specify this intent.\\n',\n",
       "  'id': '2a53dff0b5b048c4bd22f28d3dda2e5f',\n",
       "  'order_count': 19},\n",
       " {'type': 'selectMarkdownCell',\n",
       "  'time': '2020-10-30T21:30:34.366Z',\n",
       "  'code': 'You can also specify multiple values of interest using the same `|` notation that we saw earlier. \\n\\nFor example, you might want to compare the `MedianDebt` of students from colleges in `New England`, `Southeast`, and `Far West`.\\nWrite the corresponding code to specify this intent.\\n',\n",
       "  'id': '2a53dff0b5b048c4bd22f28d3dda2e5f',\n",
       "  'order_count': 19},\n",
       " {'type': 'editMarkdownCell',\n",
       "  'time': '2020-10-30T21:30:35.724Z',\n",
       "  'code': 'You can also specify multiple values of interest using the same `|` notation that we saw earlier. \\n\\nFor example, you might want to compare the `MedianDebt` of students from colleges in `New England`, `Southeast`, and `Far West`.\\nWrite the corresponding code to specify this intent.\\n',\n",
       "  'id': '2a53dff0b5b048c4bd22f28d3dda2e5f',\n",
       "  'order_count': 19},\n",
       " {'type': 'selectMarkdownCell',\n",
       "  'time': '2020-10-30T21:30:35.863Z',\n",
       "  'code': 'You can also specify multiple values of interest using the same `|` notation that we saw earlier. \\n\\nFor example, you might want to compare the `MedianDebt` of students from colleges in `New England`, `Southeast`, and `Far West`.\\nWrite the corresponding code to specify this intent.\\n',\n",
       "  'id': '2a53dff0b5b048c4bd22f28d3dda2e5f',\n",
       "  'order_count': 19},\n",
       " {'type': 'selectCodeCell',\n",
       "  'time': '2020-10-30T21:30:36.363Z',\n",
       "  'code': '# Write code to specify the intent here\\n# Hint: Try printing out your dataframe to see if your intent is specified correctly\\n\\ndf.intent = [\"Region=New England\"]\\ndf\\n\\n',\n",
       "  'id': 'bd7ba3aab28c4c7f931998398810a72a',\n",
       "  'order_count': 20},\n",
       " {'type': 'editCodeCell',\n",
       "  'time': '2020-10-30T21:30:40.816Z',\n",
       "  'code': '# Write code to specify the intent here\\n# Hint: Try printing out your dataframe to see if your intent is specified correctly\\n\\ndf.intent = [\"Region=New England\"]\\ndf\\n\\n',\n",
       "  'id': 'bd7ba3aab28c4c7f931998398810a72a',\n",
       "  'order_count': 20},\n",
       " {'type': 'selectMarkdownCell',\n",
       "  'time': '2020-10-30T21:30:46.859Z',\n",
       "  'code': 'You can also specify multiple values of interest using the same `|` notation that we saw earlier. \\n\\nFor example, you might want to compare the `MedianDebt` of students from colleges in `New England`, `Southeast`, and `Far West`.\\nWrite the corresponding code to specify this intent.\\n',\n",
       "  'id': '2a53dff0b5b048c4bd22f28d3dda2e5f',\n",
       "  'order_count': 19},\n",
       " {'type': 'selectMarkdownCell',\n",
       "  'time': '2020-10-30T21:30:47.019Z',\n",
       "  'code': 'You can also specify multiple values of interest using the same `|` notation that we saw earlier. \\n\\nFor example, you might want to compare the `MedianDebt` of students from colleges in `New England`, `Southeast`, and `Far West`.\\nWrite the corresponding code to specify this intent.\\n',\n",
       "  'id': '2a53dff0b5b048c4bd22f28d3dda2e5f',\n",
       "  'order_count': 19},\n",
       " {'type': 'editMarkdownCell',\n",
       "  'time': '2020-10-30T21:30:49.297Z',\n",
       "  'code': 'You can also specify multiple values of interest using the same `|` notation that we saw earlier. \\n\\nFor example, you might want to compare the `MedianDebt` of students from colleges in `New England`, `Southeast`, and `Far West`.\\nWrite the corresponding code to specify this intent.\\n',\n",
       "  'id': '2a53dff0b5b048c4bd22f28d3dda2e5f',\n",
       "  'order_count': 19},\n",
       " {'type': 'selectMarkdownCell',\n",
       "  'time': '2020-10-30T21:30:49.397Z',\n",
       "  'code': 'You can also specify multiple values of interest using the same `|` notation that we saw earlier. \\n\\nFor example, you might want to compare the `MedianDebt` of students from colleges in `New England`, `Southeast`, and `Far West`.\\nWrite the corresponding code to specify this intent.\\n',\n",
       "  'id': '2a53dff0b5b048c4bd22f28d3dda2e5f',\n",
       "  'order_count': 19},\n",
       " {'type': 'selectCodeCell',\n",
       "  'time': '2020-10-30T21:30:49.712Z',\n",
       "  'code': '# Write code to specify the intent here\\n# Hint: Try printing out your dataframe to see if your intent is specified correctly\\n\\ndf.intent = [\"Region=New England|\"]\\ndf\\n\\n',\n",
       "  'id': 'bd7ba3aab28c4c7f931998398810a72a',\n",
       "  'order_count': 20},\n",
       " {'type': 'selectMarkdownCell',\n",
       "  'time': '2020-10-30T21:30:50.757Z',\n",
       "  'code': 'You can also specify multiple values of interest using the same `|` notation that we saw earlier. \\n\\nFor example, you might want to compare the `MedianDebt` of students from colleges in `New England`, `Southeast`, and `Far West`.\\nWrite the corresponding code to specify this intent.\\n',\n",
       "  'id': '2a53dff0b5b048c4bd22f28d3dda2e5f',\n",
       "  'order_count': 19},\n",
       " {'type': 'selectMarkdownCell',\n",
       "  'time': '2020-10-30T21:30:50.974Z',\n",
       "  'code': 'You can also specify multiple values of interest using the same `|` notation that we saw earlier. \\n\\nFor example, you might want to compare the `MedianDebt` of students from colleges in `New England`, `Southeast`, and `Far West`.\\nWrite the corresponding code to specify this intent.\\n',\n",
       "  'id': '2a53dff0b5b048c4bd22f28d3dda2e5f',\n",
       "  'order_count': 19},\n",
       " {'type': 'editMarkdownCell',\n",
       "  'time': '2020-10-30T21:30:52.332Z',\n",
       "  'code': 'You can also specify multiple values of interest using the same `|` notation that we saw earlier. \\n\\nFor example, you might want to compare the `MedianDebt` of students from colleges in `New England`, `Southeast`, and `Far West`.\\nWrite the corresponding code to specify this intent.\\n',\n",
       "  'id': '2a53dff0b5b048c4bd22f28d3dda2e5f',\n",
       "  'order_count': 19},\n",
       " {'type': 'selectMarkdownCell',\n",
       "  'time': '2020-10-30T21:30:53.140Z',\n",
       "  'code': 'You can also specify multiple values of interest using the same `|` notation that we saw earlier. \\n\\nFor example, you might want to compare the `MedianDebt` of students from colleges in `New England`, `Southeast`, and `Far West`.\\nWrite the corresponding code to specify this intent.\\n',\n",
       "  'id': '2a53dff0b5b048c4bd22f28d3dda2e5f',\n",
       "  'order_count': 19},\n",
       " {'type': 'selectCodeCell',\n",
       "  'time': '2020-10-30T21:30:54.728Z',\n",
       "  'code': '# Write code to specify the intent here\\n# Hint: Try printing out your dataframe to see if your intent is specified correctly\\n\\ndf.intent = [\"Region=New England|\"]\\ndf\\n\\n',\n",
       "  'id': 'bd7ba3aab28c4c7f931998398810a72a',\n",
       "  'order_count': 20},\n",
       " {'type': 'editCodeCell',\n",
       "  'time': '2020-10-30T21:30:54.733Z',\n",
       "  'code': '# Write code to specify the intent here\\n# Hint: Try printing out your dataframe to see if your intent is specified correctly\\n\\ndf.intent = [\"Region=New England|\"]\\ndf\\n\\n',\n",
       "  'id': 'bd7ba3aab28c4c7f931998398810a72a',\n",
       "  'order_count': 20},\n",
       " {'type': 'selectMarkdownCell',\n",
       "  'time': '2020-10-30T21:30:58.294Z',\n",
       "  'code': 'You can also specify multiple values of interest using the same `|` notation that we saw earlier. \\n\\nFor example, you might want to compare the `MedianDebt` of students from colleges in `New England`, `Southeast`, and `Far West`.\\nWrite the corresponding code to specify this intent.\\n',\n",
       "  'id': '2a53dff0b5b048c4bd22f28d3dda2e5f',\n",
       "  'order_count': 19},\n",
       " {'type': 'editMarkdownCell',\n",
       "  'time': '2020-10-30T21:30:58.297Z',\n",
       "  'code': 'You can also specify multiple values of interest using the same `|` notation that we saw earlier. \\n\\nFor example, you might want to compare the `MedianDebt` of students from colleges in `New England`, `Southeast`, and `Far West`.\\nWrite the corresponding code to specify this intent.\\n',\n",
       "  'id': '2a53dff0b5b048c4bd22f28d3dda2e5f',\n",
       "  'order_count': 19},\n",
       " {'type': 'selectMarkdownCell',\n",
       "  'time': '2020-10-30T21:30:58.985Z',\n",
       "  'code': 'You can also specify multiple values of interest using the same `|` notation that we saw earlier. \\n\\nFor example, you might want to compare the `MedianDebt` of students from colleges in `New England`, `Southeast`, and `Far West`.\\nWrite the corresponding code to specify this intent.\\n',\n",
       "  'id': '2a53dff0b5b048c4bd22f28d3dda2e5f',\n",
       "  'order_count': 19},\n",
       " {'type': 'selectCodeCell',\n",
       "  'time': '2020-10-30T21:31:01.156Z',\n",
       "  'code': '# Write code to specify the intent here\\n# Hint: Try printing out your dataframe to see if your intent is specified correctly\\n\\ndf.intent = [\"Region=New England|Southeast|\"]\\ndf\\n\\n',\n",
       "  'id': 'bd7ba3aab28c4c7f931998398810a72a',\n",
       "  'order_count': 20},\n",
       " {'type': 'editCodeCell',\n",
       "  'time': '2020-10-30T21:31:01.162Z',\n",
       "  'code': '# Write code to specify the intent here\\n# Hint: Try printing out your dataframe to see if your intent is specified correctly\\n\\ndf.intent = [\"Region=New England|Southeast|\"]\\ndf\\n\\n',\n",
       "  'id': 'bd7ba3aab28c4c7f931998398810a72a',\n",
       "  'order_count': 20},\n",
       " {'type': 'selectMarkdownCell',\n",
       "  'time': '2020-10-30T21:31:01.878Z',\n",
       "  'code': 'You can also specify multiple values of interest using the same `|` notation that we saw earlier. \\n\\nFor example, you might want to compare the `MedianDebt` of students from colleges in `New England`, `Southeast`, and `Far West`.\\nWrite the corresponding code to specify this intent.\\n',\n",
       "  'id': '2a53dff0b5b048c4bd22f28d3dda2e5f',\n",
       "  'order_count': 19},\n",
       " {'type': 'editMarkdownCell',\n",
       "  'time': '2020-10-30T21:31:01.880Z',\n",
       "  'code': 'You can also specify multiple values of interest using the same `|` notation that we saw earlier. \\n\\nFor example, you might want to compare the `MedianDebt` of students from colleges in `New England`, `Southeast`, and `Far West`.\\nWrite the corresponding code to specify this intent.\\n',\n",
       "  'id': '2a53dff0b5b048c4bd22f28d3dda2e5f',\n",
       "  'order_count': 19},\n",
       " {'type': 'selectMarkdownCell',\n",
       "  'time': '2020-10-30T21:31:02.000Z',\n",
       "  'code': 'You can also specify multiple values of interest using the same `|` notation that we saw earlier. \\n\\nFor example, you might want to compare the `MedianDebt` of students from colleges in `New England`, `Southeast`, and `Far West`.\\nWrite the corresponding code to specify this intent.\\n',\n",
       "  'id': '2a53dff0b5b048c4bd22f28d3dda2e5f',\n",
       "  'order_count': 19},\n",
       " {'type': 'selectCodeCell',\n",
       "  'time': '2020-10-30T21:31:02.432Z',\n",
       "  'code': '# Write code to specify the intent here\\n# Hint: Try printing out your dataframe to see if your intent is specified correctly\\n\\ndf.intent = [\"Region=New England|Southeast|Far West\"]\\ndf\\n\\n',\n",
       "  'id': 'bd7ba3aab28c4c7f931998398810a72a',\n",
       "  'order_count': 20},\n",
       " {'type': 'selectCodeCell',\n",
       "  'time': '2020-10-30T21:31:20.433Z',\n",
       "  'code': 'df.intent = [\"Region=New England\"]\\ndf',\n",
       "  'id': '4f13e741bc5a4ebf8e52e5bce7abf383',\n",
       "  'order_count': 17},\n",
       " {'type': 'editCodeCell',\n",
       "  'time': '2020-10-30T21:31:20.441Z',\n",
       "  'code': 'df.intent = [\"Region=New England\"]\\ndf',\n",
       "  'id': '4f13e741bc5a4ebf8e52e5bce7abf383',\n",
       "  'order_count': 17},\n",
       " {'type': 'selectMarkdownCell',\n",
       "  'time': '2020-10-30T21:31:23.363Z',\n",
       "  'code': 'You can also specify multiple values of interest using the same `|` notation that we saw earlier. \\n\\nFor example, you might want to compare the `MedianDebt` of students from colleges in `New England`, `Southeast`, and `Far West`.\\nWrite the corresponding code to specify this intent.\\n',\n",
       "  'id': '2a53dff0b5b048c4bd22f28d3dda2e5f',\n",
       "  'order_count': 19},\n",
       " {'type': 'selectMarkdownCell',\n",
       "  'time': '2020-10-30T21:31:23.781Z',\n",
       "  'code': 'You can also specify multiple values of interest using the same `|` notation that we saw earlier. \\n\\nFor example, you might want to compare the `MedianDebt` of students from colleges in `New England`, `Southeast`, and `Far West`.\\nWrite the corresponding code to specify this intent.\\n',\n",
       "  'id': '2a53dff0b5b048c4bd22f28d3dda2e5f',\n",
       "  'order_count': 19},\n",
       " {'type': 'editMarkdownCell',\n",
       "  'time': '2020-10-30T21:31:24.618Z',\n",
       "  'code': 'You can also specify multiple values of interest using the same `|` notation that we saw earlier. \\n\\nFor example, you might want to compare the `MedianDebt` of students from colleges in `New England`, `Southeast`, and `Far West`.\\nWrite the corresponding code to specify this intent.\\n',\n",
       "  'id': '2a53dff0b5b048c4bd22f28d3dda2e5f',\n",
       "  'order_count': 19},\n",
       " {'type': 'selectMarkdownCell',\n",
       "  'time': '2020-10-30T21:31:24.699Z',\n",
       "  'code': 'You can also specify multiple values of interest using the same `|` notation that we saw earlier. \\n\\nFor example, you might want to compare the `MedianDebt` of students from colleges in `New England`, `Southeast`, and `Far West`.\\nWrite the corresponding code to specify this intent.\\n',\n",
       "  'id': '2a53dff0b5b048c4bd22f28d3dda2e5f',\n",
       "  'order_count': 19},\n",
       " {'type': 'selectMarkdownCell',\n",
       "  'time': '2020-10-30T21:31:24.870Z',\n",
       "  'code': 'You can also specify multiple values of interest using the same `|` notation that we saw earlier. \\n\\nFor example, you might want to compare the `MedianDebt` of students from colleges in `New England`, `Southeast`, and `Far West`.\\nWrite the corresponding code to specify this intent.\\n',\n",
       "  'id': '2a53dff0b5b048c4bd22f28d3dda2e5f',\n",
       "  'order_count': 19},\n",
       " {'type': 'selectMarkdownCell',\n",
       "  'time': '2020-10-30T21:31:26.050Z',\n",
       "  'code': 'You can also specify multiple values of interest using the same `|` notation that we saw earlier. \\n\\nFor example, you might want to compare the `MedianDebt` of students from colleges in `New England`, `Southeast`, and `Far West`.\\nWrite the corresponding code to specify this intent.\\n',\n",
       "  'id': '2a53dff0b5b048c4bd22f28d3dda2e5f',\n",
       "  'order_count': 19},\n",
       " {'type': 'selectCodeCell',\n",
       "  'time': '2020-10-30T21:31:26.542Z',\n",
       "  'code': '# Write code to specify the intent here\\n# Hint: Try printing out your dataframe to see if your intent is specified correctly\\n\\ndf.intent = [\"Region=New England|Southeast|Far West\"]\\ndf\\n\\n',\n",
       "  'id': 'bd7ba3aab28c4c7f931998398810a72a',\n",
       "  'order_count': 20},\n",
       " {'type': 'editCodeCell',\n",
       "  'time': '2020-10-30T21:31:27.947Z',\n",
       "  'code': '# Write code to specify the intent here\\n# Hint: Try printing out your dataframe to see if your intent is specified correctly\\n\\ndf.intent = [\"Region=New England|Southeast|Far West\"]\\ndf\\n\\n',\n",
       "  'id': 'bd7ba3aab28c4c7f931998398810a72a',\n",
       "  'order_count': 20},\n",
       " {'type': 'selectCodeCell',\n",
       "  'time': '2020-10-30T21:31:32.867Z',\n",
       "  'code': '# Write code to specify the intent here\\n# Hint: Try printing out your dataframe to see if your intent is specified correctly\\n\\ndf.intent = [\"Region=New England|Southeast|Far West\", \"MedianDebt\"]\\ndf\\n\\n',\n",
       "  'id': 'bd7ba3aab28c4c7f931998398810a72a',\n",
       "  'order_count': 20},\n",
       " {'type': 'executeCodeCell',\n",
       "  'time': '2020-10-30T21:31:35.123Z',\n",
       "  'code': '# Write code to specify the intent here\\n# Hint: Try printing out your dataframe to see if your intent is specified correctly\\n\\ndf.intent = [\"Region=New England|Southeast|Far West\", \"MedianDebt\"]\\ndf',\n",
       "  'id': 'bd7ba3aab28c4c7f931998398810a72a',\n",
       "  'order_count': 20},\n",
       " {'type': 'selectMarkdownCell',\n",
       "  'time': '2020-10-30T21:31:35.131Z',\n",
       "  'code': 'The Lux supports advanced capabilities in specifying the intent, such as contraining an attribute to display on a specific axes, or modifying the aggregation or binning parameters, through the [lux.Clause](https://lux-api.readthedocs.io/en/latest/source/reference/lux.vis.html#module-lux.vis.Clause) object, which can be thought of as a single *unit* of intent. We will see some example of how lux.Clause is used thorughout the subsequent tutorials, see [this page](https://lux-api.readthedocs.io/en/latest/source/guide/intent.html#advanced-intent-specification-through-lux-clause) for more information.',\n",
       "  'id': 'd43be9b7222649e8845f631a505e24e3',\n",
       "  'order_count': 21},\n",
       " {'type': 'completeCodeCell',\n",
       "  'time': '2020-10-30T21:31:35.262Z',\n",
       "  'code': '# Write code to specify the intent here\\n# Hint: Try printing out your dataframe to see if your intent is specified correctly\\n\\ndf.intent = [\"Region=New England|Southeast|Far West\", \"MedianDebt\"]\\ndf',\n",
       "  'id': 'bd7ba3aab28c4c7f931998398810a72a',\n",
       "  'isPrintPandasDf': False,\n",
       "  'isPrintLuxDf': True,\n",
       "  'isPrintVis': False,\n",
       "  'isPrintVisCollection': False,\n",
       "  'order_count': 20},\n",
       " {'type': 'initWidget', 'time': '2020-10-30T21:31:35.282Z', 'param': ''},\n",
       " {'type': 'initWidget', 'time': '2020-10-30T21:31:35.329Z', 'param': ''},\n",
       " {'type': 'selectCodeCell',\n",
       "  'time': '2020-10-30T21:39:11.452Z',\n",
       "  'code': '# Write code to specify the intent here\\n# Hint: Try printing out your dataframe to see if your intent is specified correctly\\n\\ndf.intent = [\"Region=New England|Southeast|Far West\", \"MedianDebt\"]\\ndf',\n",
       "  'id': 'bd7ba3aab28c4c7f931998398810a72a',\n",
       "  'order_count': 20},\n",
       " {'type': 'editCodeCell',\n",
       "  'time': '2020-10-30T21:39:11.460Z',\n",
       "  'code': '# Write code to specify the intent here\\n# Hint: Try printing out your dataframe to see if your intent is specified correctly\\n\\ndf.intent = [\"Region=New England|Southeast|Far West\", \"MedianDebt\"]\\ndf',\n",
       "  'id': 'bd7ba3aab28c4c7f931998398810a72a',\n",
       "  'order_count': 20},\n",
       " {'type': 'editCodeCell',\n",
       "  'time': '2020-10-30T21:39:34.819Z',\n",
       "  'code': '# Write code to specify the intent here\\n# Hint: Try printing out your dataframe to see if your intent is specified correctly\\n\\ndf.intent = [\"Region=New England|Southeast|Far West\", \"MedianDebt\"]\\ndf',\n",
       "  'id': 'bd7ba3aab28c4c7f931998398810a72a',\n",
       "  'order_count': 20},\n",
       " {'type': 'editCodeCell',\n",
       "  'time': '2020-10-30T21:40:16.709Z',\n",
       "  'code': '# Write code to specify the intent here\\n# Hint: Try printing out your dataframe to see if your intent is specified correctly\\n\\ndf.intent = [\"Region=New England|Southeast|Far West\", \"MedianDebt\"]\\ndf',\n",
       "  'id': 'bd7ba3aab28c4c7f931998398810a72a',\n",
       "  'order_count': 20},\n",
       " {'type': 'editCodeCell',\n",
       "  'time': '2020-10-30T21:40:23.590Z',\n",
       "  'code': '# Write code to specify the intent here\\n# Hint: Try printing out your dataframe to see if your intent is specified correctly\\n\\ndf.intent = [\"Region=New England|Southeast|Far West\", \"MedianDebt\"]\\ndf',\n",
       "  'id': 'bd7ba3aab28c4c7f931998398810a72a',\n",
       "  'order_count': 20},\n",
       " {'type': 'editCodeCell',\n",
       "  'time': '2020-10-30T21:51:51.933Z',\n",
       "  'code': '# Write code to specify the intent here\\n# Hint: Try printing out your dataframe to see if your intent is specified correctly\\n\\ndf.intent = [\"Region=New England|Southeast|Far West\", \"MedianDebt\"]\\ndf',\n",
       "  'id': 'bd7ba3aab28c4c7f931998398810a72a',\n",
       "  'order_count': 20},\n",
       " {'type': 'pageClose', 'time': '2020-10-30T22:28:26.332Z'}]"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['history'][1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_toggles(history):\n",
    "    count = 0\n",
    "    for entry in history:\n",
    "        if 'type' in entry.keys() and entry['type'] == 'initWidget':\n",
    "            count += 1\n",
    "    return count // 2\n",
    "\n",
    "def get_times_until_toggle(history):\n",
    "    times = []\n",
    "    for i in range(len(history)):\n",
    "        if 'type' in history[i].keys() and history[i]['type'] == 'initWidget' and history[i-1]['type'] == 'initWidget':\n",
    "            j=i-2\n",
    "            while j>=0:\n",
    "                if history[j]['type'] != 'completeCodeCell':\n",
    "                    j-=1\n",
    "                elif history[j]['isPrintPandasDf'] != True:\n",
    "                    j-=1\n",
    "                else:\n",
    "                    break\n",
    "            if j != -1:\n",
    "                timeDiff = pd.to_datetime(history[i-1]['time']) - pd.to_datetime(history[j]['time'])\n",
    "                times.append(timeDiff.total_seconds())\n",
    "    return times\n",
    "\n",
    "def get_first_switch_time(history):\n",
    "    times = []\n",
    "    for i in range(len(history)):\n",
    "        if 'type' in history[i].keys() and history[i]['type'] == 'initWidget' and history[i-1]['type'] == 'initWidget':\n",
    "            j=i+1\n",
    "            while j<len(history) and history[j]['type'] != 'switchTab':\n",
    "                j+=1\n",
    "            if j < len(history):\n",
    "                timeDiff = pd.to_datetime(history[j]['time']) - pd.to_datetime(history[i-1]['time'])\n",
    "                times.append(timeDiff.total_seconds())\n",
    "    return times"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "df['initWidgetCount'] = df['history'].apply(get_toggles)\n",
    "df['timeUntilWidgitInit'] = df['history'].apply(get_times_until_toggle)\n",
    "df['timeUntilFirstSwitchTab'] = df['history'].apply(get_first_switch_time)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {
    "scrolled": true
   },
   "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>nbName</th>\n",
       "      <th>history</th>\n",
       "      <th>userGroup</th>\n",
       "      <th>initWidgetCount</th>\n",
       "      <th>timeUntilWidgitInit</th>\n",
       "      <th>timeUntilFirstSwitchTab</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>9ixn8fst1</th>\n",
       "      <td>0-overview.ipynb</td>\n",
       "      <td>[{'type': 'executeCodeCell', 'time': '2020-10-...</td>\n",
       "      <td>RiseCamp</td>\n",
       "      <td>4</td>\n",
       "      <td>[11.329, 15.343, 30.185, 153.001]</td>\n",
       "      <td>[25.172, 21.158, 6.316]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>czs122fic</th>\n",
       "      <td>1-Specify-Intent.ipynb</td>\n",
       "      <td>[{'type': 'executeCodeCell', 'time': '2020-10-...</td>\n",
       "      <td>RiseCamp</td>\n",
       "      <td>8</td>\n",
       "      <td>[]</td>\n",
       "      <td>[18.688, 149.649, 144.711, 25.378, 56.075, 32....</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sg8cq2mmb</th>\n",
       "      <td>2-Quick-Vis.ipynb</td>\n",
       "      <td>[{'type': 'executeCodeCell', 'time': '2020-10-...</td>\n",
       "      <td>RiseCamp</td>\n",
       "      <td>11</td>\n",
       "      <td>[1.907, 2.536]</td>\n",
       "      <td>[]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0qocq99zz</th>\n",
       "      <td>cars_demo.ipynb</td>\n",
       "      <td>[{'type': 'executeCodeCell', 'time': '2020-10-...</td>\n",
       "      <td>RiseCamp</td>\n",
       "      <td>12</td>\n",
       "      <td>[3.743, 5.973, 1.928, 8.235, 10.525, 13.778, 1...</td>\n",
       "      <td>[8.652, 9.394, 13.909, 3.6, 1.31]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>txodzcfin</th>\n",
       "      <td>0-Lux-Overview.ipynb</td>\n",
       "      <td>[{'type': 'executeCodeCell', 'time': '2020-10-...</td>\n",
       "      <td>RiseCamp</td>\n",
       "      <td>3</td>\n",
       "      <td>[2.099, 2.875, 4.029]</td>\n",
       "      <td>[459.798, 239.331, 14.536]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8q8ohu1o0</th>\n",
       "      <td>employee_demo.ipynb</td>\n",
       "      <td>[{'type': 'executeCodeCell', 'time': '2021-02-...</td>\n",
       "      <td>Other</td>\n",
       "      <td>1</td>\n",
       "      <td>[]</td>\n",
       "      <td>[]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>xyvxlzdlc</th>\n",
       "      <td>employee_demo.ipynb</td>\n",
       "      <td>[{'type': 'executeCodeCell', 'time': '2021-02-...</td>\n",
       "      <td>Other</td>\n",
       "      <td>3</td>\n",
       "      <td>[37.6, 52.361, 61.124]</td>\n",
       "      <td>[]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>kgcp5p4gr</th>\n",
       "      <td>employee_demo.ipynb</td>\n",
       "      <td>[{'type': 'executeCodeCell', 'time': '2021-02-...</td>\n",
       "      <td>Other</td>\n",
       "      <td>2</td>\n",
       "      <td>[38.385, 41.265]</td>\n",
       "      <td>[13.907, 11.027]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>yzcb56im4</th>\n",
       "      <td>cars_demo.ipynb</td>\n",
       "      <td>[{'type': 'executeCodeCell', 'time': '2021-02-...</td>\n",
       "      <td>Other</td>\n",
       "      <td>7</td>\n",
       "      <td>[42.267, 63.762, 128.48, 144.077, 2.049]</td>\n",
       "      <td>[113.052, 91.557, 26.839, 11.242]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>upk8v5eo2</th>\n",
       "      <td>5-datetime.ipynb</td>\n",
       "      <td>[{'type': 'executeCodeCell', 'time': '2021-02-...</td>\n",
       "      <td>Other</td>\n",
       "      <td>2</td>\n",
       "      <td>[]</td>\n",
       "      <td>[]</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>477 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                           nbName  \\\n",
       "9ixn8fst1        0-overview.ipynb   \n",
       "czs122fic  1-Specify-Intent.ipynb   \n",
       "sg8cq2mmb       2-Quick-Vis.ipynb   \n",
       "0qocq99zz         cars_demo.ipynb   \n",
       "txodzcfin    0-Lux-Overview.ipynb   \n",
       "...                           ...   \n",
       "8q8ohu1o0     employee_demo.ipynb   \n",
       "xyvxlzdlc     employee_demo.ipynb   \n",
       "kgcp5p4gr     employee_demo.ipynb   \n",
       "yzcb56im4         cars_demo.ipynb   \n",
       "upk8v5eo2        5-datetime.ipynb   \n",
       "\n",
       "                                                     history userGroup  \\\n",
       "9ixn8fst1  [{'type': 'executeCodeCell', 'time': '2020-10-...  RiseCamp   \n",
       "czs122fic  [{'type': 'executeCodeCell', 'time': '2020-10-...  RiseCamp   \n",
       "sg8cq2mmb  [{'type': 'executeCodeCell', 'time': '2020-10-...  RiseCamp   \n",
       "0qocq99zz  [{'type': 'executeCodeCell', 'time': '2020-10-...  RiseCamp   \n",
       "txodzcfin  [{'type': 'executeCodeCell', 'time': '2020-10-...  RiseCamp   \n",
       "...                                                      ...       ...   \n",
       "8q8ohu1o0  [{'type': 'executeCodeCell', 'time': '2021-02-...     Other   \n",
       "xyvxlzdlc  [{'type': 'executeCodeCell', 'time': '2021-02-...     Other   \n",
       "kgcp5p4gr  [{'type': 'executeCodeCell', 'time': '2021-02-...     Other   \n",
       "yzcb56im4  [{'type': 'executeCodeCell', 'time': '2021-02-...     Other   \n",
       "upk8v5eo2  [{'type': 'executeCodeCell', 'time': '2021-02-...     Other   \n",
       "\n",
       "           initWidgetCount                                timeUntilWidgitInit  \\\n",
       "9ixn8fst1                4                  [11.329, 15.343, 30.185, 153.001]   \n",
       "czs122fic                8                                                 []   \n",
       "sg8cq2mmb               11                                     [1.907, 2.536]   \n",
       "0qocq99zz               12  [3.743, 5.973, 1.928, 8.235, 10.525, 13.778, 1...   \n",
       "txodzcfin                3                              [2.099, 2.875, 4.029]   \n",
       "...                    ...                                                ...   \n",
       "8q8ohu1o0                1                                                 []   \n",
       "xyvxlzdlc                3                             [37.6, 52.361, 61.124]   \n",
       "kgcp5p4gr                2                                   [38.385, 41.265]   \n",
       "yzcb56im4                7           [42.267, 63.762, 128.48, 144.077, 2.049]   \n",
       "upk8v5eo2                2                                                 []   \n",
       "\n",
       "                                     timeUntilFirstSwitchTab  \n",
       "9ixn8fst1                            [25.172, 21.158, 6.316]  \n",
       "czs122fic  [18.688, 149.649, 144.711, 25.378, 56.075, 32....  \n",
       "sg8cq2mmb                                                 []  \n",
       "0qocq99zz                  [8.652, 9.394, 13.909, 3.6, 1.31]  \n",
       "txodzcfin                         [459.798, 239.331, 14.536]  \n",
       "...                                                      ...  \n",
       "8q8ohu1o0                                                 []  \n",
       "xyvxlzdlc                                                 []  \n",
       "kgcp5p4gr                                   [13.907, 11.027]  \n",
       "yzcb56im4                  [113.052, 91.557, 26.839, 11.242]  \n",
       "upk8v5eo2                                                 []  \n",
       "\n",
       "[477 rows x 6 columns]"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "time_df = df[['nbName','history','userGroup','initWidgetCount', 'timeUntilWidgitInit', 'timeUntilFirstSwitchTab']]\n",
    "time_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [],
   "source": [
    "time_df.to_csv(\"ToggleAndSwitchTimesCorrected.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "sum(df['initWidgetCount']) / len(df['initWidgetCount'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_avg_non_zero(lst):\n",
    "    count = 0\n",
    "    total = 0\n",
    "    for elem in lst:\n",
    "        if elem != 0:\n",
    "            count += 1\n",
    "            total += elem\n",
    "    return total/count"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "widgitInit = list(np.concatenate(df['timeUntilWidgitInit']).flat)\n",
    "print(sum(widgitInit) / len(widgitInit))\n",
    "print(get_avg_non_zero(widgitInit))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "print(np.percentile(widgitInit, 90))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "switchTimes = list(np.concatenate(df['timeUntilFirstSwitchTab']).flat)\n",
    "print(get_avg_non_zero(switchTimes))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.figure(figsize=(1, 5))\n",
    "plt.boxplot(df['initWidgetCount'])\n",
    "plt.ylabel(\"Number of Widget Inits\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "non_zero = list(filter(lambda x: x!=0, widgitInit))\n",
    "non_zero_truncated = list(filter(lambda x: x < 200, non_zero))\n",
    "plt.figure(figsize=(5, 3))\n",
    "plt.hist(non_zero_truncated)\n",
    "plt.xlabel(\"Time Until Widgit Init\")\n",
    "plt.ylabel(\"Count\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "truncated_widgit_init = list(filter(lambda x: x != 0 and x < 3*np.std(widgitInit), widgitInit))\n",
    "plt.figure(figsize=(1, 5))\n",
    "plt.hist(truncated_widgit_init)\n",
    "plt.ylabel(\"Time Until Init Widget\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.figure(figsize=(5, 3))\n",
    "truncated_switchTimes = list(filter(lambda x: x < 200, switchTimes))\n",
    "plt.hist(truncated_switchTimes)\n",
    "plt.xlabel(\"Time Until First Tab Switch\")\n",
    "plt.ylabel(\"Count\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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