analysis/Log Analysis.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import json\n",
    "nb = json.load(open(\"CarsDemo.ipynb\"))\n",
    "log = nb[\"metadata\"][\"history\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "df = pd.DataFrame(log)\n",
    "df[\"time\"] = pd.to_datetime(df[\"time\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Merge editCodeCell, executeCodeCell and completeCodeCell into one based on id with 2 additional computed interval"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "```\n",
    "queryFormulationTime = T(editCodeCell) - T(executeCodeCell)\n",
    "codeExecutionTime = T(completeCodeCell) - T(executeCodeCell)\n",
    "```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/dorislee/.virtualenvs/lux/lib/python3.7/site-packages/pandas/core/indexing.py:845: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-vis-versus-a-copy\n",
      "  self.obj[key] = _infer_fill_value(value)\n",
      "/Users/dorislee/.virtualenvs/lux/lib/python3.7/site-packages/pandas/core/indexing.py:966: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-vis-versus-a-copy\n",
      "  self.obj[item] = s\n"
     ]
    }
   ],
   "source": [
    "subset1 = df[(df[\"type\"]==\"editCodeCell\")|(df[\"type\"]==\"executeCodeCell\")]\n",
    "subset1.loc[:,\"queryFormulationTime\"] = subset1.groupby([\"id\"])[\"time\"].diff()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "subset2 = df[(df[\"type\"]==\"executeCodeCell\")|(df[\"type\"]==\"completeCodeCell\")]\n",
    "subset2.loc[:,\"codeExecutionTime\"] = subset2.groupby([\"id\"])[\"time\"].diff()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "codeCellsubset=pd.concat([subset1,subset2])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
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       "      <th></th>\n",
       "      <th>time</th>\n",
       "      <th>type</th>\n",
       "      <th>code</th>\n",
       "      <th>id</th>\n",
       "      <th>order_count</th>\n",
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       "      <td>b244c5509aa44ae9a534c03b8ea3accc</td>\n",
       "      <td>13.0</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaT</td>\n",
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       "      <th>160</th>\n",
       "      <td>2020-07-12 07:14:26.526000+00:00</td>\n",
       "      <td>completeCodeCell</td>\n",
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       "      <td>2020-07-12 07:14:43.856000+00:00</td>\n",
       "      <td>executeCodeCell</td>\n",
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       "      <td>b244c5509aa44ae9a534c03b8ea3accc</td>\n",
       "      <td>13.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaT</td>\n",
       "      <td>00:00:17.330000</td>\n",
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       "    <tr>\n",
       "      <th>167</th>\n",
       "      <td>2020-07-12 07:14:45.035000+00:00</td>\n",
       "      <td>completeCodeCell</td>\n",
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      "text/plain": [
       "                                time              type  \\\n",
       "157 2020-07-12 07:14:21.723000+00:00      editCodeCell   \n",
       "158 2020-07-12 07:14:25.344000+00:00   executeCodeCell   \n",
       "164 2020-07-12 07:14:33.868000+00:00      editCodeCell   \n",
       "165 2020-07-12 07:14:43.856000+00:00   executeCodeCell   \n",
       "158 2020-07-12 07:14:25.344000+00:00   executeCodeCell   \n",
       "160 2020-07-12 07:14:26.526000+00:00  completeCodeCell   \n",
       "165 2020-07-12 07:14:43.856000+00:00   executeCodeCell   \n",
       "167 2020-07-12 07:14:45.035000+00:00  completeCodeCell   \n",
       "\n",
       "                                                  code  \\\n",
       "157  df.set_intent([lux.VisSpec(attribute = \"Cylinder...   \n",
       "158  df.set_intent([lux.VisSpec(attribute = \"Cylinder...   \n",
       "164  df.set_intent([lux.VisSpec(attribute = \"Cylinder...   \n",
       "165  df.set_intent([lux.VisSpec(attribute = \"Origin\")...   \n",
       "158  df.set_intent([lux.VisSpec(attribute = \"Cylinder...   \n",
       "160                                                NaN   \n",
       "165  df.set_intent([lux.VisSpec(attribute = \"Origin\")...   \n",
       "167                                                NaN   \n",
       "\n",
       "                                   id  order_count isPrintLuxDf  \\\n",
       "157  b244c5509aa44ae9a534c03b8ea3accc         13.0          NaN   \n",
       "158  b244c5509aa44ae9a534c03b8ea3accc         13.0          NaN   \n",
       "164  b244c5509aa44ae9a534c03b8ea3accc         13.0          NaN   \n",
       "165  b244c5509aa44ae9a534c03b8ea3accc         13.0          NaN   \n",
       "158  b244c5509aa44ae9a534c03b8ea3accc         13.0          NaN   \n",
       "160  b244c5509aa44ae9a534c03b8ea3accc          NaN         True   \n",
       "165  b244c5509aa44ae9a534c03b8ea3accc         13.0          NaN   \n",
       "167  b244c5509aa44ae9a534c03b8ea3accc          NaN         True   \n",
       "\n",
       "    isPrintPandasDf isPrintVis isPrintVisList param  \\\n",
       "157             NaN         NaN                   NaN   NaN   \n",
       "158             NaN         NaN                   NaN   NaN   \n",
       "164             NaN         NaN                   NaN   NaN   \n",
       "165             NaN         NaN                   NaN   NaN   \n",
       "158             NaN         NaN                   NaN   NaN   \n",
       "160           False       False                 False   NaN   \n",
       "165             NaN         NaN                   NaN   NaN   \n",
       "167           False       False                 False   NaN   \n",
       "\n",
       "    queryFormulationTime codeExecutionTime  \n",
       "157                  NaT               NaT  \n",
       "158      00:00:03.621000               NaT  \n",
       "164      00:00:08.524000               NaT  \n",
       "165      00:00:09.988000               NaT  \n",
       "158                  NaT               NaT  \n",
       "160                  NaT   00:00:01.182000  \n",
       "165                  NaT   00:00:17.330000  \n",
       "167                  NaT   00:00:01.179000  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "codeCellsubset[codeCellsubset[\"id\"]==\"b244c5509aa44ae9a534c03b8ea3accc\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "#cellEvents is a dataframe with edit/complete/execute Code Cell all merged as one event, with computed queryFormulationTime and codeExecutionTime\n",
    "cellEvents = codeCellsubset.groupby(['id']).last()\n",
    "# cellEvents = cellEvents.reset_index()\n",
    "# cellEvents.type = \"CodeCellEvent\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>time</th>\n",
       "      <th>type</th>\n",
       "      <th>code</th>\n",
       "      <th>order_count</th>\n",
       "      <th>isPrintLuxDf</th>\n",
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       "    <tr>\n",
       "      <th>id</th>\n",
       "      <th></th>\n",
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       "      <th>935297a48d58491680d5f58b74d00510</th>\n",
       "      <td>2020-07-13 07:11:22.577000+00:00</td>\n",
       "      <td>completeCodeCell</td>\n",
       "      <td>cd ..</td>\n",
       "      <td>0.0</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaT</td>\n",
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       "      <th>51edfcf5d9d6483987dd16f10e9faf0d</th>\n",
       "      <td>2020-07-12 07:10:39.879000+00:00</td>\n",
       "      <td>completeCodeCell</td>\n",
       "      <td>cd ..</td>\n",
       "      <td>0.0</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
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       "      <th>8c51ce7768ca40de88ed278d8b4793ad</th>\n",
       "      <td>2020-07-12 07:10:40.569000+00:00</td>\n",
       "      <td>completeCodeCell</td>\n",
       "      <td>import pandas as pd\\nimport lux</td>\n",
       "      <td>1.0</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>2020-07-13 07:11:23.266000+00:00</td>\n",
       "      <td>completeCodeCell</td>\n",
       "      <td>import pandas as pd\\nimport lux</td>\n",
       "      <td>1.0</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaT</td>\n",
       "      <td>00:00:00.516000</td>\n",
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       "    <tr>\n",
       "      <th>9f3524a071cc488688174f0d949a05ec</th>\n",
       "      <td>2020-07-13 07:11:23.337000+00:00</td>\n",
       "      <td>completeCodeCell</td>\n",
       "      <td>df = pd.read_csv(\"lux/data/car.csv\")\\n# df.set...</td>\n",
       "      <td>3.0</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>2020-07-12 07:10:41.425000+00:00</td>\n",
       "      <td>completeCodeCell</td>\n",
       "      <td>df = pd.read_csv(\"lux/data/car.csv\")\\n# df.set...</td>\n",
       "      <td>3.0</td>\n",
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       "      <td>True</td>\n",
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       "      <td>2020-07-12 07:10:45.350000+00:00</td>\n",
       "      <td>completeCodeCell</td>\n",
       "      <td>df</td>\n",
       "      <td>4.0</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaT</td>\n",
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       "      <td>2020-07-13 07:11:23.338000+00:00</td>\n",
       "      <td>completeCodeCell</td>\n",
       "      <td>df</td>\n",
       "      <td>4.0</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
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       "      <td>2020-07-13 07:11:23.340000+00:00</td>\n",
       "      <td>completeCodeCell</td>\n",
       "      <td>df.set_default_display(\"lux\")</td>\n",
       "      <td>5.0</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaT</td>\n",
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       "      <td>2020-07-12 07:11:17.782000+00:00</td>\n",
       "      <td>completeCodeCell</td>\n",
       "      <td>df.set_default_display(\"lux\")</td>\n",
       "      <td>5.0</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>00:00:01.343000</td>\n",
       "      <td>00:00:00.030000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>d974d331407b4136b3a33a52a2b653da</th>\n",
       "      <td>2020-07-13 07:11:20.463000+00:00</td>\n",
       "      <td>completeCodeCell</td>\n",
       "      <td>df</td>\n",
       "      <td>6.0</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaT</td>\n",
       "      <td>00:00:00.086000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cb87e650a7924a61a8f287c77c7235ea</th>\n",
       "      <td>2020-07-12 07:11:18.770000+00:00</td>\n",
       "      <td>completeCodeCell</td>\n",
       "      <td>df</td>\n",
       "      <td>6.0</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaT</td>\n",
       "      <td>00:00:00.425000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31f9084286dc4b78bb7cbcd643cbc9ce</th>\n",
       "      <td>2020-07-12 07:11:48.721000+00:00</td>\n",
       "      <td>completeCodeCell</td>\n",
       "      <td>df.set_intent([lux.VisSpec(attribute = \"Accelera...</td>\n",
       "      <td>8.0</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>00:00:00.871000</td>\n",
       "      <td>00:00:00.765000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4c83108ca0ba4d0fa5487fa81972a523</th>\n",
       "      <td>2020-07-12 07:13:08.233000+00:00</td>\n",
       "      <td>completeCodeCell</td>\n",
       "      <td>df.get_exported()</td>\n",
       "      <td>9.0</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>00:00:40.593000</td>\n",
       "      <td>00:00:00.063000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4897af9f1cb14322884d0e1a77487507</th>\n",
       "      <td>2020-07-12 07:13:56.972000+00:00</td>\n",
       "      <td>completeCodeCell</td>\n",
       "      <td>vis = df.get_exported()[0]\\nvis</td>\n",
       "      <td>10.0</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>00:00:04.833000</td>\n",
       "      <td>00:00:00.063000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>b244c5509aa44ae9a534c03b8ea3accc</th>\n",
       "      <td>2020-07-12 07:14:45.035000+00:00</td>\n",
       "      <td>completeCodeCell</td>\n",
       "      <td>df.set_intent([lux.VisSpec(attribute = \"Origin\")...</td>\n",
       "      <td>13.0</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>00:00:09.988000</td>\n",
       "      <td>00:00:01.179000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9f8b16b0e6f64f488b17857edeb6a030</th>\n",
       "      <td>2020-07-12 07:15:23.585000+00:00</td>\n",
       "      <td>completeCodeCell</td>\n",
       "      <td>df.get_exported()</td>\n",
       "      <td>14.0</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>00:00:01.266000</td>\n",
       "      <td>00:00:00.047000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                             time  \\\n",
       "id                                                                  \n",
       "935297a48d58491680d5f58b74d00510 2020-07-13 07:11:22.577000+00:00   \n",
       "51edfcf5d9d6483987dd16f10e9faf0d 2020-07-12 07:10:39.879000+00:00   \n",
       "8c51ce7768ca40de88ed278d8b4793ad 2020-07-12 07:10:40.569000+00:00   \n",
       "6e966b315ac04d60a96aef44ca9450bd 2020-07-13 07:11:23.266000+00:00   \n",
       "9f3524a071cc488688174f0d949a05ec 2020-07-13 07:11:23.337000+00:00   \n",
       "ddee4d330fab46958695f2508f27dff6 2020-07-12 07:10:41.425000+00:00   \n",
       "6828c0270b5d4bcf8853677b8ae74f7d 2020-07-12 07:10:45.350000+00:00   \n",
       "0a3a1fbeea67448d8dda5fc0108f402e 2020-07-13 07:11:23.338000+00:00   \n",
       "7bd4cbf923a249e5b2ef35a9b90901e2 2020-07-13 07:11:23.340000+00:00   \n",
       "93ebe0e18ef74efc9a90755baab47e2a 2020-07-12 07:11:17.782000+00:00   \n",
       "d974d331407b4136b3a33a52a2b653da 2020-07-13 07:11:20.463000+00:00   \n",
       "cb87e650a7924a61a8f287c77c7235ea 2020-07-12 07:11:18.770000+00:00   \n",
       "31f9084286dc4b78bb7cbcd643cbc9ce 2020-07-12 07:11:48.721000+00:00   \n",
       "4c83108ca0ba4d0fa5487fa81972a523 2020-07-12 07:13:08.233000+00:00   \n",
       "4897af9f1cb14322884d0e1a77487507 2020-07-12 07:13:56.972000+00:00   \n",
       "b244c5509aa44ae9a534c03b8ea3accc 2020-07-12 07:14:45.035000+00:00   \n",
       "9f8b16b0e6f64f488b17857edeb6a030 2020-07-12 07:15:23.585000+00:00   \n",
       "\n",
       "                                              type  \\\n",
       "id                                                   \n",
       "935297a48d58491680d5f58b74d00510  completeCodeCell   \n",
       "51edfcf5d9d6483987dd16f10e9faf0d  completeCodeCell   \n",
       "8c51ce7768ca40de88ed278d8b4793ad  completeCodeCell   \n",
       "6e966b315ac04d60a96aef44ca9450bd  completeCodeCell   \n",
       "9f3524a071cc488688174f0d949a05ec  completeCodeCell   \n",
       "ddee4d330fab46958695f2508f27dff6  completeCodeCell   \n",
       "6828c0270b5d4bcf8853677b8ae74f7d  completeCodeCell   \n",
       "0a3a1fbeea67448d8dda5fc0108f402e  completeCodeCell   \n",
       "7bd4cbf923a249e5b2ef35a9b90901e2  completeCodeCell   \n",
       "93ebe0e18ef74efc9a90755baab47e2a  completeCodeCell   \n",
       "d974d331407b4136b3a33a52a2b653da  completeCodeCell   \n",
       "cb87e650a7924a61a8f287c77c7235ea  completeCodeCell   \n",
       "31f9084286dc4b78bb7cbcd643cbc9ce  completeCodeCell   \n",
       "4c83108ca0ba4d0fa5487fa81972a523  completeCodeCell   \n",
       "4897af9f1cb14322884d0e1a77487507  completeCodeCell   \n",
       "b244c5509aa44ae9a534c03b8ea3accc  completeCodeCell   \n",
       "9f8b16b0e6f64f488b17857edeb6a030  completeCodeCell   \n",
       "\n",
       "                                                                               code  \\\n",
       "id                                                                                    \n",
       "935297a48d58491680d5f58b74d00510                                              cd ..   \n",
       "51edfcf5d9d6483987dd16f10e9faf0d                                              cd ..   \n",
       "8c51ce7768ca40de88ed278d8b4793ad                    import pandas as pd\\nimport lux   \n",
       "6e966b315ac04d60a96aef44ca9450bd                    import pandas as pd\\nimport lux   \n",
       "9f3524a071cc488688174f0d949a05ec  df = pd.read_csv(\"lux/data/car.csv\")\\n# df.set...   \n",
       "ddee4d330fab46958695f2508f27dff6  df = pd.read_csv(\"lux/data/car.csv\")\\n# df.set...   \n",
       "6828c0270b5d4bcf8853677b8ae74f7d                                                 df   \n",
       "0a3a1fbeea67448d8dda5fc0108f402e                                                 df   \n",
       "7bd4cbf923a249e5b2ef35a9b90901e2                      df.set_default_display(\"lux\")   \n",
       "93ebe0e18ef74efc9a90755baab47e2a                      df.set_default_display(\"lux\")   \n",
       "d974d331407b4136b3a33a52a2b653da                                                 df   \n",
       "cb87e650a7924a61a8f287c77c7235ea                                                 df   \n",
       "31f9084286dc4b78bb7cbcd643cbc9ce  df.set_intent([lux.VisSpec(attribute = \"Accelera...   \n",
       "4c83108ca0ba4d0fa5487fa81972a523                                  df.get_exported()   \n",
       "4897af9f1cb14322884d0e1a77487507                 vis = df.get_exported()[0]\\nvis    \n",
       "b244c5509aa44ae9a534c03b8ea3accc  df.set_intent([lux.VisSpec(attribute = \"Origin\")...   \n",
       "9f8b16b0e6f64f488b17857edeb6a030                                  df.get_exported()   \n",
       "\n",
       "                                  order_count  isPrintLuxDf  isPrintPandasDf  \\\n",
       "id                                                                             \n",
       "935297a48d58491680d5f58b74d00510          0.0         False            False   \n",
       "51edfcf5d9d6483987dd16f10e9faf0d          0.0         False            False   \n",
       "8c51ce7768ca40de88ed278d8b4793ad          1.0         False            False   \n",
       "6e966b315ac04d60a96aef44ca9450bd          1.0         False            False   \n",
       "9f3524a071cc488688174f0d949a05ec          3.0         False            False   \n",
       "ddee4d330fab46958695f2508f27dff6          3.0         False             True   \n",
       "6828c0270b5d4bcf8853677b8ae74f7d          4.0         False             True   \n",
       "0a3a1fbeea67448d8dda5fc0108f402e          4.0         False            False   \n",
       "7bd4cbf923a249e5b2ef35a9b90901e2          5.0         False            False   \n",
       "93ebe0e18ef74efc9a90755baab47e2a          5.0         False            False   \n",
       "d974d331407b4136b3a33a52a2b653da          6.0         False            False   \n",
       "cb87e650a7924a61a8f287c77c7235ea          6.0          True            False   \n",
       "31f9084286dc4b78bb7cbcd643cbc9ce          8.0          True            False   \n",
       "4c83108ca0ba4d0fa5487fa81972a523          9.0         False            False   \n",
       "4897af9f1cb14322884d0e1a77487507         10.0         False            False   \n",
       "b244c5509aa44ae9a534c03b8ea3accc         13.0          True            False   \n",
       "9f8b16b0e6f64f488b17857edeb6a030         14.0         False            False   \n",
       "\n",
       "                                  isPrintVis  isPrintVisList  param  \\\n",
       "id                                                                            \n",
       "935297a48d58491680d5f58b74d00510        False                  False    NaN   \n",
       "51edfcf5d9d6483987dd16f10e9faf0d        False                  False    NaN   \n",
       "8c51ce7768ca40de88ed278d8b4793ad        False                  False    NaN   \n",
       "6e966b315ac04d60a96aef44ca9450bd        False                  False    NaN   \n",
       "9f3524a071cc488688174f0d949a05ec        False                  False    NaN   \n",
       "ddee4d330fab46958695f2508f27dff6        False                  False    NaN   \n",
       "6828c0270b5d4bcf8853677b8ae74f7d        False                  False    NaN   \n",
       "0a3a1fbeea67448d8dda5fc0108f402e        False                  False    NaN   \n",
       "7bd4cbf923a249e5b2ef35a9b90901e2        False                  False    NaN   \n",
       "93ebe0e18ef74efc9a90755baab47e2a        False                  False    NaN   \n",
       "d974d331407b4136b3a33a52a2b653da        False                  False    NaN   \n",
       "cb87e650a7924a61a8f287c77c7235ea        False                  False    NaN   \n",
       "31f9084286dc4b78bb7cbcd643cbc9ce        False                  False    NaN   \n",
       "4c83108ca0ba4d0fa5487fa81972a523         True                  False    NaN   \n",
       "4897af9f1cb14322884d0e1a77487507         True                  False    NaN   \n",
       "b244c5509aa44ae9a534c03b8ea3accc        False                  False    NaN   \n",
       "9f8b16b0e6f64f488b17857edeb6a030         True                  False    NaN   \n",
       "\n",
       "                                 queryFormulationTime codeExecutionTime  \n",
       "id                                                                       \n",
       "935297a48d58491680d5f58b74d00510                  NaT   00:00:00.020000  \n",
       "51edfcf5d9d6483987dd16f10e9faf0d      00:00:00.922000   00:00:00.028000  \n",
       "8c51ce7768ca40de88ed278d8b4793ad                  NaT   00:00:00.313000  \n",
       "6e966b315ac04d60a96aef44ca9450bd                  NaT   00:00:00.516000  \n",
       "9f3524a071cc488688174f0d949a05ec                  NaT   00:00:00.324000  \n",
       "ddee4d330fab46958695f2508f27dff6                  NaT   00:00:00.057000  \n",
       "6828c0270b5d4bcf8853677b8ae74f7d                  NaT   00:00:00.702000  \n",
       "0a3a1fbeea67448d8dda5fc0108f402e                  NaT   00:00:00.184000  \n",
       "7bd4cbf923a249e5b2ef35a9b90901e2                  NaT   00:00:00.027000  \n",
       "93ebe0e18ef74efc9a90755baab47e2a      00:00:01.343000   00:00:00.030000  \n",
       "d974d331407b4136b3a33a52a2b653da                  NaT   00:00:00.086000  \n",
       "cb87e650a7924a61a8f287c77c7235ea                  NaT   00:00:00.425000  \n",
       "31f9084286dc4b78bb7cbcd643cbc9ce      00:00:00.871000   00:00:00.765000  \n",
       "4c83108ca0ba4d0fa5487fa81972a523      00:00:40.593000   00:00:00.063000  \n",
       "4897af9f1cb14322884d0e1a77487507      00:00:04.833000   00:00:00.063000  \n",
       "b244c5509aa44ae9a534c03b8ea3accc      00:00:09.988000   00:00:01.179000  \n",
       "9f8b16b0e6f64f488b17857edeb6a030      00:00:01.266000   00:00:00.047000  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cellEvents.sort_values('order_count')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total number of dataframe prints: 2\n",
      "Total number of dataframe prints: 3\n"
     ]
    }
   ],
   "source": [
    "print (\"Total number of dataframe prints:\", sum(cellEvents[\"isPrintPandasDf\"]))\n",
    "print (\"Total number of dataframe prints:\", sum(cellEvents[\"isPrintLuxDf\"]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Use `selectCodeCell` events to assign `id` and `order_count` to UI events"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "UIevents = ['initWidget','switchTab', 'startScroll', 'stopScroll', \n",
    "       'toggleBtnClick', 'clickVis', 'exportBtnClick']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "#fill forwards based on the values in the most recent `selectCodeCell` to infer the order_count and other information about which cell is interacted upon\n",
    "uiEventDf = df[df.type.isin(UIevents+['selectCodeCell'])].ffill() "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "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>time</th>\n",
       "      <th>type</th>\n",
       "      <th>code</th>\n",
       "      <th>id</th>\n",
       "      <th>order_count</th>\n",
       "      <th>isPrintLuxDf</th>\n",
       "      <th>isPrintPandasDf</th>\n",
       "      <th>isPrintVis</th>\n",
       "      <th>isPrintVisList</th>\n",
       "      <th>param</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2020-07-12 07:10:32.875000+00:00</td>\n",
       "      <td>selectCodeCell</td>\n",
       "      <td>cd ..</td>\n",
       "      <td>51edfcf5d9d6483987dd16f10e9faf0d</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2020-07-12 07:10:33.435000+00:00</td>\n",
       "      <td>selectCodeCell</td>\n",
       "      <td>cd ..</td>\n",
       "      <td>51edfcf5d9d6483987dd16f10e9faf0d</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2020-07-12 07:10:39.267000+00:00</td>\n",
       "      <td>selectCodeCell</td>\n",
       "      <td>cd ..</td>\n",
       "      <td>51edfcf5d9d6483987dd16f10e9faf0d</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>2020-07-12 07:10:39.872000+00:00</td>\n",
       "      <td>selectCodeCell</td>\n",
       "      <td>import pandas as pd\\nimport lux</td>\n",
       "      <td>8c51ce7768ca40de88ed278d8b4793ad</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>2020-07-12 07:10:40.877000+00:00</td>\n",
       "      <td>selectCodeCell</td>\n",
       "      <td>df = pd.read_csv(\"lux/data/car.csv\")\\n# df.set...</td>\n",
       "      <td>ddee4d330fab46958695f2508f27dff6</td>\n",
       "      <td>3.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>191</th>\n",
       "      <td>2020-07-13 07:11:22.569000+00:00</td>\n",
       "      <td>selectCodeCell</td>\n",
       "      <td>import pandas as pd\\nimport lux</td>\n",
       "      <td>6e966b315ac04d60a96aef44ca9450bd</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>195</th>\n",
       "      <td>2020-07-13 07:11:22.901000+00:00</td>\n",
       "      <td>selectCodeCell</td>\n",
       "      <td>df = pd.read_csv(\"lux/data/car.csv\")\\n# df.set...</td>\n",
       "      <td>9f3524a071cc488688174f0d949a05ec</td>\n",
       "      <td>3.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>197</th>\n",
       "      <td>2020-07-13 07:11:23.024000+00:00</td>\n",
       "      <td>selectCodeCell</td>\n",
       "      <td>df</td>\n",
       "      <td>0a3a1fbeea67448d8dda5fc0108f402e</td>\n",
       "      <td>4.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>199</th>\n",
       "      <td>2020-07-13 07:11:23.165000+00:00</td>\n",
       "      <td>selectCodeCell</td>\n",
       "      <td>df.set_default_display(\"lux\")</td>\n",
       "      <td>7bd4cbf923a249e5b2ef35a9b90901e2</td>\n",
       "      <td>5.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>202</th>\n",
       "      <td>2020-07-13 07:11:23.322000+00:00</td>\n",
       "      <td>selectCodeCell</td>\n",
       "      <td>df</td>\n",
       "      <td>d974d331407b4136b3a33a52a2b653da</td>\n",
       "      <td>6.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>141 rows × 10 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                time            type  \\\n",
       "2   2020-07-12 07:10:32.875000+00:00  selectCodeCell   \n",
       "6   2020-07-12 07:10:33.435000+00:00  selectCodeCell   \n",
       "8   2020-07-12 07:10:39.267000+00:00  selectCodeCell   \n",
       "10  2020-07-12 07:10:39.872000+00:00  selectCodeCell   \n",
       "15  2020-07-12 07:10:40.877000+00:00  selectCodeCell   \n",
       "..                               ...             ...   \n",
       "191 2020-07-13 07:11:22.569000+00:00  selectCodeCell   \n",
       "195 2020-07-13 07:11:22.901000+00:00  selectCodeCell   \n",
       "197 2020-07-13 07:11:23.024000+00:00  selectCodeCell   \n",
       "199 2020-07-13 07:11:23.165000+00:00  selectCodeCell   \n",
       "202 2020-07-13 07:11:23.322000+00:00  selectCodeCell   \n",
       "\n",
       "                                                  code  \\\n",
       "2                                                cd ..   \n",
       "6                                                cd ..   \n",
       "8                                                cd ..   \n",
       "10                     import pandas as pd\\nimport lux   \n",
       "15   df = pd.read_csv(\"lux/data/car.csv\")\\n# df.set...   \n",
       "..                                                 ...   \n",
       "191                    import pandas as pd\\nimport lux   \n",
       "195  df = pd.read_csv(\"lux/data/car.csv\")\\n# df.set...   \n",
       "197                                                 df   \n",
       "199                      df.set_default_display(\"lux\")   \n",
       "202                                                 df   \n",
       "\n",
       "                                   id  order_count  isPrintLuxDf  \\\n",
       "2    51edfcf5d9d6483987dd16f10e9faf0d          0.0           NaN   \n",
       "6    51edfcf5d9d6483987dd16f10e9faf0d          0.0           NaN   \n",
       "8    51edfcf5d9d6483987dd16f10e9faf0d          0.0           NaN   \n",
       "10   8c51ce7768ca40de88ed278d8b4793ad          1.0           NaN   \n",
       "15   ddee4d330fab46958695f2508f27dff6          3.0           NaN   \n",
       "..                                ...          ...           ...   \n",
       "191  6e966b315ac04d60a96aef44ca9450bd          1.0           NaN   \n",
       "195  9f3524a071cc488688174f0d949a05ec          3.0           NaN   \n",
       "197  0a3a1fbeea67448d8dda5fc0108f402e          4.0           NaN   \n",
       "199  7bd4cbf923a249e5b2ef35a9b90901e2          5.0           NaN   \n",
       "202  d974d331407b4136b3a33a52a2b653da          6.0           NaN   \n",
       "\n",
       "     isPrintPandasDf  isPrintVis  isPrintVisList param  \n",
       "2                NaN          NaN                    NaN   NaN  \n",
       "6                NaN          NaN                    NaN   NaN  \n",
       "8                NaN          NaN                    NaN   NaN  \n",
       "10               NaN          NaN                    NaN   NaN  \n",
       "15               NaN          NaN                    NaN   NaN  \n",
       "..               ...          ...                    ...   ...  \n",
       "191              NaN          NaN                    NaN        \n",
       "195              NaN          NaN                    NaN        \n",
       "197              NaN          NaN                    NaN        \n",
       "199              NaN          NaN                    NaN        \n",
       "202              NaN          NaN                    NaN        \n",
       "\n",
       "[141 rows x 10 columns]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "uiEventDf"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Number of Times Lux Widget is initialized"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "init = uiEventDf[uiEventDf[\"type\"]=='initWidget']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "def filter_df_by_delta(df, timeDelta):\n",
    "    return df[df.time.diff()<timeDelta]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "init =  filter_df_by_delta(init,\"00:00:00.5\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>time</th>\n",
       "      <th>type</th>\n",
       "      <th>code</th>\n",
       "      <th>id</th>\n",
       "      <th>order_count</th>\n",
       "      <th>isPrintLuxDf</th>\n",
       "      <th>isPrintPandasDf</th>\n",
       "      <th>isPrintVis</th>\n",
       "      <th>isPrintVisList</th>\n",
       "      <th>param</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>2020-07-12 07:10:47.884000+00:00</td>\n",
       "      <td>initWidget</td>\n",
       "      <td>df</td>\n",
       "      <td>6828c0270b5d4bcf8853677b8ae74f7d</td>\n",
       "      <td>4.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>82</th>\n",
       "      <td>2020-07-12 07:11:18.936000+00:00</td>\n",
       "      <td>initWidget</td>\n",
       "      <td>df</td>\n",
       "      <td>cb87e650a7924a61a8f287c77c7235ea</td>\n",
       "      <td>6.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>92</th>\n",
       "      <td>2020-07-12 07:11:49.042000+00:00</td>\n",
       "      <td>initWidget</td>\n",
       "      <td>df.set_intent([lux.VisSpec(attribute = \"Accelera...</td>\n",
       "      <td>31f9084286dc4b78bb7cbcd643cbc9ce</td>\n",
       "      <td>8.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>128</th>\n",
       "      <td>2020-07-12 07:12:38.157000+00:00</td>\n",
       "      <td>initWidget</td>\n",
       "      <td>df.set_intent([lux.VisSpec(attribute = \"Accelera...</td>\n",
       "      <td>31f9084286dc4b78bb7cbcd643cbc9ce</td>\n",
       "      <td>8.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>162</th>\n",
       "      <td>2020-07-12 07:14:26.684000+00:00</td>\n",
       "      <td>initWidget</td>\n",
       "      <td>df.get_exported()</td>\n",
       "      <td>9f8b16b0e6f64f488b17857edeb6a030</td>\n",
       "      <td>14.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>169</th>\n",
       "      <td>2020-07-12 07:14:45.155000+00:00</td>\n",
       "      <td>initWidget</td>\n",
       "      <td>df.get_exported()</td>\n",
       "      <td>9f8b16b0e6f64f488b17857edeb6a030</td>\n",
       "      <td>14.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                time        type  \\\n",
       "24  2020-07-12 07:10:47.884000+00:00  initWidget   \n",
       "82  2020-07-12 07:11:18.936000+00:00  initWidget   \n",
       "92  2020-07-12 07:11:49.042000+00:00  initWidget   \n",
       "128 2020-07-12 07:12:38.157000+00:00  initWidget   \n",
       "162 2020-07-12 07:14:26.684000+00:00  initWidget   \n",
       "169 2020-07-12 07:14:45.155000+00:00  initWidget   \n",
       "\n",
       "                                                  code  \\\n",
       "24                                                  df   \n",
       "82                                                  df   \n",
       "92   df.set_intent([lux.VisSpec(attribute = \"Accelera...   \n",
       "128  df.set_intent([lux.VisSpec(attribute = \"Accelera...   \n",
       "162                                  df.get_exported()   \n",
       "169                                  df.get_exported()   \n",
       "\n",
       "                                   id  order_count  isPrintLuxDf  \\\n",
       "24   6828c0270b5d4bcf8853677b8ae74f7d          4.0           NaN   \n",
       "82   cb87e650a7924a61a8f287c77c7235ea          6.0           NaN   \n",
       "92   31f9084286dc4b78bb7cbcd643cbc9ce          8.0           NaN   \n",
       "128  31f9084286dc4b78bb7cbcd643cbc9ce          8.0           NaN   \n",
       "162  9f8b16b0e6f64f488b17857edeb6a030         14.0           NaN   \n",
       "169  9f8b16b0e6f64f488b17857edeb6a030         14.0           NaN   \n",
       "\n",
       "     isPrintPandasDf  isPrintVis  isPrintVisList param  \n",
       "24               NaN          NaN                    NaN        \n",
       "82               NaN          NaN                    NaN        \n",
       "92               NaN          NaN                    NaN        \n",
       "128              NaN          NaN                    NaN        \n",
       "162              NaN          NaN                    NaN        \n",
       "169              NaN          NaN                    NaN        "
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "init"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Number of Times Toggle to Lux v.s. to Pandas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "toggle = uiEventDf[uiEventDf[\"type\"]=='toggleBtnClick']\n",
    "toggle = filter_df_by_delta(toggle,\"00:00:00.01\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>time</th>\n",
       "      <th>type</th>\n",
       "      <th>code</th>\n",
       "      <th>id</th>\n",
       "      <th>order_count</th>\n",
       "      <th>isPrintLuxDf</th>\n",
       "      <th>isPrintPandasDf</th>\n",
       "      <th>isPrintVis</th>\n",
       "      <th>isPrintVisList</th>\n",
       "      <th>param</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>122</th>\n",
       "      <td>2020-07-12 07:12:33.259000+00:00</td>\n",
       "      <td>toggleBtnClick</td>\n",
       "      <td>df.set_intent([lux.VisSpec(attribute = \"Accelera...</td>\n",
       "      <td>31f9084286dc4b78bb7cbcd643cbc9ce</td>\n",
       "      <td>8.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>pandas</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>125</th>\n",
       "      <td>2020-07-12 07:12:37.883000+00:00</td>\n",
       "      <td>toggleBtnClick</td>\n",
       "      <td>df.set_intent([lux.VisSpec(attribute = \"Accelera...</td>\n",
       "      <td>31f9084286dc4b78bb7cbcd643cbc9ce</td>\n",
       "      <td>8.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>lux</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                time            type  \\\n",
       "122 2020-07-12 07:12:33.259000+00:00  toggleBtnClick   \n",
       "125 2020-07-12 07:12:37.883000+00:00  toggleBtnClick   \n",
       "\n",
       "                                                  code  \\\n",
       "122  df.set_intent([lux.VisSpec(attribute = \"Accelera...   \n",
       "125  df.set_intent([lux.VisSpec(attribute = \"Accelera...   \n",
       "\n",
       "                                   id  order_count  isPrintLuxDf  \\\n",
       "122  31f9084286dc4b78bb7cbcd643cbc9ce          8.0           NaN   \n",
       "125  31f9084286dc4b78bb7cbcd643cbc9ce          8.0           NaN   \n",
       "\n",
       "     isPrintPandasDf  isPrintVis  isPrintVisList   param  \n",
       "122              NaN          NaN                    NaN  pandas  \n",
       "125              NaN          NaN                    NaN     lux  "
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "toggle"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "122    pandas\n",
      "125       lux\n",
      "Name: param, dtype: object\n"
     ]
    }
   ],
   "source": [
    "print (toggle.param)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Interactions with Tabs and Scroll"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "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>time</th>\n",
       "      <th>type</th>\n",
       "      <th>code</th>\n",
       "      <th>id</th>\n",
       "      <th>order_count</th>\n",
       "      <th>isPrintLuxDf</th>\n",
       "      <th>isPrintPandasDf</th>\n",
       "      <th>isPrintVis</th>\n",
       "      <th>isPrintVisList</th>\n",
       "      <th>param</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>2020-07-12 07:10:49.385000+00:00</td>\n",
       "      <td>switchTab</td>\n",
       "      <td>df</td>\n",
       "      <td>6828c0270b5d4bcf8853677b8ae74f7d</td>\n",
       "      <td>4.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Distribution</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>2020-07-12 07:10:49.982000+00:00</td>\n",
       "      <td>switchTab</td>\n",
       "      <td>df</td>\n",
       "      <td>6828c0270b5d4bcf8853677b8ae74f7d</td>\n",
       "      <td>4.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Category</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>2020-07-12 07:10:50.687000+00:00</td>\n",
       "      <td>switchTab</td>\n",
       "      <td>df</td>\n",
       "      <td>6828c0270b5d4bcf8853677b8ae74f7d</td>\n",
       "      <td>4.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Temporal</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>2020-07-12 07:10:52.015000+00:00</td>\n",
       "      <td>switchTab</td>\n",
       "      <td>df</td>\n",
       "      <td>6828c0270b5d4bcf8853677b8ae74f7d</td>\n",
       "      <td>4.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Correlation</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>66</th>\n",
       "      <td>2020-07-12 07:11:06.664000+00:00</td>\n",
       "      <td>switchTab</td>\n",
       "      <td>df</td>\n",
       "      <td>6828c0270b5d4bcf8853677b8ae74f7d</td>\n",
       "      <td>4.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Distribution</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>94</th>\n",
       "      <td>2020-07-12 07:11:59.429000+00:00</td>\n",
       "      <td>switchTab</td>\n",
       "      <td>df.set_intent([lux.VisSpec(attribute = \"Accelera...</td>\n",
       "      <td>31f9084286dc4b78bb7cbcd643cbc9ce</td>\n",
       "      <td>8.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Filter</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>96</th>\n",
       "      <td>2020-07-12 07:12:00.640000+00:00</td>\n",
       "      <td>switchTab</td>\n",
       "      <td>df.set_intent([lux.VisSpec(attribute = \"Accelera...</td>\n",
       "      <td>31f9084286dc4b78bb7cbcd643cbc9ce</td>\n",
       "      <td>8.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Generalize</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>98</th>\n",
       "      <td>2020-07-12 07:12:12.571000+00:00</td>\n",
       "      <td>switchTab</td>\n",
       "      <td>df.set_intent([lux.VisSpec(attribute = \"Accelera...</td>\n",
       "      <td>31f9084286dc4b78bb7cbcd643cbc9ce</td>\n",
       "      <td>8.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Enhance</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                               time       type  \\\n",
       "26 2020-07-12 07:10:49.385000+00:00  switchTab   \n",
       "28 2020-07-12 07:10:49.982000+00:00  switchTab   \n",
       "30 2020-07-12 07:10:50.687000+00:00  switchTab   \n",
       "32 2020-07-12 07:10:52.015000+00:00  switchTab   \n",
       "66 2020-07-12 07:11:06.664000+00:00  switchTab   \n",
       "94 2020-07-12 07:11:59.429000+00:00  switchTab   \n",
       "96 2020-07-12 07:12:00.640000+00:00  switchTab   \n",
       "98 2020-07-12 07:12:12.571000+00:00  switchTab   \n",
       "\n",
       "                                                 code  \\\n",
       "26                                                 df   \n",
       "28                                                 df   \n",
       "30                                                 df   \n",
       "32                                                 df   \n",
       "66                                                 df   \n",
       "94  df.set_intent([lux.VisSpec(attribute = \"Accelera...   \n",
       "96  df.set_intent([lux.VisSpec(attribute = \"Accelera...   \n",
       "98  df.set_intent([lux.VisSpec(attribute = \"Accelera...   \n",
       "\n",
       "                                  id  order_count  isPrintLuxDf  \\\n",
       "26  6828c0270b5d4bcf8853677b8ae74f7d          4.0           NaN   \n",
       "28  6828c0270b5d4bcf8853677b8ae74f7d          4.0           NaN   \n",
       "30  6828c0270b5d4bcf8853677b8ae74f7d          4.0           NaN   \n",
       "32  6828c0270b5d4bcf8853677b8ae74f7d          4.0           NaN   \n",
       "66  6828c0270b5d4bcf8853677b8ae74f7d          4.0           NaN   \n",
       "94  31f9084286dc4b78bb7cbcd643cbc9ce          8.0           NaN   \n",
       "96  31f9084286dc4b78bb7cbcd643cbc9ce          8.0           NaN   \n",
       "98  31f9084286dc4b78bb7cbcd643cbc9ce          8.0           NaN   \n",
       "\n",
       "    isPrintPandasDf  isPrintVis  isPrintVisList         param  \n",
       "26              NaN          NaN                    NaN  Distribution  \n",
       "28              NaN          NaN                    NaN      Category  \n",
       "30              NaN          NaN                    NaN      Temporal  \n",
       "32              NaN          NaN                    NaN   Correlation  \n",
       "66              NaN          NaN                    NaN  Distribution  \n",
       "94              NaN          NaN                    NaN        Filter  \n",
       "96              NaN          NaN                    NaN    Generalize  \n",
       "98              NaN          NaN                    NaN       Enhance  "
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "uiEventDf[uiEventDf[\"type\"]=='switchTab']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/dorislee/.virtualenvs/lux/lib/python3.7/site-packages/ipykernel_launcher.py:5: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-vis-versus-a-copy\n",
      "  \"\"\"\n"
     ]
    },
    {
     "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",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>code</th>\n",
       "      <th>id</th>\n",
       "      <th>order_count</th>\n",
       "      <th>isPrintLuxDf</th>\n",
       "      <th>isPrintPandasDf</th>\n",
       "      <th>isPrintVis</th>\n",
       "      <th>isPrintVisList</th>\n",
       "      <th>param</th>\n",
       "      <th>duration</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>df</td>\n",
       "      <td>6828c0270b5d4bcf8853677b8ae74f7d</td>\n",
       "      <td>4.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Correlation</td>\n",
       "      <td>00:00:11.830000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>67</th>\n",
       "      <td>df</td>\n",
       "      <td>6828c0270b5d4bcf8853677b8ae74f7d</td>\n",
       "      <td>4.0</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Distribution</td>\n",
       "      <td>00:00:03.483000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99</th>\n",
       "      <td>df.set_intent([lux.VisSpec(attribute = \"Accelera...</td>\n",
       "      <td>31f9084286dc4b78bb7cbcd643cbc9ce</td>\n",
       "      <td>8.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Enhance</td>\n",
       "      <td>00:00:10.011000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                 code  \\\n",
       "33                                                 df   \n",
       "67                                                 df   \n",
       "99  df.set_intent([lux.VisSpec(attribute = \"Accelera...   \n",
       "\n",
       "                                  id  order_count  isPrintLuxDf  \\\n",
       "33  6828c0270b5d4bcf8853677b8ae74f7d          4.0           NaN   \n",
       "67  6828c0270b5d4bcf8853677b8ae74f7d          4.0           NaN   \n",
       "99  31f9084286dc4b78bb7cbcd643cbc9ce          8.0           NaN   \n",
       "\n",
       "    isPrintPandasDf  isPrintVis  isPrintVisList         param  \\\n",
       "33              NaN          NaN                    NaN   Correlation   \n",
       "67              NaN          NaN                    NaN  Distribution   \n",
       "99              NaN          NaN                    NaN       Enhance   \n",
       "\n",
       "          duration  \n",
       "33 00:00:11.830000  \n",
       "67 00:00:03.483000  \n",
       "99 00:00:10.011000  "
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import numpy as np\n",
    "scrollEvents = uiEventDf[(uiEventDf[\"type\"]=='startScroll')|(uiEventDf[\"type\"]=='stopScroll')]\n",
    "\n",
    "# Compute the max-min of the group to find total duration for scrolling\n",
    "scrollEvents[\"duration\"] =scrollEvents.groupby([\"id\",\"code\",\"param\"])[\"time\"].transform(np.ptp)\n",
    "\n",
    "scrollEvents = scrollEvents.drop(columns=[\"time\",\"type\"])\n",
    "\n",
    "scrollEvents = scrollEvents.drop_duplicates()\n",
    "\n",
    "scrollEvents"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "scrollEvents[\"duration\"] = scrollEvents[\"duration\"].dt.total_seconds()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
       "\n",
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       "        text-align: right;\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>code</th>\n",
       "      <th>id</th>\n",
       "      <th>order_count</th>\n",
       "      <th>isPrintLuxDf</th>\n",
       "      <th>isPrintPandasDf</th>\n",
       "      <th>isPrintVis</th>\n",
       "      <th>isPrintVisList</th>\n",
       "      <th>param</th>\n",
       "      <th>duration</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>df</td>\n",
       "      <td>6828c0270b5d4bcf8853677b8ae74f7d</td>\n",
       "      <td>4.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Correlation</td>\n",
       "      <td>11.830</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>67</th>\n",
       "      <td>df</td>\n",
       "      <td>6828c0270b5d4bcf8853677b8ae74f7d</td>\n",
       "      <td>4.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Distribution</td>\n",
       "      <td>3.483</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99</th>\n",
       "      <td>df.set_intent([lux.VisSpec(attribute = \"Accelera...</td>\n",
       "      <td>31f9084286dc4b78bb7cbcd643cbc9ce</td>\n",
       "      <td>8.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Enhance</td>\n",
       "      <td>10.011</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                 code  \\\n",
       "33                                                 df   \n",
       "67                                                 df   \n",
       "99  df.set_intent([lux.VisSpec(attribute = \"Accelera...   \n",
       "\n",
       "                                  id  order_count  isPrintLuxDf  \\\n",
       "33  6828c0270b5d4bcf8853677b8ae74f7d          4.0           NaN   \n",
       "67  6828c0270b5d4bcf8853677b8ae74f7d          4.0           NaN   \n",
       "99  31f9084286dc4b78bb7cbcd643cbc9ce          8.0           NaN   \n",
       "\n",
       "    isPrintPandasDf  isPrintVis  isPrintVisList         param  \\\n",
       "33              NaN          NaN                    NaN   Correlation   \n",
       "67              NaN          NaN                    NaN  Distribution   \n",
       "99              NaN          NaN                    NaN       Enhance   \n",
       "\n",
       "    duration  \n",
       "33    11.830  \n",
       "67     3.483  \n",
       "99    10.011  "
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "scrollEvents"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "param\n",
       "Correlation     11.830\n",
       "Distribution     3.483\n",
       "Enhance         10.011\n",
       "Name: duration, dtype: float64"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "scrollEvents.groupby(\"param\")[\"duration\"].mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Cells that were deleted"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>time</th>\n",
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       "      <th>code</th>\n",
       "      <th>id</th>\n",
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       "      <th>isPrintPandasDf</th>\n",
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       "      <th>isPrintVisList</th>\n",
       "      <th>param</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Empty DataFrame\n",
       "Columns: [time, type, code, id, order_count, isPrintLuxDf, isPrintPandasDf, isPrintVis, isPrintVisList, param]\n",
       "Index: []"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df[\"type\"]=='deleteCodeCell']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Export Analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>time</th>\n",
       "      <th>type</th>\n",
       "      <th>code</th>\n",
       "      <th>id</th>\n",
       "      <th>order_count</th>\n",
       "      <th>isPrintLuxDf</th>\n",
       "      <th>isPrintPandasDf</th>\n",
       "      <th>isPrintVis</th>\n",
       "      <th>isPrintVisList</th>\n",
       "      <th>param</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>136</th>\n",
       "      <td>2020-07-12 07:13:01.522000+00:00</td>\n",
       "      <td>exportBtnClick</td>\n",
       "      <td>df.set_intent([lux.VisSpec(attribute = \"Accelera...</td>\n",
       "      <td>31f9084286dc4b78bb7cbcd643cbc9ce</td>\n",
       "      <td>8.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>{'Enhance': [0]}</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>175</th>\n",
       "      <td>2020-07-12 07:15:20.309000+00:00</td>\n",
       "      <td>exportBtnClick</td>\n",
       "      <td>df.set_intent([lux.VisSpec(attribute = \"Origin\")...</td>\n",
       "      <td>b244c5509aa44ae9a534c03b8ea3accc</td>\n",
       "      <td>13.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>{'Enhance': [0, 1]}</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                time            type  \\\n",
       "136 2020-07-12 07:13:01.522000+00:00  exportBtnClick   \n",
       "175 2020-07-12 07:15:20.309000+00:00  exportBtnClick   \n",
       "\n",
       "                                                  code  \\\n",
       "136  df.set_intent([lux.VisSpec(attribute = \"Accelera...   \n",
       "175  df.set_intent([lux.VisSpec(attribute = \"Origin\")...   \n",
       "\n",
       "                                   id  order_count  isPrintLuxDf  \\\n",
       "136  31f9084286dc4b78bb7cbcd643cbc9ce          8.0           NaN   \n",
       "175  b244c5509aa44ae9a534c03b8ea3accc         13.0           NaN   \n",
       "\n",
       "     isPrintPandasDf  isPrintVis  isPrintVisList                param  \n",
       "136              NaN          NaN                    NaN     {'Enhance': [0]}  \n",
       "175              NaN          NaN                    NaN  {'Enhance': [0, 1]}  "
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "uiEventDf[uiEventDf[\"type\"]==\"exportBtnClick\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
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       "      <th>time</th>\n",
       "      <th>type</th>\n",
       "      <th>code</th>\n",
       "      <th>id</th>\n",
       "      <th>order_count</th>\n",
       "      <th>isPrintLuxDf</th>\n",
       "      <th>isPrintPandasDf</th>\n",
       "      <th>isPrintVis</th>\n",
       "      <th>isPrintVisList</th>\n",
       "      <th>param</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>130</th>\n",
       "      <td>2020-07-12 07:12:52.044000+00:00</td>\n",
       "      <td>clickVis</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>{'index': 0, 'tabTitle': 'Enhance', 'vis': {'$...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>132</th>\n",
       "      <td>2020-07-12 07:12:53.254000+00:00</td>\n",
       "      <td>clickVis</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>{'index': 1, 'tabTitle': 'Enhance', 'vis': {'$...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>171</th>\n",
       "      <td>2020-07-12 07:15:04.594000+00:00</td>\n",
       "      <td>clickVis</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>{'index': 0, 'tabTitle': 'Enhance', 'vis': {'$...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>173</th>\n",
       "      <td>2020-07-12 07:15:05.786000+00:00</td>\n",
       "      <td>clickVis</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>{'index': 1, 'tabTitle': 'Enhance', 'vis': {'$...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                time      type code   id  order_count  \\\n",
       "130 2020-07-12 07:12:52.044000+00:00  clickVis  NaN  NaN          NaN   \n",
       "132 2020-07-12 07:12:53.254000+00:00  clickVis  NaN  NaN          NaN   \n",
       "171 2020-07-12 07:15:04.594000+00:00  clickVis  NaN  NaN          NaN   \n",
       "173 2020-07-12 07:15:05.786000+00:00  clickVis  NaN  NaN          NaN   \n",
       "\n",
       "    isPrintLuxDf isPrintPandasDf isPrintVis isPrintVisList  \\\n",
       "130          NaN             NaN         NaN                   NaN   \n",
       "132          NaN             NaN         NaN                   NaN   \n",
       "171          NaN             NaN         NaN                   NaN   \n",
       "173          NaN             NaN         NaN                   NaN   \n",
       "\n",
       "                                                 param  \n",
       "130  {'index': 0, 'tabTitle': 'Enhance', 'vis': {'$...  \n",
       "132  {'index': 1, 'tabTitle': 'Enhance', 'vis': {'$...  \n",
       "171  {'index': 0, 'tabTitle': 'Enhance', 'vis': {'$...  \n",
       "173  {'index': 1, 'tabTitle': 'Enhance', 'vis': {'$...  "
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df[\"type\"]==\"clickVis\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'index': 0,\n",
       " 'tabTitle': 'Enhance',\n",
       " 'vis': {'$schema': 'https://vega.github.io/schema/vega-lite/v4.8.1.json',\n",
       "  'config': {'mark': {'tooltip': {'content': 'encoding'}},\n",
       "   'vis': {'continuousHeight': 300, 'continuousWidth': 400}},\n",
       "  'data': {'name': 'data-9aec80917404170c20d0ca3f5e5ebe79'},\n",
       "  'datasets': {'data-9aec80917404170c20d0ca3f5e5ebe79': [{'Acceleration': 12,\n",
       "     'Horsepower': 130,\n",
       "     'Origin': 'USA'},\n",
       "    {'Acceleration': 11.5, 'Horsepower': 165, 'Origin': 'USA'},\n",
       "    {'Acceleration': 11, 'Horsepower': 150, 'Origin': 'USA'},\n",
       "    {'Acceleration': 12, 'Horsepower': 150, 'Origin': 'USA'},\n",
       "    {'Acceleration': 10.5, 'Horsepower': 140, 'Origin': 'USA'},\n",
       "    {'Acceleration': 10, 'Horsepower': 198, 'Origin': 'USA'},\n",
       "    {'Acceleration': 9, 'Horsepower': 220, 'Origin': 'USA'},\n",
       "    {'Acceleration': 8.5, 'Horsepower': 215, 'Origin': 'USA'},\n",
       "    {'Acceleration': 10, 'Horsepower': 225, 'Origin': 'USA'},\n",
       "    {'Acceleration': 8.5, 'Horsepower': 190, 'Origin': 'USA'},\n",
       "    {'Acceleration': 10, 'Horsepower': 170, 'Origin': 'USA'},\n",
       "    {'Acceleration': 8, 'Horsepower': 160, 'Origin': 'USA'},\n",
       "    {'Acceleration': 9.5, 'Horsepower': 150, 'Origin': 'USA'},\n",
       "    {'Acceleration': 10, 'Horsepower': 225, 'Origin': 'USA'},\n",
       "    {'Acceleration': 15, 'Horsepower': 95, 'Origin': 'Japan'},\n",
       "    {'Acceleration': 15.5, 'Horsepower': 95, 'Origin': 'USA'},\n",
       "    {'Acceleration': 15.5, 'Horsepower': 97, 'Origin': 'USA'},\n",
       "    {'Acceleration': 16, 'Horsepower': 85, 'Origin': 'USA'},\n",
       "    {'Acceleration': 14.5, 'Horsepower': 88, 'Origin': 'Japan'},\n",
       "    {'Acceleration': 20.5, 'Horsepower': 46, 'Origin': 'Europe'},\n",
       "    {'Acceleration': 17.5, 'Horsepower': 87, 'Origin': 'Europe'},\n",
       "    {'Acceleration': 14.5, 'Horsepower': 90, 'Origin': 'Europe'},\n",
       "    {'Acceleration': 17.5, 'Horsepower': 95, 'Origin': 'Europe'},\n",
       "    {'Acceleration': 12.5, 'Horsepower': 113, 'Origin': 'Europe'},\n",
       "    {'Acceleration': 15, 'Horsepower': 90, 'Origin': 'USA'},\n",
       "    {'Acceleration': 14, 'Horsepower': 215, 'Origin': 'USA'},\n",
       "    {'Acceleration': 15, 'Horsepower': 200, 'Origin': 'USA'},\n",
       "    {'Acceleration': 13.5, 'Horsepower': 210, 'Origin': 'USA'},\n",
       "    {'Acceleration': 18.5, 'Horsepower': 193, 'Origin': 'USA'},\n",
       "    {'Acceleration': 14.5, 'Horsepower': 88, 'Origin': 'Japan'},\n",
       "    {'Acceleration': 15.5, 'Horsepower': 90, 'Origin': 'USA'},\n",
       "    {'Acceleration': 14, 'Horsepower': 95, 'Origin': 'Japan'},\n",
       "    {'Acceleration': 13, 'Horsepower': 100, 'Origin': 'USA'},\n",
       "    {'Acceleration': 15.5, 'Horsepower': 105, 'Origin': 'USA'},\n",
       "    {'Acceleration': 15.5, 'Horsepower': 100, 'Origin': 'USA'},\n",
       "    {'Acceleration': 15.5, 'Horsepower': 88, 'Origin': 'USA'},\n",
       "    {'Acceleration': 15.5, 'Horsepower': 100, 'Origin': 'USA'},\n",
       "    {'Acceleration': 12, 'Horsepower': 165, 'Origin': 'USA'},\n",
       "    {'Acceleration': 11.5, 'Horsepower': 175, 'Origin': 'USA'},\n",
       "    {'Acceleration': 13.5, 'Horsepower': 153, 'Origin': 'USA'},\n",
       "    {'Acceleration': 13, 'Horsepower': 150, 'Origin': 'USA'},\n",
       "    {'Acceleration': 11.5, 'Horsepower': 180, 'Origin': 'USA'},\n",
       "    {'Acceleration': 12, 'Horsepower': 170, 'Origin': 'USA'},\n",
       "    {'Acceleration': 12, 'Horsepower': 175, 'Origin': 'USA'},\n",
       "    {'Acceleration': 13.5, 'Horsepower': 110, 'Origin': 'USA'},\n",
       "    {'Acceleration': 19, 'Horsepower': 72, 'Origin': 'USA'},\n",
       "    {'Acceleration': 15, 'Horsepower': 100, 'Origin': 'USA'},\n",
       "    {'Acceleration': 14.5, 'Horsepower': 88, 'Origin': 'USA'},\n",
       "    {'Acceleration': 14, 'Horsepower': 86, 'Origin': 'USA'},\n",
       "    {'Acceleration': 14, 'Horsepower': 90, 'Origin': 'Europe'},\n",
       "    {'Acceleration': 19.5, 'Horsepower': 70, 'Origin': 'Europe'},\n",
       "    {'Acceleration': 14.5, 'Horsepower': 76, 'Origin': 'Europe'},\n",
       "    {'Acceleration': 19, 'Horsepower': 65, 'Origin': 'Japan'},\n",
       "    {'Acceleration': 18, 'Horsepower': 69, 'Origin': 'Japan'},\n",
       "    {'Acceleration': 19, 'Horsepower': 60, 'Origin': 'Europe'},\n",
       "    {'Acceleration': 20.5, 'Horsepower': 70, 'Origin': 'USA'},\n",
       "    {'Acceleration': 15.5, 'Horsepower': 95, 'Origin': 'Japan'},\n",
       "    {'Acceleration': 17, 'Horsepower': 80, 'Origin': 'USA'},\n",
       "    {'Acceleration': 23.5, 'Horsepower': 54, 'Origin': 'Europe'},\n",
       "    {'Acceleration': 19.5, 'Horsepower': 90, 'Origin': 'USA'},\n",
       "    {'Acceleration': 16.5, 'Horsepower': 86, 'Origin': 'USA'},\n",
       "    {'Acceleration': 12, 'Horsepower': 165, 'Origin': 'USA'},\n",
       "    {'Acceleration': 12, 'Horsepower': 175, 'Origin': 'USA'},\n",
       "    {'Acceleration': 13.5, 'Horsepower': 150, 'Origin': 'USA'},\n",
       "    {'Acceleration': 13, 'Horsepower': 153, 'Origin': 'USA'},\n",
       "    {'Acceleration': 11.5, 'Horsepower': 150, 'Origin': 'USA'},\n",
       "    {'Acceleration': 11, 'Horsepower': 208, 'Origin': 'USA'},\n",
       "    {'Acceleration': 13.5, 'Horsepower': 155, 'Origin': 'USA'},\n",
       "    {'Acceleration': 13.5, 'Horsepower': 160, 'Origin': 'USA'},\n",
       "    {'Acceleration': 12.5, 'Horsepower': 190, 'Origin': 'USA'},\n",
       "    {'Acceleration': 13.5, 'Horsepower': 97, 'Origin': 'Japan'},\n",
       "    {'Acceleration': 12.5, 'Horsepower': 150, 'Origin': 'USA'},\n",
       "    {'Acceleration': 14, 'Horsepower': 130, 'Origin': 'USA'},\n",
       "    {'Acceleration': 16, 'Horsepower': 140, 'Origin': 'USA'},\n",
       "    {'Acceleration': 14, 'Horsepower': 150, 'Origin': 'USA'},\n",
       "    {'Acceleration': 14.5, 'Horsepower': 112, 'Origin': 'Europe'},\n",
       "    {'Acceleration': 18, 'Horsepower': 76, 'Origin': 'Europe'},\n",
       "    {'Acceleration': 19.5, 'Horsepower': 87, 'Origin': 'Europe'},\n",
       "    {'Acceleration': 18, 'Horsepower': 69, 'Origin': 'Europe'},\n",
       "    {'Acceleration': 16, 'Horsepower': 86, 'Origin': 'USA'},\n",
       "    {'Acceleration': 17, 'Horsepower': 92, 'Origin': 'Japan'},\n",
       "    {'Acceleration': 14.5, 'Horsepower': 97, 'Origin': 'Japan'},\n",
       "    {'Acceleration': 15, 'Horsepower': 80, 'Origin': 'USA'},\n",
       "    {'Acceleration': 16.5, 'Horsepower': 88, 'Origin': 'Japan'},\n",
       "    {'Acceleration': 13, 'Horsepower': 175, 'Origin': 'USA'},\n",
       "    {'Acceleration': 11.5, 'Horsepower': 150, 'Origin': 'USA'},\n",
       "    {'Acceleration': 13, 'Horsepower': 145, 'Origin': 'USA'},\n",
       "    {'Acceleration': 14.5, 'Horsepower': 137, 'Origin': 'USA'},\n",
       "    {'Acceleration': 12.5, 'Horsepower': 150, 'Origin': 'USA'},\n",
       "    {'Acceleration': 11.5, 'Horsepower': 198, 'Origin': 'USA'},\n",
       "    {'Acceleration': 12, 'Horsepower': 150, 'Origin': 'USA'},\n",
       "    {'Acceleration': 13, 'Horsepower': 158, 'Origin': 'USA'},\n",
       "    {'Acceleration': 14.5, 'Horsepower': 150, 'Origin': 'USA'},\n",
       "    {'Acceleration': 11, 'Horsepower': 215, 'Origin': 'USA'},\n",
       "    {'Acceleration': 11, 'Horsepower': 225, 'Origin': 'USA'},\n",
       "    {'Acceleration': 11, 'Horsepower': 175, 'Origin': 'USA'},\n",
       "    {'Acceleration': 16.5, 'Horsepower': 105, 'Origin': 'USA'},\n",
       "    {'Acceleration': 18, 'Horsepower': 100, 'Origin': 'USA'},\n",
       "    {'Acceleration': 16, 'Horsepower': 100, 'Origin': 'USA'},\n",
       "    {'Acceleration': 16.5, 'Horsepower': 88, 'Origin': 'USA'},\n",
       "    {'Acceleration': 16, 'Horsepower': 95, 'Origin': 'USA'},\n",
       "    {'Acceleration': 21, 'Horsepower': 46, 'Origin': 'Europe'},\n",
       "    {'Acceleration': 14, 'Horsepower': 150, 'Origin': 'USA'},\n",
       "    {'Acceleration': 12.5, 'Horsepower': 167, 'Origin': 'USA'},\n",
       "    {'Acceleration': 13, 'Horsepower': 170, 'Origin': 'USA'},\n",
       "    {'Acceleration': 12.5, 'Horsepower': 180, 'Origin': 'USA'},\n",
       "    {'Acceleration': 15, 'Horsepower': 100, 'Origin': 'USA'},\n",
       "    {'Acceleration': 19, 'Horsepower': 88, 'Origin': 'Japan'},\n",
       "    {'Acceleration': 19.5, 'Horsepower': 72, 'Origin': 'USA'},\n",
       "    {'Acceleration': 16.5, 'Horsepower': 94, 'Origin': 'Japan'},\n",
       "    {'Acceleration': 13.5, 'Horsepower': 90, 'Origin': 'Japan'},\n",
       "    {'Acceleration': 18.5, 'Horsepower': 85, 'Origin': 'USA'},\n",
       "    {'Acceleration': 14, 'Horsepower': 107, 'Origin': 'USA'},\n",
       "    {'Acceleration': 15.5, 'Horsepower': 90, 'Origin': 'Europe'},\n",
       "    {'Acceleration': 13, 'Horsepower': 145, 'Origin': 'USA'},\n",
       "    {'Acceleration': 9.5, 'Horsepower': 230, 'Origin': 'USA'},\n",
       "    {'Acceleration': 19.5, 'Horsepower': 49, 'Origin': 'Europe'},\n",
       "    {'Acceleration': 15.5, 'Horsepower': 75, 'Origin': 'Europe'},\n",
       "    {'Acceleration': 14, 'Horsepower': 91, 'Origin': 'Europe'},\n",
       "    {'Acceleration': 15.5, 'Horsepower': 112, 'Origin': 'Europe'},\n",
       "    {'Acceleration': 11, 'Horsepower': 150, 'Origin': 'USA'},\n",
       "    {'Acceleration': 14, 'Horsepower': 110, 'Origin': 'Europe'},\n",
       "    {'Acceleration': 13.5, 'Horsepower': 122, 'Origin': 'Japan'},\n",
       "    {'Acceleration': 11, 'Horsepower': 180, 'Origin': 'USA'},\n",
       "    {'Acceleration': 16.5, 'Horsepower': 95, 'Origin': 'USA'},\n",
       "    {'Acceleration': 16, 'Horsepower': 100, 'Origin': 'USA'},\n",
       "    {'Acceleration': 17, 'Horsepower': 100, 'Origin': 'USA'},\n",
       "    {'Acceleration': 19, 'Horsepower': 67, 'Origin': 'Japan'},\n",
       "    {'Acceleration': 16.5, 'Horsepower': 80, 'Origin': 'USA'},\n",
       "    {'Acceleration': 21, 'Horsepower': 65, 'Origin': 'Japan'},\n",
       "    {'Acceleration': 17, 'Horsepower': 75, 'Origin': 'USA'},\n",
       "    {'Acceleration': 17, 'Horsepower': 100, 'Origin': 'USA'},\n",
       "    {'Acceleration': 18, 'Horsepower': 110, 'Origin': 'USA'},\n",
       "    {'Acceleration': 16.5, 'Horsepower': 105, 'Origin': 'USA'},\n",
       "    {'Acceleration': 14, 'Horsepower': 140, 'Origin': 'USA'},\n",
       "    {'Acceleration': 14.5, 'Horsepower': 150, 'Origin': 'USA'},\n",
       "    {'Acceleration': 13.5, 'Horsepower': 150, 'Origin': 'USA'},\n",
       "    {'Acceleration': 16, 'Horsepower': 140, 'Origin': 'USA'},\n",
       "    {'Acceleration': 15.5, 'Horsepower': 150, 'Origin': 'USA'},\n",
       "    {'Acceleration': 16.5, 'Horsepower': 83, 'Origin': 'Europe'},\n",
       "    {'Acceleration': 15.5, 'Horsepower': 67, 'Origin': 'Europe'},\n",
       "    {'Acceleration': 14.5, 'Horsepower': 78, 'Origin': 'Europe'},\n",
       "    {'Acceleration': 16.5, 'Horsepower': 52, 'Origin': 'Japan'},\n",
       "    {'Acceleration': 19, 'Horsepower': 61, 'Origin': 'Japan'},\n",
       "    {'Acceleration': 14.5, 'Horsepower': 75, 'Origin': 'USA'},\n",
       "    {'Acceleration': 15.5, 'Horsepower': 75, 'Origin': 'Europe'},\n",
       "    {'Acceleration': 14, 'Horsepower': 75, 'Origin': 'Europe'},\n",
       "    {'Acceleration': 15, 'Horsepower': 97, 'Origin': 'Japan'},\n",
       "    {'Acceleration': 15.5, 'Horsepower': 93, 'Origin': 'Japan'},\n",
       "    {'Acceleration': 16, 'Horsepower': 67, 'Origin': 'Europe'},\n",
       "    {'Acceleration': 16, 'Horsepower': 95, 'Origin': 'USA'},\n",
       "    {'Acceleration': 16, 'Horsepower': 105, 'Origin': 'USA'},\n",
       "    {'Acceleration': 21, 'Horsepower': 72, 'Origin': 'USA'},\n",
       "    {'Acceleration': 19.5, 'Horsepower': 72, 'Origin': 'USA'},\n",
       "    {'Acceleration': 11.5, 'Horsepower': 170, 'Origin': 'USA'},\n",
       "    {'Acceleration': 14, 'Horsepower': 145, 'Origin': 'USA'},\n",
       "    {'Acceleration': 14.5, 'Horsepower': 150, 'Origin': 'USA'},\n",
       "    {'Acceleration': 13.5, 'Horsepower': 148, 'Origin': 'USA'},\n",
       "    {'Acceleration': 21, 'Horsepower': 110, 'Origin': 'USA'},\n",
       "    {'Acceleration': 18.5, 'Horsepower': 105, 'Origin': 'USA'},\n",
       "    {'Acceleration': 19, 'Horsepower': 110, 'Origin': 'USA'},\n",
       "    {'Acceleration': 19, 'Horsepower': 95, 'Origin': 'USA'},\n",
       "    {'Acceleration': 15, 'Horsepower': 110, 'Origin': 'USA'},\n",
       "    {'Acceleration': 13.5, 'Horsepower': 110, 'Origin': 'USA'},\n",
       "    {'Acceleration': 12, 'Horsepower': 129, 'Origin': 'USA'},\n",
       "    {'Acceleration': 16, 'Horsepower': 75, 'Origin': 'Japan'},\n",
       "    {'Acceleration': 17, 'Horsepower': 83, 'Origin': 'USA'},\n",
       "    {'Acceleration': 16, 'Horsepower': 100, 'Origin': 'USA'},\n",
       "    {'Acceleration': 18.5, 'Horsepower': 78, 'Origin': 'USA'},\n",
       "    {'Acceleration': 13.5, 'Horsepower': 96, 'Origin': 'Japan'},\n",
       "    {'Acceleration': 16.5, 'Horsepower': 71, 'Origin': 'Europe'},\n",
       "    {'Acceleration': 17, 'Horsepower': 97, 'Origin': 'Japan'},\n",
       "    {'Acceleration': 14.5, 'Horsepower': 97, 'Origin': 'USA'},\n",
       "    {'Acceleration': 14, 'Horsepower': 70, 'Origin': 'Europe'},\n",
       "    {'Acceleration': 17, 'Horsepower': 90, 'Origin': 'USA'},\n",
       "    {'Acceleration': 15, 'Horsepower': 95, 'Origin': 'Europe'},\n",
       "    {'Acceleration': 17, 'Horsepower': 88, 'Origin': 'Europe'},\n",
       "    {'Acceleration': 14.5, 'Horsepower': 98, 'Origin': 'Europe'},\n",
       "    {'Acceleration': 13.5, 'Horsepower': 115, 'Origin': 'Europe'},\n",
       "    {'Acceleration': 17.5, 'Horsepower': 53, 'Origin': 'Japan'},\n",
       "    {'Acceleration': 15.5, 'Horsepower': 86, 'Origin': 'Europe'},\n",
       "    {'Acceleration': 16.9, 'Horsepower': 81, 'Origin': 'Europe'},\n",
       "    {'Acceleration': 14.9, 'Horsepower': 92, 'Origin': 'USA'},\n",
       "    {'Acceleration': 17.7, 'Horsepower': 79, 'Origin': 'USA'},\n",
       "    {'Acceleration': 15.3, 'Horsepower': 83, 'Origin': 'Europe'},\n",
       "    {'Acceleration': 13, 'Horsepower': 140, 'Origin': 'USA'},\n",
       "    {'Acceleration': 13, 'Horsepower': 150, 'Origin': 'USA'},\n",
       "    {'Acceleration': 13.9, 'Horsepower': 120, 'Origin': 'USA'},\n",
       "    {'Acceleration': 12.8, 'Horsepower': 152, 'Origin': 'USA'},\n",
       "    {'Acceleration': 15.4, 'Horsepower': 100, 'Origin': 'USA'},\n",
       "    {'Acceleration': 14.5, 'Horsepower': 105, 'Origin': 'USA'},\n",
       "    {'Acceleration': 17.6, 'Horsepower': 81, 'Origin': 'USA'},\n",
       "    {'Acceleration': 17.6, 'Horsepower': 90, 'Origin': 'USA'},\n",
       "    {'Acceleration': 22.2, 'Horsepower': 52, 'Origin': 'USA'},\n",
       "    {'Acceleration': 22.1, 'Horsepower': 60, 'Origin': 'USA'},\n",
       "    {'Acceleration': 14.2, 'Horsepower': 70, 'Origin': 'Europe'},\n",
       "    {'Acceleration': 17.4, 'Horsepower': 53, 'Origin': 'Japan'},\n",
       "    {'Acceleration': 17.7, 'Horsepower': 100, 'Origin': 'USA'},\n",
       "    {'Acceleration': 21, 'Horsepower': 78, 'Origin': 'USA'},\n",
       "    {'Acceleration': 16.2, 'Horsepower': 110, 'Origin': 'USA'},\n",
       "    {'Acceleration': 17.8, 'Horsepower': 95, 'Origin': 'USA'},\n",
       "    {'Acceleration': 12.2, 'Horsepower': 71, 'Origin': 'Europe'},\n",
       "    {'Acceleration': 17, 'Horsepower': 70, 'Origin': 'Japan'},\n",
       "    {'Acceleration': 16.4, 'Horsepower': 75, 'Origin': 'Japan'},\n",
       "    {'Acceleration': 13.6, 'Horsepower': 72, 'Origin': 'USA'},\n",
       "    {'Acceleration': 15.7, 'Horsepower': 102, 'Origin': 'Europe'},\n",
       "    {'Acceleration': 13.2, 'Horsepower': 150, 'Origin': 'USA'},\n",
       "    {'Acceleration': 21.9, 'Horsepower': 88, 'Origin': 'Europe'},\n",
       "    {'Acceleration': 15.5, 'Horsepower': 108, 'Origin': 'Japan'},\n",
       "    {'Acceleration': 16.7, 'Horsepower': 120, 'Origin': 'Europe'},\n",
       "    {'Acceleration': 12.1, 'Horsepower': 180, 'Origin': 'USA'},\n",
       "    {'Acceleration': 12, 'Horsepower': 145, 'Origin': 'USA'},\n",
       "    {'Acceleration': 15, 'Horsepower': 130, 'Origin': 'USA'},\n",
       "    {'Acceleration': 14, 'Horsepower': 150, 'Origin': 'USA'},\n",
       "    {'Acceleration': 18.5, 'Horsepower': 68, 'Origin': 'Japan'},\n",
       "    {'Acceleration': 14.8, 'Horsepower': 80, 'Origin': 'USA'},\n",
       "    {'Acceleration': 18.6, 'Horsepower': 58, 'Origin': 'Europe'},\n",
       "    {'Acceleration': 15.5, 'Horsepower': 96, 'Origin': 'USA'},\n",
       "    {'Acceleration': 16.8, 'Horsepower': 70, 'Origin': 'Japan'},\n",
       "    {'Acceleration': 12.5, 'Horsepower': 145, 'Origin': 'USA'},\n",
       "    {'Acceleration': 19, 'Horsepower': 110, 'Origin': 'USA'},\n",
       "    {'Acceleration': 13.7, 'Horsepower': 145, 'Origin': 'USA'},\n",
       "    {'Acceleration': 14.9, 'Horsepower': 130, 'Origin': 'USA'},\n",
       "    {'Acceleration': 16.4, 'Horsepower': 110, 'Origin': 'USA'},\n",
       "    {'Acceleration': 16.9, 'Horsepower': 105, 'Origin': 'USA'},\n",
       "    {'Acceleration': 17.7, 'Horsepower': 100, 'Origin': 'USA'},\n",
       "    {'Acceleration': 19, 'Horsepower': 98, 'Origin': 'USA'},\n",
       "    {'Acceleration': 11.1, 'Horsepower': 180, 'Origin': 'USA'},\n",
       "    {'Acceleration': 11.4, 'Horsepower': 170, 'Origin': 'USA'},\n",
       "    {'Acceleration': 12.2, 'Horsepower': 190, 'Origin': 'USA'},\n",
       "    {'Acceleration': 14.5, 'Horsepower': 149, 'Origin': 'USA'},\n",
       "    {'Acceleration': 14.5, 'Horsepower': 78, 'Origin': 'Europe'},\n",
       "    {'Acceleration': 16, 'Horsepower': 88, 'Origin': 'USA'},\n",
       "    {'Acceleration': 18.2, 'Horsepower': 75, 'Origin': 'Japan'},\n",
       "    {'Acceleration': 15.8, 'Horsepower': 89, 'Origin': 'USA'},\n",
       "    {'Acceleration': 17, 'Horsepower': 63, 'Origin': 'USA'},\n",
       "    {'Acceleration': 15.9, 'Horsepower': 83, 'Origin': 'USA'},\n",
       "    {'Acceleration': 16.4, 'Horsepower': 67, 'Origin': 'Japan'},\n",
       "    {'Acceleration': 14.1, 'Horsepower': 78, 'Origin': 'Europe'},\n",
       "    {'Acceleration': 14.5, 'Horsepower': 97, 'Origin': 'Japan'},\n",
       "    {'Acceleration': 12.8, 'Horsepower': 110, 'Origin': 'Europe'},\n",
       "    {'Acceleration': 13.5, 'Horsepower': 110, 'Origin': 'Japan'},\n",
       "    {'Acceleration': 21.5, 'Horsepower': 48, 'Origin': 'Europe'},\n",
       "    {'Acceleration': 14.4, 'Horsepower': 66, 'Origin': 'USA'},\n",
       "    {'Acceleration': 19.4, 'Horsepower': 52, 'Origin': 'Japan'},\n",
       "    {'Acceleration': 18.6, 'Horsepower': 70, 'Origin': 'Japan'},\n",
       "    {'Acceleration': 16.4, 'Horsepower': 60, 'Origin': 'Japan'},\n",
       "    {'Acceleration': 15.5, 'Horsepower': 110, 'Origin': 'USA'},\n",
       "    {'Acceleration': 13.2, 'Horsepower': 140, 'Origin': 'USA'},\n",
       "    {'Acceleration': 12.8, 'Horsepower': 139, 'Origin': 'USA'},\n",
       "    {'Acceleration': 19.2, 'Horsepower': 105, 'Origin': 'USA'},\n",
       "    {'Acceleration': 18.2, 'Horsepower': 95, 'Origin': 'USA'},\n",
       "    {'Acceleration': 15.8, 'Horsepower': 85, 'Origin': 'USA'},\n",
       "    {'Acceleration': 15.4, 'Horsepower': 88, 'Origin': 'USA'},\n",
       "    {'Acceleration': 17.2, 'Horsepower': 100, 'Origin': 'USA'},\n",
       "    {'Acceleration': 17.2, 'Horsepower': 90, 'Origin': 'USA'},\n",
       "    {'Acceleration': 15.8, 'Horsepower': 105, 'Origin': 'USA'},\n",
       "    {'Acceleration': 16.7, 'Horsepower': 85, 'Origin': 'USA'},\n",
       "    {'Acceleration': 18.7, 'Horsepower': 110, 'Origin': 'USA'},\n",
       "    {'Acceleration': 15.1, 'Horsepower': 120, 'Origin': 'USA'},\n",
       "    {'Acceleration': 13.2, 'Horsepower': 145, 'Origin': 'USA'},\n",
       "    {'Acceleration': 13.4, 'Horsepower': 165, 'Origin': 'USA'},\n",
       "    {'Acceleration': 11.2, 'Horsepower': 139, 'Origin': 'USA'},\n",
       "    {'Acceleration': 13.7, 'Horsepower': 140, 'Origin': 'USA'},\n",
       "    {'Acceleration': 16.5, 'Horsepower': 68, 'Origin': 'USA'},\n",
       "    {'Acceleration': 14.2, 'Horsepower': 95, 'Origin': 'Japan'},\n",
       "    {'Acceleration': 14.7, 'Horsepower': 97, 'Origin': 'Japan'},\n",
       "    {'Acceleration': 14.5, 'Horsepower': 75, 'Origin': 'USA'},\n",
       "    {'Acceleration': 14.8, 'Horsepower': 95, 'Origin': 'Japan'},\n",
       "    {'Acceleration': 16.7, 'Horsepower': 105, 'Origin': 'USA'},\n",
       "    {'Acceleration': 17.6, 'Horsepower': 85, 'Origin': 'USA'},\n",
       "    {'Acceleration': 14.9, 'Horsepower': 97, 'Origin': 'Japan'},\n",
       "    {'Acceleration': 15.9, 'Horsepower': 103, 'Origin': 'Europe'},\n",
       "    {'Acceleration': 13.6, 'Horsepower': 125, 'Origin': 'Europe'},\n",
       "    {'Acceleration': 15.7, 'Horsepower': 115, 'Origin': 'Europe'},\n",
       "    {'Acceleration': 15.8, 'Horsepower': 133, 'Origin': 'Europe'},\n",
       "    {'Acceleration': 14.9, 'Horsepower': 71, 'Origin': 'Europe'},\n",
       "    {'Acceleration': 16.6, 'Horsepower': 68, 'Origin': 'Japan'},\n",
       "    {'Acceleration': 15.4, 'Horsepower': 115, 'Origin': 'USA'},\n",
       "    {'Acceleration': 18.2, 'Horsepower': 85, 'Origin': 'USA'},\n",
       "    {'Acceleration': 17.3, 'Horsepower': 88, 'Origin': 'USA'},\n",
       "    {'Acceleration': 18.2, 'Horsepower': 90, 'Origin': 'USA'},\n",
       "    {'Acceleration': 16.6, 'Horsepower': 110, 'Origin': 'USA'},\n",
       "    {'Acceleration': 15.4, 'Horsepower': 130, 'Origin': 'USA'},\n",
       "    {'Acceleration': 13.4, 'Horsepower': 129, 'Origin': 'USA'},\n",
       "    {'Acceleration': 13.2, 'Horsepower': 138, 'Origin': 'USA'},\n",
       "    {'Acceleration': 15.2, 'Horsepower': 135, 'Origin': 'USA'},\n",
       "    {'Acceleration': 14.9, 'Horsepower': 155, 'Origin': 'USA'},\n",
       "    {'Acceleration': 14.3, 'Horsepower': 142, 'Origin': 'USA'},\n",
       "    {'Acceleration': 15, 'Horsepower': 125, 'Origin': 'USA'},\n",
       "    {'Acceleration': 13, 'Horsepower': 150, 'Origin': 'USA'},\n",
       "    {'Acceleration': 14, 'Horsepower': 71, 'Origin': 'Europe'},\n",
       "    {'Acceleration': 15.2, 'Horsepower': 65, 'Origin': 'Japan'},\n",
       "    {'Acceleration': 14.4, 'Horsepower': 80, 'Origin': 'USA'},\n",
       "    {'Acceleration': 15, 'Horsepower': 80, 'Origin': 'USA'},\n",
       "    {'Acceleration': 20.1, 'Horsepower': 77, 'Origin': 'Europe'},\n",
       "    {'Acceleration': 17.4, 'Horsepower': 125, 'Origin': 'USA'},\n",
       "    {'Acceleration': 24.8, 'Horsepower': 71, 'Origin': 'Europe'},\n",
       "    {'Acceleration': 22.2, 'Horsepower': 90, 'Origin': 'USA'},\n",
       "    {'Acceleration': 13.2, 'Horsepower': 70, 'Origin': 'USA'},\n",
       "    {'Acceleration': 14.9, 'Horsepower': 70, 'Origin': 'USA'},\n",
       "    {'Acceleration': 19.2, 'Horsepower': 65, 'Origin': 'Japan'},\n",
       "    {'Acceleration': 14.7, 'Horsepower': 69, 'Origin': 'Europe'},\n",
       "    {'Acceleration': 16, 'Horsepower': 90, 'Origin': 'USA'},\n",
       "    {'Acceleration': 11.3, 'Horsepower': 115, 'Origin': 'USA'},\n",
       "    {'Acceleration': 12.9, 'Horsepower': 115, 'Origin': 'USA'},\n",
       "    {'Acceleration': 13.2, 'Horsepower': 90, 'Origin': 'USA'},\n",
       "    {'Acceleration': 14.7, 'Horsepower': 76, 'Origin': 'Europe'},\n",
       "    {'Acceleration': 18.8, 'Horsepower': 60, 'Origin': 'Japan'},\n",
       "    {'Acceleration': 15.5, 'Horsepower': 70, 'Origin': 'USA'},\n",
       "    {'Acceleration': 16.4, 'Horsepower': 65, 'Origin': 'Japan'},\n",
       "    {'Acceleration': 16.5, 'Horsepower': 90, 'Origin': 'USA'},\n",
       "    {'Acceleration': 18.1, 'Horsepower': 88, 'Origin': 'USA'},\n",
       "    {'Acceleration': 20.1, 'Horsepower': 90, 'Origin': 'USA'},\n",
       "    {'Acceleration': 18.7, 'Horsepower': 90, 'Origin': 'USA'},\n",
       "    {'Acceleration': 15.8, 'Horsepower': 78, 'Origin': 'Europe'},\n",
       "    {'Acceleration': 15.5, 'Horsepower': 90, 'Origin': 'Japan'},\n",
       "    {'Acceleration': 17.5, 'Horsepower': 75, 'Origin': 'Japan'},\n",
       "    {'Acceleration': 15, 'Horsepower': 92, 'Origin': 'Japan'},\n",
       "    {'Acceleration': 15.2, 'Horsepower': 75, 'Origin': 'Japan'},\n",
       "    {'Acceleration': 17.9, 'Horsepower': 65, 'Origin': 'Japan'},\n",
       "    {'Acceleration': 14.4, 'Horsepower': 105, 'Origin': 'USA'},\n",
       "    {'Acceleration': 19.2, 'Horsepower': 65, 'Origin': 'Japan'},\n",
       "    {'Acceleration': 21.7, 'Horsepower': 48, 'Origin': 'Europe'},\n",
       "    {'Acceleration': 23.7, 'Horsepower': 48, 'Origin': 'Europe'},\n",
       "    {'Acceleration': 19.9, 'Horsepower': 67, 'Origin': 'Europe'},\n",
       "    {'Acceleration': 21.8, 'Horsepower': 67, 'Origin': 'Europe'},\n",
       "    {'Acceleration': 13.8, 'Horsepower': 67, 'Origin': 'Japan'},\n",
       "    {'Acceleration': 18, 'Horsepower': 67, 'Origin': 'Japan'},\n",
       "    {'Acceleration': 15.3, 'Horsepower': 62, 'Origin': 'Europe'},\n",
       "    {'Acceleration': 11.4, 'Horsepower': 132, 'Origin': 'Japan'},\n",
       "    {'Acceleration': 12.5, 'Horsepower': 100, 'Origin': 'Japan'},\n",
       "    {'Acceleration': 15.1, 'Horsepower': 88, 'Origin': 'Europe'},\n",
       "    {'Acceleration': 17, 'Horsepower': 72, 'Origin': 'Japan'},\n",
       "    {'Acceleration': 15.7, 'Horsepower': 84, 'Origin': 'USA'},\n",
       "    {'Acceleration': 16.4, 'Horsepower': 84, 'Origin': 'USA'},\n",
       "    {'Acceleration': 14.4, 'Horsepower': 92, 'Origin': 'USA'},\n",
       "    {'Acceleration': 12.6, 'Horsepower': 110, 'Origin': 'USA'},\n",
       "    {'Acceleration': 12.9, 'Horsepower': 84, 'Origin': 'USA'},\n",
       "    {'Acceleration': 16.9, 'Horsepower': 58, 'Origin': 'Japan'},\n",
       "    {'Acceleration': 16.4, 'Horsepower': 64, 'Origin': 'USA'},\n",
       "    {'Acceleration': 16.1, 'Horsepower': 60, 'Origin': 'Japan'},\n",
       "    {'Acceleration': 17.8, 'Horsepower': 67, 'Origin': 'Japan'},\n",
       "    {'Acceleration': 19.4, 'Horsepower': 65, 'Origin': 'Japan'},\n",
       "    {'Acceleration': 17.3, 'Horsepower': 62, 'Origin': 'Japan'},\n",
       "    {'Acceleration': 16, 'Horsepower': 68, 'Origin': 'Japan'},\n",
       "    {'Acceleration': 14.9, 'Horsepower': 63, 'Origin': 'USA'},\n",
       "    {'Acceleration': 16.2, 'Horsepower': 65, 'Origin': 'USA'},\n",
       "    {'Acceleration': 20.7, 'Horsepower': 65, 'Origin': 'USA'},\n",
       "    {'Acceleration': 14.2, 'Horsepower': 74, 'Origin': 'Europe'},\n",
       "    {'Acceleration': 14.4, 'Horsepower': 75, 'Origin': 'Japan'},\n",
       "    {'Acceleration': 16.8, 'Horsepower': 75, 'Origin': 'Japan'},\n",
       "    {'Acceleration': 14.8, 'Horsepower': 100, 'Origin': 'Japan'},\n",
       "    {'Acceleration': 18.3, 'Horsepower': 74, 'Origin': 'Japan'},\n",
       "    {'Acceleration': 20.4, 'Horsepower': 80, 'Origin': 'Europe'},\n",
       "    {'Acceleration': 19.6, 'Horsepower': 76, 'Origin': 'Europe'},\n",
       "    {'Acceleration': 12.6, 'Horsepower': 116, 'Origin': 'Japan'},\n",
       "    {'Acceleration': 13.8, 'Horsepower': 120, 'Origin': 'Japan'},\n",
       "    {'Acceleration': 15.8, 'Horsepower': 110, 'Origin': 'USA'},\n",
       "    {'Acceleration': 19, 'Horsepower': 105, 'Origin': 'USA'},\n",
       "    {'Acceleration': 17.1, 'Horsepower': 88, 'Origin': 'USA'},\n",
       "    {'Acceleration': 16.6, 'Horsepower': 85, 'Origin': 'USA'},\n",
       "    {'Acceleration': 19.6, 'Horsepower': 88, 'Origin': 'USA'},\n",
       "    {'Acceleration': 18.6, 'Horsepower': 88, 'Origin': 'USA'},\n",
       "    {'Acceleration': 18, 'Horsepower': 88, 'Origin': 'USA'},\n",
       "    {'Acceleration': 16.2, 'Horsepower': 85, 'Origin': 'USA'},\n",
       "    {'Acceleration': 16, 'Horsepower': 84, 'Origin': 'USA'},\n",
       "    {'Acceleration': 18, 'Horsepower': 90, 'Origin': 'USA'},\n",
       "    {'Acceleration': 16.4, 'Horsepower': 92, 'Origin': 'USA'},\n",
       "    {'Acceleration': 15.3, 'Horsepower': 74, 'Origin': 'Europe'},\n",
       "    {'Acceleration': 18.2, 'Horsepower': 68, 'Origin': 'Japan'},\n",
       "    {'Acceleration': 17.6, 'Horsepower': 68, 'Origin': 'Japan'},\n",
       "    {'Acceleration': 14.7, 'Horsepower': 63, 'Origin': 'USA'},\n",
       "    {'Acceleration': 17.3, 'Horsepower': 70, 'Origin': 'USA'},\n",
       "    {'Acceleration': 14.5, 'Horsepower': 88, 'Origin': 'Japan'},\n",
       "    {'Acceleration': 14.5, 'Horsepower': 75, 'Origin': 'Japan'},\n",
       "    {'Acceleration': 16.9, 'Horsepower': 70, 'Origin': 'Japan'},\n",
       "    {'Acceleration': 15, 'Horsepower': 67, 'Origin': 'Japan'},\n",
       "    {'Acceleration': 15.7, 'Horsepower': 67, 'Origin': 'Japan'},\n",
       "    {'Acceleration': 16.2, 'Horsepower': 67, 'Origin': 'Japan'},\n",
       "    {'Acceleration': 16.4, 'Horsepower': 110, 'Origin': 'USA'},\n",
       "    {'Acceleration': 17, 'Horsepower': 85, 'Origin': 'USA'},\n",
       "    {'Acceleration': 14.5, 'Horsepower': 92, 'Origin': 'USA'},\n",
       "    {'Acceleration': 14.7, 'Horsepower': 112, 'Origin': 'USA'},\n",
       "    {'Acceleration': 13.9, 'Horsepower': 96, 'Origin': 'Japan'},\n",
       "    {'Acceleration': 13, 'Horsepower': 84, 'Origin': 'USA'},\n",
       "    {'Acceleration': 17.3, 'Horsepower': 90, 'Origin': 'USA'},\n",
       "    {'Acceleration': 15.6, 'Horsepower': 86, 'Origin': 'USA'},\n",
       "    {'Acceleration': 24.6, 'Horsepower': 52, 'Origin': 'Europe'},\n",
       "    {'Acceleration': 11.6, 'Horsepower': 84, 'Origin': 'USA'},\n",
       "    {'Acceleration': 18.6, 'Horsepower': 79, 'Origin': 'USA'},\n",
       "    {'Acceleration': 19.4, 'Horsepower': 82, 'Origin': 'USA'}]},\n",
       "  'encoding': {'color': {'field': 'Origin', 'type': 'nominal'},\n",
       "   'x': {'field': 'Acceleration',\n",
       "    'scale': {'domain': [8, 24.8]},\n",
       "    'type': 'quantitative'},\n",
       "   'y': {'field': 'Horsepower',\n",
       "    'scale': {'domain': [46, 230]},\n",
       "    'type': 'quantitative'}},\n",
       "  'height': 150,\n",
       "  'mark': 'circle',\n",
       "  'selection': {'selector024': {'bind': 'scales',\n",
       "    'encodings': ['x', 'y'],\n",
       "    'type': 'interval'}},\n",
       "  'width': 160}}"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df[\"type\"]==\"clickVis\"].param.values[0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Code Analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "codeList = df.code.unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "luxKeywords = ['set_intent', 'Vis','VisList']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['df.set_intent([lux.VisSpec(attribute = \"Acceleration\"),lux.VisSpec(attribute = \"Horsepower\")])\\ndf',\n",
       " 'df.set_intent([lux.VisSpec(attribute = \"Cylinders\")])\\ndf',\n",
       " 'df.set_intent([lux.VisSpec(attribute = \"Origin\")])\\ndf']"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "list(filter(lambda x: 'set_intent' in str(x), codeList))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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  "history": [],
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
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  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.9"
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