exploring_CAN_data.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Exploring OBD-II CAN Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 522,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "from collections import Counter"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Sequence of Events \n",
    "(File \"0000286.TXT\", 286_video.mov for reference):\n",
    "```\n",
    "Battery and Engine On (~10 second mark in video)\n",
    "Logger On (unclear on *exact* timing, somewhere between battery on and first event)\n",
    "\n",
    "    Event     time in video    time after previous event     timestamp guess\n",
    "   Park           ~27s            ~10 seconds later             ~17:29:31\n",
    "   Drive          ~35s            ~8  seconds later             ~17:29:39\n",
    "   Park           ~39s            ~4  seconds later             ~17:29:43\n",
    "   Drive          ~45s            ~6  seconds later             ~17:29:49\n",
    "   Accelerate     ~50s            ~5  seconds later             ~17:29:54\n",
    "   Right Turn     ~60s            ~10 seconds later             ~17:30:04\n",
    "   ~20 mph\n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Available CAN Channels\n",
    "\n",
    "According to [This Vehicle CAN Database Reference for the Volvo S60](http://www.racelogic.co.uk/_downloads/vbox/Vehicles/Other/Docs/Volvo-S60%202010-.pdf), the available CAN channels and their units are:\n",
    "```\n",
    "* Accelerator Pedal Position (%)\n",
    "* Brake Position (%)\n",
    "* Engine Speed (rpm)\n",
    "* Steering Angle (degrees)\n",
    "* Wheel Speed FL (km/h)\n",
    "* Wheel Speed FR (km/h)\n",
    "* Wheel Speed RL (km/h)\n",
    "* Wheel Speed RR (km/h)\n",
    "```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 523,
   "metadata": {},
   "outputs": [],
   "source": [
    "# pull log into a dataframe\n",
    "logfile = open(\"0000286.TXT\", \"r\")\n",
    "df = pd.read_csv(logfile, sep=' ', parse_dates=True, infer_datetime_format=True, skiprows=17, header=None, names = [\"Date\", \"Timestamp\", \"Type\", \"ID\", \"Data\"])\n",
    "logfile.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 524,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
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       "      <td>04/25</td>\n",
       "      <td>17:30:02:158</td>\n",
       "      <td>1</td>\n",
       "      <td>1400006</td>\n",
       "      <td>0000000003f70262</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>55375</th>\n",
       "      <td>04/25</td>\n",
       "      <td>17:30:02:159</td>\n",
       "      <td>1</td>\n",
       "      <td>1600012</td>\n",
       "      <td>800005076e5dc81a</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>55376</th>\n",
       "      <td>04/25</td>\n",
       "      <td>17:30:02:164</td>\n",
       "      <td>1</td>\n",
       "      <td>c0402a</td>\n",
       "      <td>1ee4000027ffd3ff</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>55377</th>\n",
       "      <td>04/25</td>\n",
       "      <td>17:30:02:164</td>\n",
       "      <td>1</td>\n",
       "      <td>d00022</td>\n",
       "      <td>0000000000000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>55378 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        Date     Timestamp  Type        ID              Data\n",
       "0      04/25  17:29:21:398     1    c0402a  1ee4000027ffd3ff\n",
       "1      04/25  17:29:21:398     1    d00022  0000000000000000\n",
       "2      04/25  17:29:21:398     1  11000022  01bbcc000055034a\n",
       "3      04/25  17:29:21:399     1  11420006  0000000f0000c000\n",
       "4      04/25  17:29:21:400     1    42406c  408a40006000c00f\n",
       "5      04/25  17:29:21:400     1   1000020  00000001cc04c000\n",
       "6      04/25  17:29:21:402     1    62401e  02144440fa156400\n",
       "7      04/25  17:29:21:402     1    80401e  06000366002b7a00\n",
       "8      04/25  17:29:21:403     1    a20016  0519840000636300\n",
       "9      04/25  17:29:21:403     1    f00006  000032000000e000\n",
       "10     04/25  17:29:21:403     1   1400006  0000000003f30215\n",
       "11     04/25  17:29:21:405     1    224024  1c48440013ff0000\n",
       "12     04/25  17:29:21:406     1    e24026  0001e0408f000000\n",
       "13     04/25  17:29:21:406     1  10004002  80000000c000c000\n",
       "14     04/25  17:29:21:406     1  10800006  0080006f00000100\n",
       "15     04/25  17:29:21:407     1  10e24000  0000000000000000\n",
       "16     04/25  17:29:21:408     1    c0402a  1ee4000027ffd3ff\n",
       "17     04/25  17:29:21:408     1    d00022  0000000000000000\n",
       "18     04/25  17:29:21:410     1    42406c  80e800006000c00f\n",
       "19     04/25  17:29:21:410     1   1000020  00000001cc04c000\n",
       "20     04/25  17:29:21:410     1  10400020  802947689b370db5\n",
       "21     04/25  17:29:21:412     1    62401e  02146440fa156400\n",
       "22     04/25  17:29:21:412     1    80401e  1e00036a002b7a00\n",
       "23     04/25  17:29:21:412     1    a20016  0019840000636300\n",
       "24     04/25  17:29:21:413     1    f00006  000032000000e000\n",
       "25     04/25  17:29:21:413     1   1400006  0000000003f30215\n",
       "26     04/25  17:29:21:413     1   1600012  000005076e5dc81a\n",
       "27     04/25  17:29:21:418     1    c0402a  1ee4000027ffd3ff\n",
       "28     04/25  17:29:21:418     1    d00022  0000000000000000\n",
       "29     04/25  17:29:21:419     1    224024  1848440013ff0000\n",
       "...      ...           ...   ...       ...               ...\n",
       "55348  04/25  17:30:02:142     1    e24026  0001e0408f0e0543\n",
       "55349  04/25  17:30:02:142     1  10004002  82b5036cc2f9c3b6\n",
       "55350  04/25  17:30:02:143     1  10e24000  0000000000000000\n",
       "55351  04/25  17:30:02:144     1    c0402a  1ee4000027ffd3ff\n",
       "55352  04/25  17:30:02:144     1    d00022  0000000000000000\n",
       "55353  04/25  17:30:02:147     1    42406c  00a800006000c00f\n",
       "55354  04/25  17:30:02:147     1    62401e  02604591ea6264d5\n",
       "55355  04/25  17:30:02:148     1    80401e  0608066400247200\n",
       "55356  04/25  17:30:02:148     1    a20016  011984d50069f700\n",
       "55357  04/25  17:30:02:148     1    f00006  000032000000e000\n",
       "55358  04/25  17:30:02:149     1   1000020  00000001cc04c000\n",
       "55359  04/25  17:30:02:149     1   1400006  0000000003f70262\n",
       "55360  04/25  17:30:02:149     1  10400020  002947659b370dc8\n",
       "55361  04/25  17:30:02:149     1  10800006  006f006f8daca0bd\n",
       "55362  04/25  17:30:02:154     1    c0402a  1ee4000027ffd3ff\n",
       "55363  04/25  17:30:02:154     1    d00022  0000000000000000\n",
       "55364  04/25  17:30:02:155     1  11000022  01bbcc0d0056034a\n",
       "55365  04/25  17:30:02:156     1    224024  184800089bff0000\n",
       "55366  04/25  17:30:02:156     1    e24026  000160408f0e053e\n",
       "55367  04/25  17:30:02:156     1  10004002  82b20379c312c3c2\n",
       "55368  04/25  17:30:02:157     1    62401e  02604594ea6264d5\n",
       "55369  04/25  17:30:02:157     1    80401e  1e08066400247200\n",
       "55370  04/25  17:30:02:157     1    42406c  408800006000c00f\n",
       "55371  04/25  17:30:02:158     1    a20016  001984d50069f700\n",
       "55372  04/25  17:30:02:158     1    f00006  000032000000e000\n",
       "55373  04/25  17:30:02:158     1   1000020  00000001cc04c000\n",
       "55374  04/25  17:30:02:158     1   1400006  0000000003f70262\n",
       "55375  04/25  17:30:02:159     1   1600012  800005076e5dc81a\n",
       "55376  04/25  17:30:02:164     1    c0402a  1ee4000027ffd3ff\n",
       "55377  04/25  17:30:02:164     1    d00022  0000000000000000\n",
       "\n",
       "[55378 rows x 5 columns]"
      ]
     },
     "execution_count": 524,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Pruning log to most important fields"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 525,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[1]"
      ]
     },
     "execution_count": 525,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "list(df.Type.unique())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From *\"CLX000_Manual_FW_5.7X.pdf\"*:\n",
    "\n",
    "**Type**:\n",
    "\n",
    "```Message type indicator. Is either 0 (received standard), 1 (received extended), 8 (transmitted standard) or 9 (transmitted extended) in the log file```\n",
    "\n",
    "So every row in this file has Type=1, 'received extended' - extended CAN frames are 8 bytes each"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 526,
   "metadata": {},
   "outputs": [],
   "source": [
    "# So we don't need Type or Date (all same day)\n",
    "del df['Date']\n",
    "del df['Type']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 527,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "        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>Timestamp</th>\n",
       "      <th>ID</th>\n",
       "      <th>Data</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>17:29:21:398</td>\n",
       "      <td>c0402a</td>\n",
       "      <td>1ee4000027ffd3ff</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>17:29:21:398</td>\n",
       "      <td>d00022</td>\n",
       "      <td>0000000000000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>17:29:21:398</td>\n",
       "      <td>11000022</td>\n",
       "      <td>01bbcc000055034a</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>17:29:21:399</td>\n",
       "      <td>11420006</td>\n",
       "      <td>0000000f0000c000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>17:29:21:400</td>\n",
       "      <td>42406c</td>\n",
       "      <td>408a40006000c00f</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>17:29:21:400</td>\n",
       "      <td>1000020</td>\n",
       "      <td>00000001cc04c000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>17:29:21:402</td>\n",
       "      <td>62401e</td>\n",
       "      <td>02144440fa156400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>17:29:21:402</td>\n",
       "      <td>80401e</td>\n",
       "      <td>06000366002b7a00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>17:29:21:403</td>\n",
       "      <td>a20016</td>\n",
       "      <td>0519840000636300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>17:29:21:403</td>\n",
       "      <td>f00006</td>\n",
       "      <td>000032000000e000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Timestamp        ID              Data\n",
       "0  17:29:21:398    c0402a  1ee4000027ffd3ff\n",
       "1  17:29:21:398    d00022  0000000000000000\n",
       "2  17:29:21:398  11000022  01bbcc000055034a\n",
       "3  17:29:21:399  11420006  0000000f0000c000\n",
       "4  17:29:21:400    42406c  408a40006000c00f\n",
       "5  17:29:21:400   1000020  00000001cc04c000\n",
       "6  17:29:21:402    62401e  02144440fa156400\n",
       "7  17:29:21:402    80401e  06000366002b7a00\n",
       "8  17:29:21:403    a20016  0519840000636300\n",
       "9  17:29:21:403    f00006  000032000000e000"
      ]
     },
     "execution_count": 527,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Here's what our dataframe looks like now\n",
    "df.head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Examining IDs and Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 528,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "There are 55378 rows in this log\n",
      "There are 8423 unique Data values in this log\n",
      "There are 27 unique ID values in this log\n"
     ]
    }
   ],
   "source": [
    "id_vals = list(df.ID.unique())\n",
    "data_vals = list(df.Data.unique())\n",
    "\n",
    "print(\"There are \"    +str(len(df))          +\" rows in this log\")\n",
    "print(\"There are \"    +str(len(data_vals))   +\" unique Data values in this log\")\n",
    "print(\"There are \"    +str(len(id_vals))     +\" unique ID values in this log\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 529,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('62401e', 4078),\n",
       " ('80401e', 4078),\n",
       " ('a20016', 4078),\n",
       " ('f00006', 4078),\n",
       " ('1400006', 4078),\n",
       " ('42406c', 4077),\n",
       " ('1000020', 4077),\n",
       " ('c0402a', 4076),\n",
       " ('d00022', 4076),\n",
       " ('224024', 2910)]"
      ]
     },
     "execution_count": 529,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Most common ID values\n",
    "id_counter = Counter(df.ID)\n",
    "id_counter.most_common(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 530,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('0000000000000000', 4862),\n",
       " ('000032000000e000', 4078),\n",
       " ('00000001cc04c000', 3277),\n",
       " ('1ee4000027ffd3ff', 2726),\n",
       " ('80000000c000c000', 1872),\n",
       " ('000160408f000000', 1405),\n",
       " ('1ee4000007ffc7ff', 1121),\n",
       " ('00000001cb04c000', 800),\n",
       " ('00a800006000c00f', 521),\n",
       " ('000005076e5dc81a', 510)]"
      ]
     },
     "execution_count": 530,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Most common Data values\n",
    "data_counter = Counter(df.Data)\n",
    "data_counter.most_common(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Let's take the most common ID and see what happens to the data associated with this ID over time"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 490,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Timestamp</th>\n",
       "      <th>ID</th>\n",
       "      <th>Data</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>17:29:21:402</td>\n",
       "      <td>62401e</td>\n",
       "      <td>02144440fa156400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>17:29:21:412</td>\n",
       "      <td>62401e</td>\n",
       "      <td>02146440fa156400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>17:29:21:422</td>\n",
       "      <td>62401e</td>\n",
       "      <td>02144440fa156400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>17:29:21:432</td>\n",
       "      <td>62401e</td>\n",
       "      <td>02144440fa156400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>61</th>\n",
       "      <td>17:29:21:442</td>\n",
       "      <td>62401e</td>\n",
       "      <td>02144440fa156400</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Timestamp      ID              Data\n",
       "6   17:29:21:402  62401e  02144440fa156400\n",
       "21  17:29:21:412  62401e  02146440fa156400\n",
       "35  17:29:21:422  62401e  02144440fa156400\n",
       "44  17:29:21:432  62401e  02144440fa156400\n",
       "61  17:29:21:442  62401e  02144440fa156400"
      ]
     },
     "execution_count": 490,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2 = df[df['ID'] == '62401e']\n",
    "df2.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 502,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1108c12e8>"
      ]
     },
     "execution_count": 502,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df2.plot(x=\"Timestamp\", y=\"Data\", title=\"Data with ID=62401e over time\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can see the graph spikes at the end. Referring back to the sequence of live events, we know that the car was sitting still switching back from park to drive before accelerating at the end of the logged period. So this could be 'Engine Speed'.\n",
    "\n",
    "Let's look at the other common IDs and see if a clearer picture emerges."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 620,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Timestamp</th>\n",
       "      <th>ID</th>\n",
       "      <th>Data</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>17:29:21:402</td>\n",
       "      <td>80401e</td>\n",
       "      <td>06000366002b7a00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>17:29:21:412</td>\n",
       "      <td>80401e</td>\n",
       "      <td>1e00036a002b7a00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>17:29:21:422</td>\n",
       "      <td>80401e</td>\n",
       "      <td>0601036a002b7a00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>17:29:21:432</td>\n",
       "      <td>80401e</td>\n",
       "      <td>1e01036a002b7a00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>62</th>\n",
       "      <td>17:29:21:442</td>\n",
       "      <td>80401e</td>\n",
       "      <td>06000367002b7a00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Timestamp      ID              Data\n",
       "7   17:29:21:402  80401e  06000366002b7a00\n",
       "22  17:29:21:412  80401e  1e00036a002b7a00\n",
       "36  17:29:21:422  80401e  0601036a002b7a00\n",
       "45  17:29:21:432  80401e  1e01036a002b7a00\n",
       "62  17:29:21:442  80401e  06000367002b7a00"
      ]
     },
     "execution_count": 620,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df3 = df[df['ID'] == '80401e']\n",
    "df3.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 629,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/anaconda3/lib/python3.6/site-packages/ipykernel_launcher.py:1: 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: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  \"\"\"Entry point for launching an IPython kernel.\n"
     ]
    }
   ],
   "source": [
    "df3['IntData'] = df3.Data.apply(int, base=16)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 626,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1a1dd45048>"
      ]
     },
     "execution_count": 626,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df3.plot(x=\"Timestamp\", y=\"IntData\", title=\"Data with ID=62401e over time\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 634,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/anaconda3/lib/python3.6/site-packages/ipykernel_launcher.py:4: 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: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  after removing the cwd from sys.path.\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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BDnczswQ53M3MEuRwNzNLkMPdzCxBDnczswQ53M3MEuRwNzNLkMPdzCxBDnczswQ53M3MEuRwNzNLkMPdzCxBDnczswQ53M3MEuRwNzNLkMPdzCxBDnczswQ53M3MEuRwNzNLkMPdzCxBDnczswQ53M3MEuRwNzNLkMPdzCxBDnczswQ53M3MEjS6SCNJm4FdwB6gPyJKNdqcDlwFjAG2R8S81pVpZmaDUSjcc/MjYnutBZIOBK4F3hcRT0k6uCXVmZlZU1o1LPNR4MaIeAogIra1qF8zM2tC0XAPYJWktZI6ayyfDhwk6Y68zcdqdSKpU1KPpJ7e3t5mazYzswaKDsvMjYit+XDL7ZIejog1Vf2cBJwBvAn4iaR7IuLRyk4iYhmwDKBUKsXQyzczs1oK7blHxNb85zagG5hT1WQLsDIiXszH5dcAs1pZqJmZFdcw3CWNk9RRngbOAjZUNbsZeK+k0ZLeDLwbeKjVxZqZWTFFhmWmAN2Syu1XRMRKSYsBIqIrIh6StBJ4ANgLfDsiql8AzMzsdaKI9gx9l0ql6Onpacu2zcz2VZLW1vqsUTV/QtXMLEEOdzOzBDnczcwS5HA3M0uQw93MLEEOdzOzBDnczcwS5HA3M0uQw93MLEEOdzOzBDnczcwS5HA3M0uQw93MLEEOdzOzBDnczcwS5HA3M0uQw93MLEEOdzOzBDnczcwS5HA3M0uQw93MLEEOdzOzBDnczcwS5HA3M0uQw93MLEEOdzOzBDnczcwSNLpII0mbgV3AHqA/IkpVy08HbgaeyGfdGBGXt65MMzMbjELhnpsfEdsHWH5XRJw91ILMzGzoPCxjZpagonvuAaySFMC3ImJZjTbvkXQ/sBX4XERsrG4gqRPoBDj88MObKnjtkzv59l1PNG5oZjZCnXX8FM773anDuo2i4T43IrZKOhi4XdLDEbGmYvnPgCMiok/S+4GbgLdXd5K/KCwDKJVK0UzBfa/s4fHevmZWNTMbEXb0HTTs2ygU7hGxNf+5TVI3MAdYU7H8hYrpf5F0raRJDcbomzJv+mTmTZ/X6m7NzJLScMxd0jhJHeVp4CxgQ1WbQyQpn56T97uj9eWamVkRRfbcpwDdeXaPBlZExEpJiwEiogtYCHxSUj/wMvDhiGhq2MXMzIauYbhHxCZgVo35XRXT1wDXtLY0MzNrlk+FNDNLkMPdzCxBDnczswQ53M3MEuRwNzNLkMPdzCxBDnczswQ53M3MEuRwNzNLkMPdzCxBDnczswQ53M3MEuRwNzNLkMPdzCxBDnczswQ53M3MEuRwNzNLkMPdzCxBDnczswQ53M3MEuRwNzNLkMPdzCxBDnczswQ53M3MEuRwNzNLkMPdzCxBDnczswQVCndJmyWtl7ROUs8A7d4laY+kha0r0czMBmv0INrOj4jt9RZK2g+4AvjXIVdlZmZD0sphmc8A3we2tbBPMzNrQtFwD2CVpLWSOqsXSjoMOA/oGqgTSZ2SeiT19Pb2Dr5aMzMrpGi4z42IE4EFwKcknVa1/CpgSUTsGaiTiFgWEaWIKE2ePLmJcs3MrIhCY+4RsTX/uU1SNzAHWFPRpAR8RxLAJOD9kvoj4qYW12tmZgU0DHdJ44BREbErnz4LuLyyTUQcWdF+OXCrg93MrH2K7LlPAbrzvfLRwIqIWClpMUBEDDjObmZmr7+G4R4Rm4BZNebXDPWI+PjQyzIzs6HwJ1TNzBLkcDczS5DD3cwsQQ53M7MEOdzNzBLkcDczS5DD3cwsQQ53M7MEOdzNzBLkcDczS5DD3cwsQQ53M7MEOdzNzBLkcDczS5DD3cwsQQ53M7MEOdzNzBLkcDczS5DD3cwsQQ53M7MEOdzNzBLkcDczS5DD3cwsQQ53M7MEOdzNzBLkcDczS5DD3cwsQYXCXdJmSeslrZPUU2P5ByU9UF4u6dTWl2pmZkWNHkTb+RGxvc6yHwK3RERIeifwXWDGkKszM7OmDCbc64qIvoq744BoRb9mZtacomPuAayStFZSZ60Gks6T9DBwG/Dnddp05sM2Pb29vc1VbGZmDRUN97kRcSKwAPiUpNOqG0REd0TMAM4FvlKrk4hYFhGliChNnjy56aLNzGxghcI9IrbmP7cB3cCcAdquAY6WNKklFZqZ2aA1DHdJ4yR1lKeBs4ANVW2OkaR8+kTgd4AdrS/XzMyKKPKG6hSgO8/u0cCKiFgpaTFARHQB/xn4mKRXgZeBD0WE31Q1M2sTtSuDS6VS9PS85pR5MzMbgKS1EVFq1M6fUDUzS5DD3cwsQQ53M7MEOdzNzBLkcDczS5DD3cwsQQ53M7MEOdzNzBLkcDczS5DD3cwsQQ53M7MEOdzNzBLkcDczS5DD3cwsQQ53M7MEOdzNzBLkcDczS5DD3cwsQQ53M7MEOdzNzBLkcDczS5DD3cwsQQ53M7MEOdzNzBLkcDczS5DD3cwsQQ53M7MEjS7SSNJmYBewB+iPiFLV8kXAkvxuH/DJiLi/hXWamdkgFAr33PyI2F5n2RPAvIj4laQFwDLg3UOuzszMmjKYcK8rIn5ccfceYGor+jUzs+YUHXMPYJWktZI6G7T9C+AHtRZI6pTUI6mnt7d3MHWamdkgFN1znxsRWyUdDNwu6eGIWFPdSNJ8snA/tVYnEbGMbMiGUqkUTdZsZmYNKGJwGSvpS0BfRHyjav47gW5gQUQ8WqCfXuDJQW38tyYB9cb/22kk1uWaihuJdbmm4kZiXcNR0xERMblRo4Z77pLGAaMiYlc+fRZweVWbw4EbgT8pEuwARYoboKae6jN2RoKRWJdrKm4k1uWaihuJdbWzpiLDMlOAbknl9isiYqWkxQAR0QVcCkwErs3bveZ0STMze/00DPeI2ATMqjG/q2L6E8AnWluamZk1a1/9hOqydhdQx0isyzUVNxLrck3FjcS62lbToN9QNTOzkW9f3XM3M7MBONzNzBLkcDczS5DD3cwsQQ53M7MEOdzNzBLUkkv+mg03SROBH+Z3DyH74pjypUVfiohThmm704BTImLFcPRvNlx8nrvtc+pdvG6YtnU68LmIOHu4t2XWSh6WsX2epL785+mS7pT0XUmPSloqaZGkeyWtl3R03m6ypO9L+ml+m5vPnydpXX67T1IHsBR4bz7vQknTJN0l6Wf57ZRBbnu5pK68j0cl+UXDhoWHZSw1s4BjgZ3AJuDbETFH0n8FPgN8Fvgm8PcR8aP8iqb/mq/zOeBTEXG3pPHAbuBiKvbcJb0Z+L2I2C3p7cANQGkQ2waYBswDjgZWSzomInYP31Nib0QOd0vNTyPilwCSHgdW5fPXA/Pz6TOB4/IrmAJMyPfS7waulHQ9cGNEbKloUzYGuEbSbLJx/+mD3DbAdyNiL/CYpE3ADGDdEB6z2Ws43C01r1RM7624v5ff/r2PAt4TES9XrbtU0m3A+4F7JJ1Zo/8LgWfJ9tJHke3dD2bbkH1tJQPcNxsyj7nbG9Eq4NPlO/leOJKOjoj1EXEF0EO2R70L6KhY9wDgl/me958A+zWx/T+UNCofhz8KeKS5h2FWn8Pd3oguAEqSHpD0ILA4n/9ZSRsk3Q+8TPZF7w8A/ZLul3QhcC3wp5LuIRuSebGJ7T8C3Jn3v9jj7TYcfCqk2etI0nLg1oj4XrtrsbR5z93MLEHeczczS5D33M3MEuRwNzNLkMPdzCxBDnczswQ53M3MEvT/AX/gcHfB6H9RAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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CDhRmZlbIgcKaUpkSvGUaq7UmBwozMyvkQGFNp4RvzEbOZlsTc6AwM7NCDhRmZlbIgcKakhO8Zs3DgcLMzAo5UFgTKl9it4wJfGsdDhRmZlbIgcLMzAo5UJiZWSEHCmtKZbroKXyJlzU5BwprOmVM7JZwyNZCHCjMzKyQA4WZmRVyoDAzs0IOFNaUypTgLc9IrVU5UFjTKWNit4wJfGsdDhRmZlaoz0AhabykJbnbRkkXV9W5NLd/maQdkg5J+26Q9JykZVVtrpb0H5KWSrpd0kGpvF3Si7njzRnIAZuZWWP6DBQRsTwiOiKiAzgD2ArcXlXn6lydzwILIqIn7Z4LTK5x6F8Ap0TEacBvUruKlZXjRcTshkdlZmYDptGlp3PInsTXFNSZCcyrbETEvUBPdaWIuDsitqfNB4BxDfbFbEgoUd7eWlSjgWIGuSBQTdKBZLOH2xo87v8EfprbPlrSI5IWSHpnL+eaJWmRpEXd3d0Nns7MzOpVd6CQtB9wHnBrQbWpwMLcslM9x/0bYDtwcyp6FnhDRLwZ+DRwi6RR1e0i4vqI6IyIzra2tnpPZy2gjFcAqZTXelmraGRGMQV4OCLWFdQpnHFUk3QhcC5wQaQL5yPipYhYn+4vBlYCJzTQTzMzG0CNBIqdcg/VJI0GJgJ31HMwSZOBzwDnRcTWXHmbpGHp/jHA8cCqBvppZmYDqK5AkXIPk4Af5cpmS8pfkTQduDsitlS1nQfcD4yX1CXpo2nXtcBI4BdVl8G+C1gq6VHgh8DsRpaybGgoU4I3/N5sa3L71lMpveIfU1U2p2p7LtmlsNVtZ/ZyzON6Kb+NxpPhZma2h/id2dZ0ypjYLWMC31qHA4WZmRVyoDAzs0IOFNaUypTgLVPi3lqTA4WZmRVyoLCmU8bEbhnHbK3DgcLMzAo5UJiZWSEHCmtKZUrwlmio1qIcKMzMrJADhTWdciZ2SzloaxEOFGZmVsiBwszMCjlQmJlZIQcKa0pluhKoTFd4WWtyoLCm448ZN2suDhRmZlbIgcLMzAo5UJiZWSEHCmtKUaoMb5nGaq2oz0AhabykJbnbRkkXV9W5NLd/maQdkg5J+26Q9JykZVVtDpH0C0lPpZ8Hp3JJ+idJKyQtlfRHAzlgMzNrTJ+BIiKWR0RHRHQAZwBbgdur6lydq/NZYEFE9KTdc4HJNQ59GfDLiDge+GXaBpgCHJ9us4BvNTwqa20lvAKohEO2FtLo0tM5wMqIWFNQZyYwr7IREfcCPTXqTQO+l+5/Dzg/V35jZB4ADpJ0eIP9NDOzAdJooJhBLghUk3Qg2ezhtjqO9fqIeBYg/Tw0lR8JPJ2r15XKzMxsENQdKCTtB5wH3FpQbSqwMLfs1B+1ZuG7ZPskzZK0SNKi7u7u3TidNaMypXdLlbe3ltTIjGIK8HBErCuoUzjjqLKusqSUfj6XyruAo3L1xgFrqxtHxPUR0RkRnW1tbXWe0szMGtVIoNgp91BN0mhgInBHnce7E7gw3b8w1+5O4CPp6qe3AhsqS1RWDmVM7PojPKyZ1RUoUu5hEvCjXNlsSbNz1aYDd0fElqq284D7gfGSuiR9NO26Cpgk6al07KtS+V3AKmAF8G3gEw2PyszMBsy+9VSKiK3AmKqyOVXbc8kuha1uO7OXY64nu4qqujyAT9bTLzMz2/P8zmxrTiVK8JZoqNaiHCjMzKyQA4U1HZUws1vG7+Cw1uFAYWZmhRwozMyskAOFNaUyJXjL9ZHq1oocKMzMrJADhTWdMqZ1S5i/txbiQGFmZoUcKMzMrJADhZmZFXKgsKZUpiuByjNSa1UOFNZ0ypjYLeGQrYU4UJiZWSEHCjMzK+RAYWZmhRworCmVKcFbory9tSgHCjMzK+RAYU2njFcAlfE7OKx1OFCYmVkhBwozMyvUZ6CQNF7Sktxto6SLq+pcmtu/TNIOSYekfZMlLZe0QtJluTb35dqslfQvqfwsSRty+z4/0IO25lemBG+Z3oVurWnfvipExHKgA0DSMOAZ4PaqOlcDV6c6U4FLIqIn1f8mMAnoAh6SdGdEPBER76y0l3QbcEfukPdFxLm7NTIzMxsQjS49nQOsjIg1BXVmAvPS/QnAiohYFRF/AL4PTMtXljQSeDfwLw32xYYoJ3bNmkujgWIGrwWBXUg6EJgM3JaKjgSezlXpSmV504FfRsTGXNnbJD0q6aeS3tTLuWZJWiRpUXd3d4PDMDOzetUdKCTtB5wH3FpQbSqwMCJ6Ks1q1KlekM3PQAAeBt4YEacD19DLTCMiro+IzojobGtrq2cIZmbWD43MKKYAD0fEuoI61TOOLuCo3PY4YG1lQ9IYsuWpn1TKImJjRGxO9+8Chksa20A/bQiIEr03uzwjtVbVSKCofuW/E0mjgYnsnJR+CDhe0tFpRjIDuDO3/0+AH0fEttxxDlNapJY0IfVxfQP9NDOzAdTnVU/wau5hEvDxXNlsgIiYk4qmA3dHxJZKnYjYLuki4OfAMOCGiHg8d+gZwFVVp/sA8OeStgMvAjPC1w+WShlT2c7fWzOrK1BExFZgTFXZnKrtucDcGm3vAu7q5bhn1Si7Fri2nn6Zmdme53dmm5lZIQcKa0qlWmws01itJTlQmJlZIQcKaz4lTOyqjIO2luFAYWZmhRwozMyskAOFNaUyJbNLNFRrUQ4UZmZWyIHCmk4ZE7t+Z7Y1MwcKMzMr5EBhZmaFHCjMzKyQA4XZIPOHI1uzc6CwplPGxG4Jh2wtxIHCzMwKOVCYmVkhBwozMyvkQGFNqUwJ3vKM1FqVA4WZmRVyoLCmU8YrgMp4pZe1jj4DhaTxkpbkbhslXVxV59Lc/mWSdkg6JO2bLGm5pBWSLsu1mSvpt7l2Halckv4p1V8q6Y8GetBmZla/ffuqEBHLgcqT+DDgGeD2qjpXA1enOlOBSyKiJ9X/JjAJ6AIeknRnRDyRml4aET+sOuUU4Ph0ewvwrfTTzMwGQaNLT+cAKyNiTUGdmcC8dH8CsCIiVkXEH4DvA9P6OMc04MbIPAAcJOnwBvtpLa5MCd4S5e2tRTUaKGbwWhDYhaQDgcnAbanoSODpXJWuVFbxpbS89HVJ+9fZpnKuWZIWSVrU3d3d4DDMzKxedQcKSfsB5wG3FlSbCiyMiJ5Ksxp1Kq+fPgucCPwxcAjwmTravFYQcX1EdEZEZ1tbWx0jsFZRxsSuyjhoaxmNzCimAA9HxLqCOtUzji7gqNz2OGAtQEQ8m5aXXgK+S7ZMVdjGzMz2vkYCRT73sAtJo4GJwB254oeA4yUdnWYkM4A7U/3D008B5wPLUps7gY+kq5/eCmyIiGcb6KeZmQ2gPq96gldzD5OAj+fKZgNExJxUNB24OyK2VOpExHZJFwE/B4YBN0TE42n3zZLayJaalgCzU/ldwPuAFcBW4H/0b2jWysqU4I1Spe6tFdUVKCJiKzCmqmxO1fZcYG6NtneRPflXl7+7l3MF8Ml6+mVmZnue35ltTUclfG92+UZsrcSBwszMCjlQmJlZIQcKa0plSvCWKXFvrcmBwszMCjlQWNMp5ZuUyzhmaxkOFGZmVsiBwszMCjlQmJlZIQcKa0pluhKoREO1FuVAYU2njMnsMr4b3VqHA4WZmRVyoDAzs0IOFGZmVsiBwppSqRK8pRqstSIHCjMzK+RAYU2ofFcAlfFKL2sdDhRmZlbIgcLMzAo5UFhTKtc7s0s0WGtJDhRmZlaoz0AhabykJbnbRkkXV9W5NLd/maQdkg5J+yZLWi5phaTLcm1uTuXLJN0gaXgqP0vShtzxPj/Qg7bmVsbEbgmHbC2kz0AREcsjoiMiOoAzgK3A7VV1rs7V+SywICJ6JA0DvglMAU4GZko6OTW7GTgROBV4HfCx3CHvqxwvIr64m2M0M7Pd0OjS0znAyohYU1BnJjAv3Z8ArIiIVRHxB+D7wDSAiLgrEuBBYFyDfTEzs72g0UAxg9eCwC4kHQhMBm5LRUcCT+eqdKWyfJvhwIeBn+WK3ybpUUk/lfSmXs41S9IiSYu6u7sbHIY1v/IkeMuUuLfWVHegkLQfcB5wa0G1qcDCiOipNKtRp/rf4jrg3oi4L20/DLwxIk4HrgH+pdaJIuL6iOiMiM62trZ6h2FmZg1qZEYxBXg4ItYV1KmecXQBR+W2xwFrKxuSLgfagE9XyiJiY0RsTvfvAoZLGttAP63FlTGxW8YEvrWORgJFPvewC0mjgYnAHbnih4DjJR2dZiQzgDtT/Y8B7wVmRsQrueMcJmX/NpImpD6ub6CfZmY2gPatp1LKPUwCPp4rmw0QEXNS0XTg7ojYUqkTEdslXQT8HBgG3BARj6fdc4A1wP0pLvwoXeH0AeDPJW0HXgRmpIR3Q15++WW6urrYtm1bo01L4YADDmDcuHEMHz58sLtiZk2urkAREVuBMVVlc6q25wJza7S9C7irRnnNc0fEtcC19fSrSFdXFyNHjqS9vR15Xr+TiGD9+vV0dXVx9NFHD3Z3aipTgrdEQ7UWNWTfmb1t2zbGjBnjIFGDJMaMGePZlpnVZcgGCsBBokAzPzZN3LU9RqVM4VurGNKBwszMdp8DxR40YsSIPut84xvfYOvWra9ut7e3c+qpp3Lqqady8skn87nPfY6XXnqp8BgvvPAC11133W7318ysFgeKQVYdKADmz5/PY489xoMPPsiqVauYNWtW4TGGYqAoU4K3Hxf1me1VdV311Oq+8K+P88TajQN6zJOPGMXlU2t+usgu7rnnHq644grGjh3LsmXLOOOMM7jpppu45pprWLt2LWeffTZjx45l/vz5O7UbMWIEc+bM4aijjqKnp4f99tuPadOm8fvf/56XX36ZK6+8kmnTpnHZZZexcuVKOjo6mDRpEpdffnnNemZm/VGKQNEMHnnkER5//HGOOOIIzjzzTBYuXMinPvUpvva1rzF//nzGjq395vNRo0Zx9NFH89RTT3HGGWdw++23M2rUKJ5//nne+ta3ct5553HVVVexbNkylixZAsD27dtr1mvmBHZeGRO7LfKrsZIqRaCo95X/njRhwgTGjcs+ILejo4PVq1fzjne8o662laWJiOCv//qvuffee9lnn3145plnWLdu109U6a3eYYcdNnADMrPSKEWgaAb777//q/eHDRvG9u3b62q3adMmVq9ezQknnMDNN99Md3c3ixcvZvjw4bS3t9d8L0S99czM6uFk9iAbOXIkmzZtqrlv8+bNfOITn+D888/n4IMPZsOGDRx66KEMHz6c+fPns2bNmprH6K2emVl/eEYxyGbNmsWUKVM4/PDDX01mn3322UQEr7zyCtOnT+dv//ZvAbjggguYOnUqnZ2ddHR0cOKJJwIwZswYzjzzTE455RSmTJnCZz7zmZr1WskLW//ApK8tGOxu7BWv+KIna3IaCpfmdXZ2xqJFi3Yqe/LJJznppJMGqUetoVkfo0Wre/juwtVESS6S3Udi9sRjOeXI0YPdFSsZSYsjorOvep5RWNPpbD+EzvZDBrsbZpY4R2FmZoWGdKAYCstqe4ofGzOr15ANFAcccADr16/3E2INle+jOOCAAwa7K2bWAoZsjmLcuHF0dXXR3d092F1pSpVvuDMz68uQDRTDhw9v2m9vMzNrJUN26cnMzAaGA4WZmRVyoDAzs0JD4p3ZkrqB3flAo7HA8wPUnYHiPtWvGfvlPtWvGftVlj69MSLa+qo0JALF7pK0qJ63se9N7lP9mrFf7lP9mrFf7tPOvPRkZmaFHCjMzKyQA0Xm+sHuQA3uU/2asV/uU/2asV/uU45zFGZmVsgzCjMzK+RAYWZmhRwozMyskAOFmZkVcqAwM7NCDhRmZlZoyH4fhVlvJI0Bfpk2DwN2AJVvuNoaEW/fQ+dtB94eEbfsieOb7Sl+H4WVmqQrgM0R8dW9cK6zgL+KiHP39LnMBpKXnsxyJG1OP8+StEDSDyT9RtJVki6Q9KCkxyQdm+q1SbpN0kPpdmYqnyhpSbo9ImkkcBXwzlR2iaR2SfdJejjd3t7guedKmpOO8RtJDkC2R3jpyax3pwMnAT3AKuD/RsQESf8b+AvgYuAfga9HxK8kvQH4eWrzV8AnI2KhpBHANuAycjMKSQcCkyJim6Ti4okTAAABUElEQVTjgXlAZwPnBmgHJgLHAvMlHRcR2/bcQ2Jl5EBh1ruHIuJZAEkrgbtT+WPA2en+e4CTJVXajEqzh4XA1yTdDPwoIrpydSqGA9dK6iDLk5zQ4LkBfhARrwBPSVoFnAgs2Y0xm+3CgcKsdy/l7r+S236F1/539gHeFhEvVrW9StJPgPcBD0h6T43jXwKsI5s97EM262jk3ADVSUYnHW3AOUdhtnvuBi6qbKTZAZKOjYjHIuLLwCKyV/qbgJG5tqOBZ9OM4MPAsH6c/08k7ZPyFscAy/s3DLPeOVCY7Z5PAZ2Slkp6Apidyi+WtEzSo8CLwE+BpcB2SY9KugS4DrhQ0gNky05b+nH+5cCCdPzZzk/YnuDLY81alKS5wI8j4oeD3Rcb2jyjMDOzQp5RmJlZIc8ozMyskAOFmZkVcqAwM7NCDhRmZlbIgcLMzAr9f2yqQEksMzccAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "for id, count in id_counter.most_common(10):\n",
    "    df_temp = df[df['ID']==id]\n",
    "    df_temp['IntData'] = df_temp.Data.apply(int, base=16)\n",
    "    df_temp.plot(x=\"Timestamp\", y=\"IntData\", title=\"Data with ID=\"+id +\" over time\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Further Exploration (unfinished)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from collections import defaultdict\n",
    "\n",
    "def data_to_bytes_df(data_list):\n",
    "    '''converts list of extended CAN frames (8 bytes) into pandas dataframe w/ columns = bytes'''\n",
    "    \n",
    "    d = defaultdict(list)\n",
    "    for data in data_list:\n",
    "        for i in range(16,0,-2):\n",
    "            d[i//2].append(data[16-i:16-i+2])\n",
    "\n",
    "    return pd.DataFrame.from_dict(d)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}