Exploratory Data Analysis.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os, json\n",
    "import pandas as pd\n",
    "\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "from scipy import stats\n",
    "from IPython.display import Image"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "PATH = \"../tubs/tub4_newTrack\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "json_files = [file for file in os.listdir(PATH) if file.endswith('json') ]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "13440"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(json_files)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "meta_json = pd.read_json(f'{PATH}/{json_files[0]}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "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>inputs</th>\n",
       "      <th>types</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>cam/image_array</td>\n",
       "      <td>image_array</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>user/angle</td>\n",
       "      <td>float</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>user/throttle</td>\n",
       "      <td>float</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>user/mode</td>\n",
       "      <td>str</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>timestamp</td>\n",
       "      <td>str</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            inputs        types\n",
       "0  cam/image_array  image_array\n",
       "1       user/angle        float\n",
       "2    user/throttle        float\n",
       "3        user/mode          str\n",
       "4        timestamp          str"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "meta_json"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "13440"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(json_files)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "ename": "ValueError",
     "evalue": "If using all scalar values, you must pass an index",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-50-d892768ddefa>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mjson1\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mpd\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mread_json\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34mf'{PATH}/{json_files[1]}'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;32m~\\Anaconda3\\envs\\py36\\lib\\site-packages\\pandas\\io\\json\\json.py\u001b[0m in \u001b[0;36mread_json\u001b[1;34m(path_or_buf, orient, typ, dtype, convert_axes, convert_dates, keep_default_dates, numpy, precise_float, date_unit, encoding, lines, chunksize, compression)\u001b[0m\n\u001b[0;32m    420\u001b[0m         \u001b[1;32mreturn\u001b[0m \u001b[0mjson_reader\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    421\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 422\u001b[1;33m     \u001b[0mresult\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mjson_reader\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mread\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    423\u001b[0m     \u001b[1;32mif\u001b[0m \u001b[0mshould_close\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    424\u001b[0m         \u001b[1;32mtry\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Anaconda3\\envs\\py36\\lib\\site-packages\\pandas\\io\\json\\json.py\u001b[0m in \u001b[0;36mread\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m    527\u001b[0m             )\n\u001b[0;32m    528\u001b[0m         \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 529\u001b[1;33m             \u001b[0mobj\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_get_object_parser\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdata\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    530\u001b[0m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mclose\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    531\u001b[0m         \u001b[1;32mreturn\u001b[0m \u001b[0mobj\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Anaconda3\\envs\\py36\\lib\\site-packages\\pandas\\io\\json\\json.py\u001b[0m in \u001b[0;36m_get_object_parser\u001b[1;34m(self, json)\u001b[0m\n\u001b[0;32m    544\u001b[0m         \u001b[0mobj\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    545\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mtyp\u001b[0m \u001b[1;33m==\u001b[0m \u001b[1;34m'frame'\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 546\u001b[1;33m             \u001b[0mobj\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mFrameParser\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mjson\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mparse\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    547\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    548\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mtyp\u001b[0m \u001b[1;33m==\u001b[0m \u001b[1;34m'series'\u001b[0m \u001b[1;32mor\u001b[0m \u001b[0mobj\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Anaconda3\\envs\\py36\\lib\\site-packages\\pandas\\io\\json\\json.py\u001b[0m in \u001b[0;36mparse\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m    636\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    637\u001b[0m         \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 638\u001b[1;33m             \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_parse_no_numpy\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    639\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    640\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mobj\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Anaconda3\\envs\\py36\\lib\\site-packages\\pandas\\io\\json\\json.py\u001b[0m in \u001b[0;36m_parse_no_numpy\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m    851\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0morient\u001b[0m \u001b[1;33m==\u001b[0m \u001b[1;34m\"columns\"\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    852\u001b[0m             self.obj = DataFrame(\n\u001b[1;32m--> 853\u001b[1;33m                 loads(json, precise_float=self.precise_float), dtype=None)\n\u001b[0m\u001b[0;32m    854\u001b[0m         \u001b[1;32melif\u001b[0m \u001b[0morient\u001b[0m \u001b[1;33m==\u001b[0m \u001b[1;34m\"split\"\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    855\u001b[0m             decoded = {str(k): v for k, v in compat.iteritems(\n",
      "\u001b[1;32m~\\Anaconda3\\envs\\py36\\lib\\site-packages\\pandas\\core\\frame.py\u001b[0m in \u001b[0;36m__init__\u001b[1;34m(self, data, index, columns, dtype, copy)\u001b[0m\n\u001b[0;32m    346\u001b[0m                                  dtype=dtype, copy=copy)\n\u001b[0;32m    347\u001b[0m         \u001b[1;32melif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdict\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 348\u001b[1;33m             \u001b[0mmgr\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_init_dict\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mindex\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcolumns\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mdtype\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    349\u001b[0m         \u001b[1;32melif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mma\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mMaskedArray\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    350\u001b[0m             \u001b[1;32mimport\u001b[0m \u001b[0mnumpy\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mma\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmrecords\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0mmrecords\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Anaconda3\\envs\\py36\\lib\\site-packages\\pandas\\core\\frame.py\u001b[0m in \u001b[0;36m_init_dict\u001b[1;34m(self, data, index, columns, dtype)\u001b[0m\n\u001b[0;32m    457\u001b[0m             \u001b[0marrays\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m[\u001b[0m\u001b[0mdata\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mk\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0mk\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mkeys\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    458\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 459\u001b[1;33m         \u001b[1;32mreturn\u001b[0m \u001b[0m_arrays_to_mgr\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0marrays\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdata_names\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mindex\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcolumns\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mdtype\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    460\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    461\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0m_init_ndarray\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mvalues\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mindex\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcolumns\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mNone\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcopy\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mFalse\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Anaconda3\\envs\\py36\\lib\\site-packages\\pandas\\core\\frame.py\u001b[0m in \u001b[0;36m_arrays_to_mgr\u001b[1;34m(arrays, arr_names, index, columns, dtype)\u001b[0m\n\u001b[0;32m   7313\u001b[0m     \u001b[1;31m# figure out the index, if necessary\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   7314\u001b[0m     \u001b[1;32mif\u001b[0m \u001b[0mindex\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 7315\u001b[1;33m         \u001b[0mindex\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mextract_index\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0marrays\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   7316\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   7317\u001b[0m     \u001b[1;31m# don't force copy because getting jammed in an ndarray anyway\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Anaconda3\\envs\\py36\\lib\\site-packages\\pandas\\core\\frame.py\u001b[0m in \u001b[0;36mextract_index\u001b[1;34m(data)\u001b[0m\n\u001b[0;32m   7350\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   7351\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[0mindexes\u001b[0m \u001b[1;32mand\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[0mraw_lengths\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 7352\u001b[1;33m             raise ValueError('If using all scalar values, you must pass'\n\u001b[0m\u001b[0;32m   7353\u001b[0m                              ' an index')\n\u001b[0;32m   7354\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mValueError\u001b[0m: If using all scalar values, you must pass an index"
     ]
    }
   ],
   "source": [
    "json1 = pd.read_json(f'{PATH}/{json_files[1]}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "with open(f'{PATH}/{json_files[1]}', 'r') as f:\n",
    "    data = json.load(f)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[\"user/throttle\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.DataFrame(data, index = [0])"
   ]
  },
  {
   "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>user/throttle</th>\n",
       "      <th>user/angle</th>\n",
       "      <th>cam/image_array</th>\n",
       "      <th>timestamp</th>\n",
       "      <th>user/mode</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0_cam-image_array_.jpg</td>\n",
       "      <td>None</td>\n",
       "      <td>user</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   user/throttle  user/angle         cam/image_array timestamp user/mode\n",
       "0              0           0  0_cam-image_array_.jpg      None      user"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'0_cam-image_array_.jpg'"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"cam/image_array\"][0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/jpeg": "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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.DataFrame()\n",
    "i = 0\n",
    "for i, j in enumerate(json_files[1:1000]):\n",
    "    with open(f'{PATH}/{j}') as f:\n",
    "        data = json.load(f)\n",
    "        \n",
    "        userThrottle = data[\"user/throttle\"]\n",
    "        userAngle = data[\"user/angle\"]\n",
    "        camImage_array = data[\"cam/image_array\"]\n",
    "        index = int(camImage_array[:-21])\n",
    "        df_row = pd.DataFrame([[userThrottle, userAngle, camImage_array]], index = [index], columns = [\"user/throttle\", \"user/angle\", \"cam/image_array\"])\n",
    "        \n",
    "        df = df.append(df_row)\n",
    "        \n",
    "df = df.sort_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = df.sort_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.DataFrame(columns=[\"A\", \"B\", \"C\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "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>A</th>\n",
       "      <th>B</th>\n",
       "      <th>C</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Empty DataFrame\n",
       "Columns: [A, B, C]\n",
       "Index: []"
      ]
     },
     "execution_count": 104,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x225ac9e9828>"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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givZCuBh44ZTs299jcFTyY+D7wBVDy76qPY8twCuntVbgycDlbd9eDpw4Jfv2y8BNwKXt5+wp3rdz1jrF+/Z97ffpUmAjQ+E6hZkwZ62TyAS/GStJnZvmc/SSpDEw6CWpcwa9JHXOoJekzhn0ktQ5g16/8pJ8McnKJK9Pss9Q/48Wud5nJHny0PSxs+6m+LHZd9uUJsGg16+k9u1kktyfwZdRvgu8HthnlwvuZD078QwGnzff4VgGd1GUdqtpvteNdJcM7t9/TlU9tk2/AXgAg1tevBrYDlxZVSck2Rf4APDbDF7jb6uqs5K8Ang+gy+v7Qs8i0EYfyXJa4GHAxuT3FxVz2zbeTvwAuAnwNqquinJx9p2jwAubmM+yuBbjHcA64EftLrubPeJeR2DW9E+PclbGHwpZvj5PQF4T3tONwOvqKobx7X/9KvNoNdydwpwaFX9LMl+re9U4IKqelXr+1qSL7d5TwIeV1W3tOljgM9X1QVJTgaeWVU3t3n7AhdW1alJ3gX8OfBPbd6jgGdX1Z1JPgBcUlXHJnkW8PGq+t32ByN+VFXvBmh/nOOcqjqzTdMe92TwxrS2qrYl+WPg7Qy+JSstmkGv5e4y4BNJPg98vvX9IfCidtQPgyP4Q1r7vKGQB3gK8Abm9nPgnNa+CPiDoXmfrao7W/v3aUfo7Q3jgCQPWsBzeDTwWOC8Fv57MLiDoTQWBr2Wi+3c85rS/drj84GnMTgt8vdJHsPgFrEvrqqrh1eQ5CgG98PZMX0YcH0N/jDEXH5Rd98j5E7u+fvy46H2Ym+BGwb353nSApaRRubFWC0XNwEPaUfLezM4b/5rwMFVtRF4I7Afg3PcXwJes+MveCXZ2e1zjwG+ODT9Q+DX70Nt/wm8tG3rGcDNNbh3+Oz17Wz9VwMzSZ7U1rFne8OSxsKg17JQVb9gcOe/TQxOp3yTwSmOf0lyOXAJcFpV3Qb8I7AncFmSb7TpuTyXewb9BuALSTYusLy3AWuSXAa8g8EfjwD4d+CP2h94fiqDe47/bfvLQb8x9Nx+DhwHvDPJ1xnczXD40zrSonj3Sv1Kav8r+K+qWrPUtUiTZtBLUuc8dSNJnTPoJalzBr0kdc6gl6TOGfSS1DmDXpI6Z9BLUuf+H8xbr9RSWr9nAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(df[\"user/throttle\"], kde=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\allace\\Anaconda3\\lib\\site-packages\\matplotlib\\axes\\_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n",
      "  warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x22ea8018550>"
      ]
     },
     "execution_count": 75,
     "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": [
    "sns.distplot(df[\"user/angle\"], kde=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\allace\\Anaconda3\\lib\\site-packages\\matplotlib\\axes\\_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n",
      "  warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n",
      "C:\\Users\\allace\\Anaconda3\\lib\\site-packages\\matplotlib\\axes\\_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n",
      "  warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x432 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "with sns.axes_style(\"white\"):\n",
    "    sns.jointplot(x=df[\"user/angle\"], y=df[\"user/throttle\"], kind=\"hex\", color=\"k\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 159,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_x = df.drop(labels=[\"cam/image_array\"], axis = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 160,
   "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>user/throttle</th>\n",
       "      <th>user/angle</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>0.102041</td>\n",
       "      <td>-0.044898</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>100</th>\n",
       "      <td>0.004082</td>\n",
       "      <td>-0.044897</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>101</th>\n",
       "      <td>0.016326</td>\n",
       "      <td>-0.065306</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     user/throttle  user/angle\n",
       "0         0.000000    0.000000\n",
       "1         0.000000    0.000000\n",
       "10        0.102041   -0.044898\n",
       "100       0.004082   -0.044897\n",
       "101       0.016326   -0.065306"
      ]
     },
     "execution_count": 160,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_x.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\allace\\Anaconda3\\lib\\site-packages\\matplotlib\\axes\\_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n",
      "  warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n",
      "C:\\Users\\allace\\Anaconda3\\lib\\site-packages\\matplotlib\\axes\\_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n",
      "  warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.JointGrid at 0x22ea8110780>"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x432 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.jointplot(x=\"user/angle\", y=\"user/throttle\", data=df_x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'1000_cam-image_array_.jpg'"
      ]
     },
     "execution_count": 137,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"cam/image_array\"][1000]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/jpeg": 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5Sfaiis+oH//Z\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Image(f'{PATH}/{df.iloc[530][2]}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'10413_cam-image_array_.jpg'"
      ]
     },
     "execution_count": 138,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.iloc[515][2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 155,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "10398    0.293877\n",
       "10399    0.297959\n",
       "10400    0.297959\n",
       "10401    0.297959\n",
       "10402    0.297959\n",
       "10403    0.322449\n",
       "10404    0.322449\n",
       "10405    0.387755\n",
       "10406    0.387755\n",
       "10407    0.453061\n",
       "10408    0.453061\n",
       "10409    0.465306\n",
       "10410    0.465306\n",
       "10411    0.465306\n",
       "10412    0.477550\n",
       "10413    0.477550\n",
       "10414    0.485714\n",
       "10415    0.485714\n",
       "10416    0.493877\n",
       "10417    0.493877\n",
       "10418    0.097960\n",
       "10419    0.097960\n",
       "10420    0.097960\n",
       "10421    0.297960\n",
       "10422    0.297960\n",
       "10423    0.412245\n",
       "10424    0.412245\n",
       "10425    0.497960\n",
       "10426    0.497960\n",
       "10427    0.526531\n",
       "Name: user/angle, dtype: float64"
      ]
     },
     "execution_count": 155,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.iloc[500:530][\"user/angle\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 158,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.35510253906249994"
      ]
     },
     "execution_count": 158,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"user/angle\"][1000]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'10177'"
      ]
     },
     "execution_count": 114,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"cam/image_array\"][200][:-21]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 152,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_steering = pd.DataFrame(df.iloc[200:1000][\"user/angle\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "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",
       "        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>user/angle</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>10098</th>\n",
       "      <td>-0.873470</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10099</th>\n",
       "      <td>-0.008163</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10100</th>\n",
       "      <td>-0.008163</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10101</th>\n",
       "      <td>-0.016326</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10102</th>\n",
       "      <td>-0.016326</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       user/angle\n",
       "10098   -0.873470\n",
       "10099   -0.008163\n",
       "10100   -0.008163\n",
       "10101   -0.016326\n",
       "10102   -0.016326"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_steering.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1d4b103e6d8>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(); df_steering.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/jpeg": 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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Image(f'{PATH}/{df.iloc[212][2]}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-0.036734444754464295"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.iloc[212][\"user/angle\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/jpeg": 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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Image(f'{PATH}/{df.iloc[245][2]}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-0.13877563476562502"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.iloc[245][\"user/angle\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1d4b22947b8>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_steering = pd.DataFrame(df.iloc[212:245][\"user/angle\"])\n",
    "plt.figure(); df_steering.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
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