Analysis.ipynb

{
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   "metadata": {
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   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "0cOcl_SiKeeR"
   },
   "source": [
    "## TASK 0"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
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    "id": "iGiqyKjZKykE"
   },
   "source": [
    "### 1."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "MTZA5GvTtSHF"
   },
   "outputs": [],
   "source": [
    "# importing the disengagement dataset\n",
    "df_disengagements = pd.read_csv('mp1_av_disengagements.csv')\n",
    "# importing the total miles dataset\n",
    "df_totalmiles = pd.read_csv('mp1_av_totalmiles.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 204
    },
    "colab_type": "code",
    "id": "5rW9oxeZtSHO",
    "outputId": "836a475b-0732-4012-bc59-ca09ea9acdbf"
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   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\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>Month</th>\n",
       "      <th>Car</th>\n",
       "      <th>Location</th>\n",
       "      <th>Weather</th>\n",
       "      <th>TypeOfTrigger</th>\n",
       "      <th>ReactionTime</th>\n",
       "      <th>Cause</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>14-Sep</td>\n",
       "      <td>WDD2221851A001072</td>\n",
       "      <td>urban-street</td>\n",
       "      <td>cloudy</td>\n",
       "      <td>automatic</td>\n",
       "      <td>0.58</td>\n",
       "      <td>Incorrect Traffic Light Detection</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>14-Sep</td>\n",
       "      <td>WDD2221851A001072</td>\n",
       "      <td>urban-street</td>\n",
       "      <td>cloudy</td>\n",
       "      <td>automatic</td>\n",
       "      <td>0.66</td>\n",
       "      <td>Adverse road surface conditions</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>14-Sep</td>\n",
       "      <td>WDD2221851A001072</td>\n",
       "      <td>urban-street</td>\n",
       "      <td>cloudy</td>\n",
       "      <td>automatic</td>\n",
       "      <td>1.93</td>\n",
       "      <td>Position Estimation Failure</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>14-Sep</td>\n",
       "      <td>WDD2221851A001072</td>\n",
       "      <td>urban-street</td>\n",
       "      <td>cloudy</td>\n",
       "      <td>automatic</td>\n",
       "      <td>0.65</td>\n",
       "      <td>Emergency Vehicle</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>14-Sep</td>\n",
       "      <td>WDD2221851A001072</td>\n",
       "      <td>urban-street</td>\n",
       "      <td>cloudy</td>\n",
       "      <td>automatic</td>\n",
       "      <td>0.64</td>\n",
       "      <td>Adverse road surface conditions</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Month                Car      Location Weather TypeOfTrigger  \\\n",
       "0  14-Sep  WDD2221851A001072  urban-street  cloudy     automatic   \n",
       "1  14-Sep  WDD2221851A001072  urban-street  cloudy     automatic   \n",
       "2  14-Sep  WDD2221851A001072  urban-street  cloudy     automatic   \n",
       "3  14-Sep  WDD2221851A001072  urban-street  cloudy     automatic   \n",
       "4  14-Sep  WDD2221851A001072  urban-street  cloudy     automatic   \n",
       "\n",
       "   ReactionTime                              Cause  \n",
       "0          0.58  Incorrect Traffic Light Detection  \n",
       "1          0.66    Adverse road surface conditions  \n",
       "2          1.93        Position Estimation Failure  \n",
       "3          0.65                  Emergency Vehicle  \n",
       "4          0.64    Adverse road surface conditions  "
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# using the head function to get a view of the dataset\n",
    "df_disengagements.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 204
    },
    "colab_type": "code",
    "id": "J31_txfmtSHc",
    "outputId": "c170171b-4f48-4f88-c8d3-28565f135188"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    .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>Month</th>\n",
       "      <th>Car</th>\n",
       "      <th>miles driven in autonomous mode</th>\n",
       "      <th>total number of disengagements</th>\n",
       "      <th>number of automatic disengagements</th>\n",
       "      <th>number of manual disengagements</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>14-Sep</td>\n",
       "      <td>WDD2221851A001072</td>\n",
       "      <td>4207</td>\n",
       "      <td>44</td>\n",
       "      <td>24</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>14-Oct</td>\n",
       "      <td>WDD2221851A001072</td>\n",
       "      <td>23971</td>\n",
       "      <td>228</td>\n",
       "      <td>100</td>\n",
       "      <td>128</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>14-Nov</td>\n",
       "      <td>WDD2221851A001072</td>\n",
       "      <td>15837</td>\n",
       "      <td>141</td>\n",
       "      <td>66</td>\n",
       "      <td>75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>14-Dec</td>\n",
       "      <td>WDD2221851A001072</td>\n",
       "      <td>9413</td>\n",
       "      <td>36</td>\n",
       "      <td>15</td>\n",
       "      <td>21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>15-Jan</td>\n",
       "      <td>WDD2221851A001072</td>\n",
       "      <td>18192</td>\n",
       "      <td>48</td>\n",
       "      <td>18</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Month                Car  miles driven in autonomous mode  \\\n",
       "0  14-Sep  WDD2221851A001072                             4207   \n",
       "1  14-Oct  WDD2221851A001072                            23971   \n",
       "2  14-Nov  WDD2221851A001072                            15837   \n",
       "3  14-Dec  WDD2221851A001072                             9413   \n",
       "4  15-Jan  WDD2221851A001072                            18192   \n",
       "\n",
       "   total number of disengagements  number of automatic disengagements  \\\n",
       "0                              44                                  24   \n",
       "1                             228                                 100   \n",
       "2                             141                                  66   \n",
       "3                              36                                  15   \n",
       "4                              48                                  18   \n",
       "\n",
       "   number of manual disengagements  \n",
       "0                               20  \n",
       "1                              128  \n",
       "2                               75  \n",
       "3                               21  \n",
       "4                               30  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# using the head function to get a view of the dataset\n",
    "df_totalmiles.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "KdJx6cEgKwyj"
   },
   "source": [
    "### 2."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### a."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 34
    },
    "colab_type": "code",
    "id": "E0PL6PYKtSHj",
    "outputId": "09866d07-0ca5-4fb6-bee8-7718e9ffbdef"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1024"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# total number of disengagements over the entire duration of available data\n",
    "len(df_disengagements)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### b."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "UGPXBj2NtSHp"
   },
   "outputs": [],
   "source": [
    "# creating a new column which separates the month and year from the Month column\n",
    "df_disengagements['sep_month'] = df_disengagements['Month'].apply(lambda x:x.split('-')[1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 51
    },
    "colab_type": "code",
    "id": "5r6tejPmtSHt",
    "outputId": "7c22a2d2-0fdf-4dc2-e5e0-feeb10dc1d24"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Sep', 'Oct', 'Nov', 'Dec', 'Jan', 'Feb', 'Mar', 'Apr', 'May',\n",
       "       'Jun', 'Jul', 'Aug'], dtype=object)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# unique function gives the unique values in sep_month\n",
    "df_disengagements['sep_month'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "12"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# number of unique months that have recorded AV disengagements\n",
    "len(df_disengagements['sep_month'].unique())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### c."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 34
    },
    "colab_type": "code",
    "id": "RMcRuAGStSH1",
    "outputId": "44412e42-7751-4f8b-bbf2-2efa9b49b979"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['urban-street', 'highway'], dtype=object)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# list of unique locations of AV disengagements\n",
    "df_disengagements['Location'].unique()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### d."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 119
    },
    "colab_type": "code",
    "id": "2TvGnxhCtSH7",
    "outputId": "bd571929-49f5-42f2-c436-1f7f7a939001"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Incorrect Traffic Light Detection',\n",
       "       'Adverse road surface conditions', 'Position Estimation Failure',\n",
       "       'Emergency Vehicle', 'Software Froze', 'Hardware Fault',\n",
       "       'Incorrect behavior prediction of others',\n",
       "       'Recklessly behaving agent', 'System Tuning and Calibration',\n",
       "       'Unwanted Driver Discomfort'], dtype=object)"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_disengagements['Cause'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "10"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# number of unique causes for AV disengagements\n",
    "len(df_disengagements['Cause'].unique())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### e."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 238
    },
    "colab_type": "code",
    "id": "rMTB9ZXJtSIC",
    "outputId": "069637b2-d50f-4bcf-bafd-b4635e762f21"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 1024 entries, 0 to 1023\n",
      "Data columns (total 8 columns):\n",
      "Month            1024 non-null object\n",
      "Car              1024 non-null object\n",
      "Location         1024 non-null object\n",
      "Weather          1024 non-null object\n",
      "TypeOfTrigger    1024 non-null object\n",
      "ReactionTime     492 non-null float64\n",
      "Cause            1024 non-null object\n",
      "sep_month        1024 non-null object\n",
      "dtypes: float64(1), object(7)\n",
      "memory usage: 64.1+ KB\n"
     ]
    }
   ],
   "source": [
    "df_disengagements.info(null_counts=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 34
    },
    "colab_type": "code",
    "id": "Vydd3tP4tSII",
    "outputId": "d868196c-53d2-4187-9b15-e20d4a63bcaf"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_disengagements['ReactionTime'].isnull().values.any()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 34
    },
    "colab_type": "code",
    "id": "ntnhndPitSIN",
    "outputId": "7f357e16-1684-498d-88c8-791c85abf202"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "532"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# number of missing values (NAs commonly occur in real world datasets)\n",
    "df_disengagements.isnull().sum().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 221
    },
    "colab_type": "code",
    "id": "J2pDyLqfGxzD",
    "outputId": "c4225314-22d5-4d64-cb36-465c5706abea"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Cause\n",
       "Adverse road surface conditions             85\n",
       "Emergency Vehicle                           98\n",
       "Hardware Fault                             104\n",
       "Incorrect Traffic Light Detection           96\n",
       "Incorrect behavior prediction of others    109\n",
       "Position Estimation Failure                 80\n",
       "Recklessly behaving agent                  124\n",
       "Software Froze                              98\n",
       "System Tuning and Calibration              102\n",
       "Unwanted Driver Discomfort                 128\n",
       "dtype: int64"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_disengagements.groupby('Cause').size()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 330
    },
    "colab_type": "code",
    "id": "FS0IK53-tSIc",
    "outputId": "c0e2fca4-afb6-400f-acb0-143f02948f4c"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot a pie chart for the causes of AV disengagement\n",
    "labels = df_disengagements['Cause'].unique()\n",
    "explode = (0.1, 0, 0, 0, 0, 0, 0, 0, 0, 0)\n",
    "colors = ['gold', 'yellowgreen', 'lightcoral', 'lightskyblue', 'cyan', 'grey', 'red', 'white', 'brown', 'pink']\n",
    "plt.pie(df_disengagements.groupby('Cause').size(), explode=explode, shadow=True, labels=labels, autopct='%1.1f%%', colors=colors)\n",
    "plt.title('Causes of AV disengagement')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "4pA915VnNtxX"
   },
   "source": [
    "Unwated Driver Discomfort & Incorrect behavior prediction of others are the top 2 leading causes of disengagement."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "JHDLF5X7K1wE"
   },
   "source": [
    "### 4."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_totalmiles['disen/miles'] = df_totalmiles['total number of disengagements']/df_totalmiles['miles driven in autonomous mode']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "# creating a new column which separates the month and year from the Month column\n",
    "df_totalmiles['sep_month'] = df_totalmiles['Month'].apply(lambda x:x.split('-')[1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "FYqlK7mwFoOM"
   },
   "outputs": [],
   "source": [
    "# changing the date format for Month\n",
    "df_totalmiles['Month'] = pd.to_datetime(df_totalmiles['Month'], format=\"%y-%b\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 609
    },
    "colab_type": "code",
    "id": "63tRQb6WtSIs",
    "outputId": "358878b5-8478-4db9-c305-25528343ddf0"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5,1,'Disengagement/mile over time with monthly granularity')"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# visualizing the trend of disengagement/mile over time with monthly granularity.\n",
    "plt.figure(figsize = (20,10))\n",
    "plt.plot(df_totalmiles.groupby('Month')['disen/miles'].mean())\n",
    "plt.xlabel('Year')\n",
    "plt.ylabel('Mean')\n",
    "plt.title('Disengagement/mile over time with monthly granularity')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "SNO5eOOMXOzH"
   },
   "source": [
    "The trend follows an expoential curve as it falls off with a steep decrease. It is maturing over time."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "Im3gjLiQJ5B5"
   },
   "source": [
    "## TASK I"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "YgcZ5EHXVwc_"
   },
   "source": [
    "### 1."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "OJvMLiTiJ8Gi"
   },
   "source": [
    "\n",
    "*  If samples are drawn from a random variable that follows the Gaussian distribution, the samples concentrate on mean value, which means that the more close to the mean value, the higher frenquency the observations have.  \n",
    "*  If samples are drawn from a random variable that follows the exponential distribution, the numbers of observations decrease sharply and then slower as the value increasing. \n",
    "* Weibull distribution depends on  λ  and k values. For some  λ  and k values, the distribution is the same as Gaussian distribution. For some  λ  and k values, the distribution looks like exponential distribution. \n",
    "\n",
    "     "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "UZ3Iyr-aPXzo"
   },
   "source": [
    "### 2."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 412
    },
    "colab_type": "code",
    "id": "nJ0fjkCCDaX8",
    "outputId": "cf155034-a972-45e8-e2bd-355dccf436ae"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# remove missing values\n",
    "df_dismiss = df_disengagements.dropna()\n",
    "sns.distplot(df_dismiss['ReactionTime'])\n",
    "plt.title('Probability distribution of reaction times')\n",
    "plt.ylabel('Count')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "lwxUza3E2r3k"
   },
   "source": [
    "The distribution fits a Weibull distribution. The fit distribution signify has one peak and looks not symmetrical because it has long tail. Therefore, the samples are more likely drawn from Weibull distribution."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "cvcXdDWKPa7e"
   },
   "source": [
    "### 3. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### a."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "dWbrEkjJyKLv"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9297703252032526"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "### average reaction time for entire duration of dataset\n",
    "df_disengagements['ReactionTime'].mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### b."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "cNmhH6TIy2WP"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Location\n",
       "highway         1.48000\n",
       "urban-street    0.92865\n",
       "Name: ReactionTime, dtype: float64"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "### average reaction time for entire duration of dataset differentiated by location of disengagement\n",
    "df_disengagements.groupby('Location')['ReactionTime'].mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "sNRmxS6wPp2G"
   },
   "source": [
    "### 4.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "yMv96Xl7QVbW"
   },
   "source": [
    "Suppose X is from the AV sample with mean 0.9, we want to check if the mean of X equals to the mean reaction value of Non-AVs $\\mu=0.93$\n",
    "$$H_{0}: \\bar{X} = \\mu \\quad vs.\\quad H_{A}: \\bar{X} \\neq \\mu$$\n",
    "Test statistic $t = \\frac{\\bar{X}-\\mu}{s/\\sqrt{n}} = \\frac{1.09-0.93}{1.694/\\sqrt{1024}} = 4.24$ \\\\\n",
    "According to the T distribution table, when the degree of freedom is greater than 500, the t value i.e 4.24 is to the right which indicates a very low p-value. \\\\\n",
    "Thus, we reject the null hypothesis at $\\alpha = 0.05$"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "B2gJ6MSpPesb"
   },
   "source": [
    "### 5."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "3j3WxVMcA3tT"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "### probability distribution of disengagement/mile with monthly granularity\n",
    "sns.distplot(df_totalmiles['disen/miles'])\n",
    "plt.ylabel('Count')\n",
    "plt.title('disengagement/mile with monthly granularity')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "R-Y0uDaPUciy"
   },
   "source": [
    "The above probability distribution does not fit any known distribution. The fit distribution signifies two peaks. More specifically, the samples are drawn from Bimodal distribution."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# TASK 2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1\n",
    "\n",
    "### a.\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The assumption on maximum number of disengagements in a mile allows us to treat the occurrence of a disengagement in a mile as a random variable with a **bernoulli** distribution."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### b."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "835"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# checking the number of disengagements which happened during cloudy weather\n",
    "len(df_disengagements[df_disengagements['Weather'] == 'cloudy'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "189"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# checking the number of disengagements which happened during clear weather\n",
    "len(df_disengagements[df_disengagements['Weather'] == 'clear'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.005902556775527249"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# probability of disengagement per mile on a cloudy day\n",
    "len(df_disengagements[df_disengagements['Weather'] == 'cloudy'])/(df_totalmiles['miles driven in autonomous mode'].sum() * 0.28)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### c."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.005902556775527249"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# probability of disengagement per mile on a cloudy day\n",
    "len(df_disengagements[df_disengagements['Weather'] == 'cloudy'])/(df_totalmiles['miles driven in autonomous mode'].sum() * 0.28)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### d."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.0028063653172267283"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# probability of automatic disengagement per mile on a cloudy day -> P(automatic disengagement per mile in cloudy day) \n",
    "len(df_disengagements[(df_disengagements['TypeOfTrigger'] == 'automatic') & (df_disengagements['Weather'] == 'cloudy')])/(df_totalmiles['miles driven in autonomous mode'].sum() * 0.28)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.00026390673008345393"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# probability of automatic disengagement per mile on a clear day -> P(automatic disengagement per mile in clear day)\n",
    "len(df_disengagements[(df_disengagements['TypeOfTrigger'] == 'automatic') & (df_disengagements['Weather'] == 'clear')])/(df_totalmiles['miles driven in autonomous mode'].sum() * 0.72)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### e."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Denote $X_{i}$ as a random variable from (a) that follows Bernoulli(p), p is calculated in (b), which is 0.0059.\n",
    "$$Denote \\quad Y = \\sum_{i=1}^{10000}X_{i} \\sim  Binomial(10000,p)$$\n",
    "$$E(Y) = np=59, Var(Y)=np(1-p)=58.6519$$\n",
    "The aymmptotic distribution is \n",
    "$$Z = \\frac{Y-np}{\\sqrt{np(1-p)}} = \\frac{Y-59}{\\sqrt{58.6519}} \\sim N(0,1)$$\n",
    "When Y = 150,  Z = 11.88, \\\\\n",
    "$\\therefore \\quad P(Z \\geq 11.88) \\approx 0$ \n",
    "\n",
    "Therefore, in 10000 miles it is very unlikely to have 150 or more disengagements under cloudy days."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let $\\widehat{p}_{1}$= 0.0059, the probability of disengagement per mile on a cloudy day,  $\\widehat{p}_{2}$= 0.00052, the probability of disengagement per mile on a clear day.\n",
    "$$H_{0}: p_{1} > p_{2} \\quad  H_{A}: p_{1}\\leq p_{2}$$\n",
    "Z-Test Statistic for two proportions:\n",
    "$$Z \\cong \\frac{(\\widehat{p}_{1}-\\widehat{p}_{2}) - (p_{1} - p_{2})}{\\sqrt{\\widehat{p}(1-\\widehat{p})(\\frac{1}{n_{1}}+ \\frac{1}{n_{2} })}}, \\quad where \\quad \\widehat{p} = \\frac{X_{1}+X_{2}}{n_{1}+n_{2}}$$\n",
    "$X_{1}$is the total number of cloudy day, $X_{2}$is the total number of clear day, $n_{1}+n_{2}$is the total miles"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.0020268036870409261"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# p_hat\n",
    "(len(df_disengagements[df_disengagements['Weather'] == 'cloudy'])+len(df_disengagements[df_disengagements['Weather'] == 'clear']))/df_totalmiles['miles driven in autonomous mode'].sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4.3867070871651753e-05"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# denominator for the Z-Test formula\n",
    "np.sqrt((0.002*0.098*(1/(df_totalmiles['miles driven in autonomous mode'].sum() * 0.28) + 1/(df_totalmiles['miles driven in autonomous mode'].sum() * 0.72))))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Plug in all the values:\n",
    "$$Z = \\frac{0.0059 - 0.00052}{4.39\\times e^{-5}} = 122.55$$\n",
    "\n",
    "The Z value is large enough to accept null hypothesis, therefore, in cloudy weather, AVs are more unsafty than clear days."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "# dropping the missing values since missing values will give incorrect results\n",
    "df_cloudy = df_disengagements[(df_disengagements['TypeOfTrigger'] == 'automatic') & (df_disengagements['Weather'] == 'cloudy')]\n",
    "df_cloudy = df_cloudy.dropna()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.5417721518987342"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# the conditional probability that the reaction time greater than 0.5s given that the weather is cloudy\n",
    "len(df_cloudy[df_cloudy['ReactionTime']>0.5])/len(df_cloudy)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "# dropping the missing values since missing values will give incorrect results\n",
    "df_clear = df_disengagements[(df_disengagements['TypeOfTrigger'] == 'automatic') & (df_disengagements['Weather'] == 'clear')]\n",
    "df_clear = df_clear.dropna()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.3854166666666667"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# the conditional probability that the reaction time greater than 0.5s given that the weather is cloudy\n",
    "len(df_clear[df_clear['ReactionTime']>0.7])/len(df_clear)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Using the total probability theorem and the conditional probabilities found in question 3. Denote the event $A_{1}$as automatic disengagement per mile in cloudy day, denote $A_{2}$as automatic disengagement per mile in clear day.\n",
    "From question 1.d, we know $P(A_{1})=0.0028$ and $P(A_{2}\n",
    ")= 0.00026$\n",
    "\n",
    "$$P(accident) = P(accident| A_{1})P(A_{1}) + P(accident|A_{2})P(A_{2})$$\n",
    "$$=0.542*0.0028+0.385*0.00026 = 0.0016=1.6 \\times e^{-3}$$"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 5."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The probability of a human driver causing an accident is much smaller $2 \\times e^{-6} $ in comparison to AVs causing accident which is $1.6 \\times e^{-3} $. When an AV is on the road, in the case of an disengagement, the time it takes for a human to react is slower at times (considering that the human depends on the AV to do most of the work), thus causing many accidents whereas in case of a human driver driving a car, the driver's sole focus is on the road and in case of any problems the driver is quick to react in most of the cases, thus resulting in fewer accidents."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# TASK 3"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [],
   "source": [
    "# adding a new variable \"Class\" to df_disengagements           \n",
    "df_disengagements.loc[(df_disengagements['Cause'] == 'Incorrect behavior prediction of others') | \\\n",
    "                      (df_disengagements['Cause'] == 'Recklessly behaving agent') | \\\n",
    "                      (df_disengagements['Cause'] == 'Unwanted Driver Discomfort'),'Class']='Controller'\n",
    "df_disengagements.loc[(df_disengagements['Cause'] == 'Adverse road surface conditions') |\\\n",
    "                      (df_disengagements['Cause'] == 'Emergency Vehicle') | \\\n",
    "                      (df_disengagements['Cause'] == 'Position Estimation Failure') |\\\n",
    "                      (df_disengagements['Cause'] == 'Incorrect Traffic Light Detection'),'Class']='Perception System'\n",
    "df_disengagements.loc[(df_disengagements['Cause'] == 'System Tuning and Calibration') |\\\n",
    "                      (df_disengagements['Cause'] == 'Hardware Fault') |\\\n",
    "                      (df_disengagements['Cause'] == 'Software Froze'),'Class']='Computer System'\n",
    "\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "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>Month</th>\n",
       "      <th>Car</th>\n",
       "      <th>Location</th>\n",
       "      <th>Weather</th>\n",
       "      <th>TypeOfTrigger</th>\n",
       "      <th>ReactionTime</th>\n",
       "      <th>Cause</th>\n",
       "      <th>Class</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>14-Sep</td>\n",
       "      <td>WDD2221851A001072</td>\n",
       "      <td>urban-street</td>\n",
       "      <td>cloudy</td>\n",
       "      <td>automatic</td>\n",
       "      <td>0.58</td>\n",
       "      <td>Incorrect Traffic Light Detection</td>\n",
       "      <td>Perception System</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>14-Sep</td>\n",
       "      <td>WDD2221851A001072</td>\n",
       "      <td>urban-street</td>\n",
       "      <td>cloudy</td>\n",
       "      <td>automatic</td>\n",
       "      <td>0.66</td>\n",
       "      <td>Adverse road surface conditions</td>\n",
       "      <td>Perception System</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>14-Sep</td>\n",
       "      <td>WDD2221851A001072</td>\n",
       "      <td>urban-street</td>\n",
       "      <td>cloudy</td>\n",
       "      <td>automatic</td>\n",
       "      <td>1.93</td>\n",
       "      <td>Position Estimation Failure</td>\n",
       "      <td>Perception System</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>14-Sep</td>\n",
       "      <td>WDD2221851A001072</td>\n",
       "      <td>urban-street</td>\n",
       "      <td>cloudy</td>\n",
       "      <td>automatic</td>\n",
       "      <td>0.65</td>\n",
       "      <td>Emergency Vehicle</td>\n",
       "      <td>Perception System</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>14-Sep</td>\n",
       "      <td>WDD2221851A001072</td>\n",
       "      <td>urban-street</td>\n",
       "      <td>cloudy</td>\n",
       "      <td>automatic</td>\n",
       "      <td>0.64</td>\n",
       "      <td>Adverse road surface conditions</td>\n",
       "      <td>Perception System</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>14-Sep</td>\n",
       "      <td>WDD2221851A001072</td>\n",
       "      <td>urban-street</td>\n",
       "      <td>cloudy</td>\n",
       "      <td>automatic</td>\n",
       "      <td>0.88</td>\n",
       "      <td>Incorrect Traffic Light Detection</td>\n",
       "      <td>Perception System</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>14-Sep</td>\n",
       "      <td>WDD2221851A001072</td>\n",
       "      <td>urban-street</td>\n",
       "      <td>cloudy</td>\n",
       "      <td>automatic</td>\n",
       "      <td>0.41</td>\n",
       "      <td>Emergency Vehicle</td>\n",
       "      <td>Perception System</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>14-Sep</td>\n",
       "      <td>WDD2221851A001072</td>\n",
       "      <td>urban-street</td>\n",
       "      <td>cloudy</td>\n",
       "      <td>automatic</td>\n",
       "      <td>1.45</td>\n",
       "      <td>Emergency Vehicle</td>\n",
       "      <td>Perception System</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>14-Sep</td>\n",
       "      <td>WDD2221851A001072</td>\n",
       "      <td>urban-street</td>\n",
       "      <td>cloudy</td>\n",
       "      <td>automatic</td>\n",
       "      <td>0.07</td>\n",
       "      <td>Software Froze</td>\n",
       "      <td>Computer System</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>14-Sep</td>\n",
       "      <td>WDD2221851A001072</td>\n",
       "      <td>urban-street</td>\n",
       "      <td>cloudy</td>\n",
       "      <td>automatic</td>\n",
       "      <td>0.37</td>\n",
       "      <td>Hardware Fault</td>\n",
       "      <td>Computer System</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>14-Sep</td>\n",
       "      <td>WDD2221851A001072</td>\n",
       "      <td>urban-street</td>\n",
       "      <td>cloudy</td>\n",
       "      <td>automatic</td>\n",
       "      <td>1.55</td>\n",
       "      <td>Emergency Vehicle</td>\n",
       "      <td>Perception System</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>14-Sep</td>\n",
       "      <td>WDD2221851A001072</td>\n",
       "      <td>urban-street</td>\n",
       "      <td>cloudy</td>\n",
       "      <td>automatic</td>\n",
       "      <td>0.04</td>\n",
       "      <td>Incorrect Traffic Light Detection</td>\n",
       "      <td>Perception System</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>14-Sep</td>\n",
       "      <td>WDD2221851A001072</td>\n",
       "      <td>urban-street</td>\n",
       "      <td>cloudy</td>\n",
       "      <td>automatic</td>\n",
       "      <td>0.40</td>\n",
       "      <td>Incorrect behavior prediction of others</td>\n",
       "      <td>Controller</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>14-Sep</td>\n",
       "      <td>WDD2221851A001072</td>\n",
       "      <td>urban-street</td>\n",
       "      <td>cloudy</td>\n",
       "      <td>automatic</td>\n",
       "      <td>1.86</td>\n",
       "      <td>Emergency Vehicle</td>\n",
       "      <td>Perception System</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>14-Sep</td>\n",
       "      <td>WDD2221851A001072</td>\n",
       "      <td>urban-street</td>\n",
       "      <td>cloudy</td>\n",
       "      <td>automatic</td>\n",
       "      <td>0.21</td>\n",
       "      <td>Adverse road surface conditions</td>\n",
       "      <td>Perception System</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>14-Sep</td>\n",
       "      <td>WDD2221851A001072</td>\n",
       "      <td>urban-street</td>\n",
       "      <td>cloudy</td>\n",
       "      <td>automatic</td>\n",
       "      <td>0.51</td>\n",
       "      <td>Recklessly behaving agent</td>\n",
       "      <td>Controller</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>14-Sep</td>\n",
       "      <td>WDD2221851A001072</td>\n",
       "      <td>urban-street</td>\n",
       "      <td>cloudy</td>\n",
       "      <td>manual</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Recklessly behaving agent</td>\n",
       "      <td>Controller</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>14-Sep</td>\n",
       "      <td>WDD2221851A001072</td>\n",
       "      <td>urban-street</td>\n",
       "      <td>cloudy</td>\n",
       "      <td>automatic</td>\n",
       "      <td>0.38</td>\n",
       "      <td>System Tuning and Calibration</td>\n",
       "      <td>Computer System</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>14-Sep</td>\n",
       "      <td>WDD2221851A001072</td>\n",
       "      <td>urban-street</td>\n",
       "      <td>cloudy</td>\n",
       "      <td>manual</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Unwanted Driver Discomfort</td>\n",
       "      <td>Controller</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>14-Sep</td>\n",
       "      <td>WDD2221851A001072</td>\n",
       "      <td>urban-street</td>\n",
       "      <td>cloudy</td>\n",
       "      <td>automatic</td>\n",
       "      <td>0.22</td>\n",
       "      <td>Incorrect Traffic Light Detection</td>\n",
       "      <td>Perception System</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>14-Sep</td>\n",
       "      <td>WDD2221851A001072</td>\n",
       "      <td>urban-street</td>\n",
       "      <td>cloudy</td>\n",
       "      <td>manual</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Unwanted Driver Discomfort</td>\n",
       "      <td>Controller</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>14-Sep</td>\n",
       "      <td>WDD2221851A001072</td>\n",
       "      <td>urban-street</td>\n",
       "      <td>cloudy</td>\n",
       "      <td>manual</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Incorrect behavior prediction of others</td>\n",
       "      <td>Controller</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>14-Sep</td>\n",
       "      <td>WDD2221851A001072</td>\n",
       "      <td>urban-street</td>\n",
       "      <td>cloudy</td>\n",
       "      <td>automatic</td>\n",
       "      <td>1.38</td>\n",
       "      <td>Adverse road surface conditions</td>\n",
       "      <td>Perception System</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>14-Sep</td>\n",
       "      <td>WDD2221851A001072</td>\n",
       "      <td>urban-street</td>\n",
       "      <td>cloudy</td>\n",
       "      <td>manual</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Software Froze</td>\n",
       "      <td>Computer System</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>14-Sep</td>\n",
       "      <td>WDD2221851A001072</td>\n",
       "      <td>urban-street</td>\n",
       "      <td>cloudy</td>\n",
       "      <td>manual</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Recklessly behaving agent</td>\n",
       "      <td>Controller</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>14-Sep</td>\n",
       "      <td>WDD2221851A001072</td>\n",
       "      <td>urban-street</td>\n",
       "      <td>cloudy</td>\n",
       "      <td>automatic</td>\n",
       "      <td>0.44</td>\n",
       "      <td>Unwanted Driver Discomfort</td>\n",
       "      <td>Controller</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>14-Sep</td>\n",
       "      <td>WDD2221851A001072</td>\n",
       "      <td>urban-street</td>\n",
       "      <td>cloudy</td>\n",
       "      <td>manual</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Software Froze</td>\n",
       "      <td>Computer System</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>14-Sep</td>\n",
       "      <td>WDD2221851A001072</td>\n",
       "      <td>urban-street</td>\n",
       "      <td>cloudy</td>\n",
       "      <td>manual</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Hardware Fault</td>\n",
       "      <td>Computer System</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>14-Sep</td>\n",
       "      <td>WDD2221851A001072</td>\n",
       "      <td>urban-street</td>\n",
       "      <td>cloudy</td>\n",
       "      <td>automatic</td>\n",
       "      <td>0.04</td>\n",
       "      <td>Unwanted Driver Discomfort</td>\n",
       "      <td>Controller</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>14-Sep</td>\n",
       "      <td>WDD2221851A001072</td>\n",
       "      <td>urban-street</td>\n",
       "      <td>cloudy</td>\n",
       "      <td>manual</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Recklessly behaving agent</td>\n",
       "      <td>Controller</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>994</th>\n",
       "      <td>14-Nov</td>\n",
       "      <td>WDD2221851A000495</td>\n",
       "      <td>urban-street</td>\n",
       "      <td>clear</td>\n",
       "      <td>automatic</td>\n",
       "      <td>0.18</td>\n",
       "      <td>Hardware Fault</td>\n",
       "      <td>Computer System</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>995</th>\n",
       "      <td>14-Nov</td>\n",
       "      <td>WDD2221851A000495</td>\n",
       "      <td>urban-street</td>\n",
       "      <td>clear</td>\n",
       "      <td>automatic</td>\n",
       "      <td>21.00</td>\n",
       "      <td>System Tuning and Calibration</td>\n",
       "      <td>Computer System</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>996</th>\n",
       "      <td>14-Nov</td>\n",
       "      <td>WDD2221851A000495</td>\n",
       "      <td>urban-street</td>\n",
       "      <td>clear</td>\n",
       "      <td>automatic</td>\n",
       "      <td>1.06</td>\n",
       "      <td>System Tuning and Calibration</td>\n",
       "      <td>Computer System</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>997</th>\n",
       "      <td>14-Nov</td>\n",
       "      <td>WDD2221851A000495</td>\n",
       "      <td>urban-street</td>\n",
       "      <td>clear</td>\n",
       "      <td>manual</td>\n",
       "      <td>NaN</td>\n",
       "      <td>System Tuning and Calibration</td>\n",
       "      <td>Computer System</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>998</th>\n",
       "      <td>14-Nov</td>\n",
       "      <td>WDD2221851A000495</td>\n",
       "      <td>urban-street</td>\n",
       "      <td>clear</td>\n",
       "      <td>manual</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Hardware Fault</td>\n",
       "      <td>Computer System</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>999</th>\n",
       "      <td>14-Nov</td>\n",
       "      <td>WDD2221851A000495</td>\n",
       "      <td>urban-street</td>\n",
       "      <td>clear</td>\n",
       "      <td>manual</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Software Froze</td>\n",
       "      <td>Computer System</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1000</th>\n",
       "      <td>14-Nov</td>\n",
       "      <td>WDD2221851A000495</td>\n",
       "      <td>urban-street</td>\n",
       "      <td>clear</td>\n",
       "      <td>automatic</td>\n",
       "      <td>1.92</td>\n",
       "      <td>System Tuning and Calibration</td>\n",
       "      <td>Computer System</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1001</th>\n",
       "      <td>14-Nov</td>\n",
       "      <td>WDD2221851A000495</td>\n",
       "      <td>urban-street</td>\n",
       "      <td>clear</td>\n",
       "      <td>automatic</td>\n",
       "      <td>0.20</td>\n",
       "      <td>System Tuning and Calibration</td>\n",
       "      <td>Computer System</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1002</th>\n",
       "      <td>14-Nov</td>\n",
       "      <td>WDD2221851A000495</td>\n",
       "      <td>urban-street</td>\n",
       "      <td>clear</td>\n",
       "      <td>automatic</td>\n",
       "      <td>1.12</td>\n",
       "      <td>Hardware Fault</td>\n",
       "      <td>Computer System</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1003</th>\n",
       "      <td>14-Nov</td>\n",
       "      <td>WDD2221851A000495</td>\n",
       "      <td>urban-street</td>\n",
       "      <td>clear</td>\n",
       "      <td>manual</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Recklessly behaving agent</td>\n",
       "      <td>Controller</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1004</th>\n",
       "      <td>14-Nov</td>\n",
       "      <td>WDD2221851A000495</td>\n",
       "      <td>urban-street</td>\n",
       "      <td>clear</td>\n",
       "      <td>manual</td>\n",
       "      <td>NaN</td>\n",
       "      <td>System Tuning and Calibration</td>\n",
       "      <td>Computer System</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1005</th>\n",
       "      <td>14-Nov</td>\n",
       "      <td>WDD2221851A000495</td>\n",
       "      <td>urban-street</td>\n",
       "      <td>clear</td>\n",
       "      <td>manual</td>\n",
       "      <td>NaN</td>\n",
       "      <td>System Tuning and Calibration</td>\n",
       "      <td>Computer System</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1006</th>\n",
       "      <td>14-Nov</td>\n",
       "      <td>WDD2221851A000495</td>\n",
       "      <td>urban-street</td>\n",
       "      <td>clear</td>\n",
       "      <td>automatic</td>\n",
       "      <td>0.29</td>\n",
       "      <td>Software Froze</td>\n",
       "      <td>Computer System</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1007</th>\n",
       "      <td>14-Nov</td>\n",
       "      <td>WDD2221851A000495</td>\n",
       "      <td>urban-street</td>\n",
       "      <td>clear</td>\n",
       "      <td>automatic</td>\n",
       "      <td>1.48</td>\n",
       "      <td>System Tuning and Calibration</td>\n",
       "      <td>Computer System</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1008</th>\n",
       "      <td>14-Nov</td>\n",
       "      <td>WDD2221851A000495</td>\n",
       "      <td>urban-street</td>\n",
       "      <td>clear</td>\n",
       "      <td>automatic</td>\n",
       "      <td>0.45</td>\n",
       "      <td>Software Froze</td>\n",
       "      <td>Computer System</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1009</th>\n",
       "      <td>14-Nov</td>\n",
       "      <td>WDD2221851A000495</td>\n",
       "      <td>urban-street</td>\n",
       "      <td>clear</td>\n",
       "      <td>manual</td>\n",
       "      <td>NaN</td>\n",
       "      <td>System Tuning and Calibration</td>\n",
       "      <td>Computer System</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1010</th>\n",
       "      <td>14-Nov</td>\n",
       "      <td>WDD2221851A000495</td>\n",
       "      <td>urban-street</td>\n",
       "      <td>clear</td>\n",
       "      <td>manual</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Software Froze</td>\n",
       "      <td>Computer System</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1011</th>\n",
       "      <td>14-Nov</td>\n",
       "      <td>WDD2221851A000495</td>\n",
       "      <td>urban-street</td>\n",
       "      <td>clear</td>\n",
       "      <td>manual</td>\n",
       "      <td>NaN</td>\n",
       "      <td>System Tuning and Calibration</td>\n",
       "      <td>Computer System</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1012</th>\n",
       "      <td>14-Nov</td>\n",
       "      <td>WDD2221851A000495</td>\n",
       "      <td>urban-street</td>\n",
       "      <td>clear</td>\n",
       "      <td>automatic</td>\n",
       "      <td>1.85</td>\n",
       "      <td>System Tuning and Calibration</td>\n",
       "      <td>Computer System</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1013</th>\n",
       "      <td>14-Nov</td>\n",
       "      <td>WDD2221851A000495</td>\n",
       "      <td>urban-street</td>\n",
       "      <td>clear</td>\n",
       "      <td>automatic</td>\n",
       "      <td>2.11</td>\n",
       "      <td>Hardware Fault</td>\n",
       "      <td>Computer System</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1014</th>\n",
       "      <td>14-Nov</td>\n",
       "      <td>WDD2221851A000495</td>\n",
       "      <td>urban-street</td>\n",
       "      <td>clear</td>\n",
       "      <td>automatic</td>\n",
       "      <td>0.90</td>\n",
       "      <td>Software Froze</td>\n",
       "      <td>Computer System</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1015</th>\n",
       "      <td>14-Nov</td>\n",
       "      <td>WDD2221851A000495</td>\n",
       "      <td>urban-street</td>\n",
       "      <td>clear</td>\n",
       "      <td>automatic</td>\n",
       "      <td>1.25</td>\n",
       "      <td>System Tuning and Calibration</td>\n",
       "      <td>Computer System</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1016</th>\n",
       "      <td>14-Nov</td>\n",
       "      <td>WDD2221851A000495</td>\n",
       "      <td>urban-street</td>\n",
       "      <td>clear</td>\n",
       "      <td>manual</td>\n",
       "      <td>NaN</td>\n",
       "      <td>System Tuning and Calibration</td>\n",
       "      <td>Computer System</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1017</th>\n",
       "      <td>14-Nov</td>\n",
       "      <td>WDD2221851A000495</td>\n",
       "      <td>urban-street</td>\n",
       "      <td>clear</td>\n",
       "      <td>manual</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Hardware Fault</td>\n",
       "      <td>Computer System</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1018</th>\n",
       "      <td>14-Nov</td>\n",
       "      <td>WDD2221851A000495</td>\n",
       "      <td>urban-street</td>\n",
       "      <td>clear</td>\n",
       "      <td>manual</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Software Froze</td>\n",
       "      <td>Computer System</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1019</th>\n",
       "      <td>14-Nov</td>\n",
       "      <td>WDD2221851A000495</td>\n",
       "      <td>urban-street</td>\n",
       "      <td>clear</td>\n",
       "      <td>manual</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Hardware Fault</td>\n",
       "      <td>Computer System</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1020</th>\n",
       "      <td>14-Nov</td>\n",
       "      <td>WDD2221851A000495</td>\n",
       "      <td>urban-street</td>\n",
       "      <td>clear</td>\n",
       "      <td>manual</td>\n",
       "      <td>NaN</td>\n",
       "      <td>System Tuning and Calibration</td>\n",
       "      <td>Computer System</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1021</th>\n",
       "      <td>14-Nov</td>\n",
       "      <td>WDD2221851A000495</td>\n",
       "      <td>highway</td>\n",
       "      <td>clear</td>\n",
       "      <td>manual</td>\n",
       "      <td>NaN</td>\n",
       "      <td>System Tuning and Calibration</td>\n",
       "      <td>Computer System</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1022</th>\n",
       "      <td>14-Nov</td>\n",
       "      <td>WDD2221851A000495</td>\n",
       "      <td>urban-street</td>\n",
       "      <td>clear</td>\n",
       "      <td>manual</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Hardware Fault</td>\n",
       "      <td>Computer System</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1023</th>\n",
       "      <td>14-Nov</td>\n",
       "      <td>WDD2221851A000495</td>\n",
       "      <td>urban-street</td>\n",
       "      <td>clear</td>\n",
       "      <td>manual</td>\n",
       "      <td>NaN</td>\n",
       "      <td>System Tuning and Calibration</td>\n",
       "      <td>Computer System</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1024 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       Month                Car      Location Weather TypeOfTrigger  \\\n",
       "0     14-Sep  WDD2221851A001072  urban-street  cloudy     automatic   \n",
       "1     14-Sep  WDD2221851A001072  urban-street  cloudy     automatic   \n",
       "2     14-Sep  WDD2221851A001072  urban-street  cloudy     automatic   \n",
       "3     14-Sep  WDD2221851A001072  urban-street  cloudy     automatic   \n",
       "4     14-Sep  WDD2221851A001072  urban-street  cloudy     automatic   \n",
       "5     14-Sep  WDD2221851A001072  urban-street  cloudy     automatic   \n",
       "6     14-Sep  WDD2221851A001072  urban-street  cloudy     automatic   \n",
       "7     14-Sep  WDD2221851A001072  urban-street  cloudy     automatic   \n",
       "8     14-Sep  WDD2221851A001072  urban-street  cloudy     automatic   \n",
       "9     14-Sep  WDD2221851A001072  urban-street  cloudy     automatic   \n",
       "10    14-Sep  WDD2221851A001072  urban-street  cloudy     automatic   \n",
       "11    14-Sep  WDD2221851A001072  urban-street  cloudy     automatic   \n",
       "12    14-Sep  WDD2221851A001072  urban-street  cloudy     automatic   \n",
       "13    14-Sep  WDD2221851A001072  urban-street  cloudy     automatic   \n",
       "14    14-Sep  WDD2221851A001072  urban-street  cloudy     automatic   \n",
       "15    14-Sep  WDD2221851A001072  urban-street  cloudy     automatic   \n",
       "16    14-Sep  WDD2221851A001072  urban-street  cloudy        manual   \n",
       "17    14-Sep  WDD2221851A001072  urban-street  cloudy     automatic   \n",
       "18    14-Sep  WDD2221851A001072  urban-street  cloudy        manual   \n",
       "19    14-Sep  WDD2221851A001072  urban-street  cloudy     automatic   \n",
       "20    14-Sep  WDD2221851A001072  urban-street  cloudy        manual   \n",
       "21    14-Sep  WDD2221851A001072  urban-street  cloudy        manual   \n",
       "22    14-Sep  WDD2221851A001072  urban-street  cloudy     automatic   \n",
       "23    14-Sep  WDD2221851A001072  urban-street  cloudy        manual   \n",
       "24    14-Sep  WDD2221851A001072  urban-street  cloudy        manual   \n",
       "25    14-Sep  WDD2221851A001072  urban-street  cloudy     automatic   \n",
       "26    14-Sep  WDD2221851A001072  urban-street  cloudy        manual   \n",
       "27    14-Sep  WDD2221851A001072  urban-street  cloudy        manual   \n",
       "28    14-Sep  WDD2221851A001072  urban-street  cloudy     automatic   \n",
       "29    14-Sep  WDD2221851A001072  urban-street  cloudy        manual   \n",
       "...      ...                ...           ...     ...           ...   \n",
       "994   14-Nov  WDD2221851A000495  urban-street   clear     automatic   \n",
       "995   14-Nov  WDD2221851A000495  urban-street   clear     automatic   \n",
       "996   14-Nov  WDD2221851A000495  urban-street   clear     automatic   \n",
       "997   14-Nov  WDD2221851A000495  urban-street   clear        manual   \n",
       "998   14-Nov  WDD2221851A000495  urban-street   clear        manual   \n",
       "999   14-Nov  WDD2221851A000495  urban-street   clear        manual   \n",
       "1000  14-Nov  WDD2221851A000495  urban-street   clear     automatic   \n",
       "1001  14-Nov  WDD2221851A000495  urban-street   clear     automatic   \n",
       "1002  14-Nov  WDD2221851A000495  urban-street   clear     automatic   \n",
       "1003  14-Nov  WDD2221851A000495  urban-street   clear        manual   \n",
       "1004  14-Nov  WDD2221851A000495  urban-street   clear        manual   \n",
       "1005  14-Nov  WDD2221851A000495  urban-street   clear        manual   \n",
       "1006  14-Nov  WDD2221851A000495  urban-street   clear     automatic   \n",
       "1007  14-Nov  WDD2221851A000495  urban-street   clear     automatic   \n",
       "1008  14-Nov  WDD2221851A000495  urban-street   clear     automatic   \n",
       "1009  14-Nov  WDD2221851A000495  urban-street   clear        manual   \n",
       "1010  14-Nov  WDD2221851A000495  urban-street   clear        manual   \n",
       "1011  14-Nov  WDD2221851A000495  urban-street   clear        manual   \n",
       "1012  14-Nov  WDD2221851A000495  urban-street   clear     automatic   \n",
       "1013  14-Nov  WDD2221851A000495  urban-street   clear     automatic   \n",
       "1014  14-Nov  WDD2221851A000495  urban-street   clear     automatic   \n",
       "1015  14-Nov  WDD2221851A000495  urban-street   clear     automatic   \n",
       "1016  14-Nov  WDD2221851A000495  urban-street   clear        manual   \n",
       "1017  14-Nov  WDD2221851A000495  urban-street   clear        manual   \n",
       "1018  14-Nov  WDD2221851A000495  urban-street   clear        manual   \n",
       "1019  14-Nov  WDD2221851A000495  urban-street   clear        manual   \n",
       "1020  14-Nov  WDD2221851A000495  urban-street   clear        manual   \n",
       "1021  14-Nov  WDD2221851A000495       highway   clear        manual   \n",
       "1022  14-Nov  WDD2221851A000495  urban-street   clear        manual   \n",
       "1023  14-Nov  WDD2221851A000495  urban-street   clear        manual   \n",
       "\n",
       "      ReactionTime                                    Cause              Class  \n",
       "0             0.58        Incorrect Traffic Light Detection  Perception System  \n",
       "1             0.66          Adverse road surface conditions  Perception System  \n",
       "2             1.93              Position Estimation Failure  Perception System  \n",
       "3             0.65                        Emergency Vehicle  Perception System  \n",
       "4             0.64          Adverse road surface conditions  Perception System  \n",
       "5             0.88        Incorrect Traffic Light Detection  Perception System  \n",
       "6             0.41                        Emergency Vehicle  Perception System  \n",
       "7             1.45                        Emergency Vehicle  Perception System  \n",
       "8             0.07                           Software Froze    Computer System  \n",
       "9             0.37                           Hardware Fault    Computer System  \n",
       "10            1.55                        Emergency Vehicle  Perception System  \n",
       "11            0.04        Incorrect Traffic Light Detection  Perception System  \n",
       "12            0.40  Incorrect behavior prediction of others         Controller  \n",
       "13            1.86                        Emergency Vehicle  Perception System  \n",
       "14            0.21          Adverse road surface conditions  Perception System  \n",
       "15            0.51                Recklessly behaving agent         Controller  \n",
       "16             NaN                Recklessly behaving agent         Controller  \n",
       "17            0.38            System Tuning and Calibration    Computer System  \n",
       "18             NaN               Unwanted Driver Discomfort         Controller  \n",
       "19            0.22        Incorrect Traffic Light Detection  Perception System  \n",
       "20             NaN               Unwanted Driver Discomfort         Controller  \n",
       "21             NaN  Incorrect behavior prediction of others         Controller  \n",
       "22            1.38          Adverse road surface conditions  Perception System  \n",
       "23             NaN                           Software Froze    Computer System  \n",
       "24             NaN                Recklessly behaving agent         Controller  \n",
       "25            0.44               Unwanted Driver Discomfort         Controller  \n",
       "26             NaN                           Software Froze    Computer System  \n",
       "27             NaN                           Hardware Fault    Computer System  \n",
       "28            0.04               Unwanted Driver Discomfort         Controller  \n",
       "29             NaN                Recklessly behaving agent         Controller  \n",
       "...            ...                                      ...                ...  \n",
       "994           0.18                           Hardware Fault    Computer System  \n",
       "995          21.00            System Tuning and Calibration    Computer System  \n",
       "996           1.06            System Tuning and Calibration    Computer System  \n",
       "997            NaN            System Tuning and Calibration    Computer System  \n",
       "998            NaN                           Hardware Fault    Computer System  \n",
       "999            NaN                           Software Froze    Computer System  \n",
       "1000          1.92            System Tuning and Calibration    Computer System  \n",
       "1001          0.20            System Tuning and Calibration    Computer System  \n",
       "1002          1.12                           Hardware Fault    Computer System  \n",
       "1003           NaN                Recklessly behaving agent         Controller  \n",
       "1004           NaN            System Tuning and Calibration    Computer System  \n",
       "1005           NaN            System Tuning and Calibration    Computer System  \n",
       "1006          0.29                           Software Froze    Computer System  \n",
       "1007          1.48            System Tuning and Calibration    Computer System  \n",
       "1008          0.45                           Software Froze    Computer System  \n",
       "1009           NaN            System Tuning and Calibration    Computer System  \n",
       "1010           NaN                           Software Froze    Computer System  \n",
       "1011           NaN            System Tuning and Calibration    Computer System  \n",
       "1012          1.85            System Tuning and Calibration    Computer System  \n",
       "1013          2.11                           Hardware Fault    Computer System  \n",
       "1014          0.90                           Software Froze    Computer System  \n",
       "1015          1.25            System Tuning and Calibration    Computer System  \n",
       "1016           NaN            System Tuning and Calibration    Computer System  \n",
       "1017           NaN                           Hardware Fault    Computer System  \n",
       "1018           NaN                           Software Froze    Computer System  \n",
       "1019           NaN                           Hardware Fault    Computer System  \n",
       "1020           NaN            System Tuning and Calibration    Computer System  \n",
       "1021           NaN            System Tuning and Calibration    Computer System  \n",
       "1022           NaN                           Hardware Fault    Computer System  \n",
       "1023           NaN            System Tuning and Calibration    Computer System  \n",
       "\n",
       "[1024 rows x 8 columns]"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_disengagements"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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