Project 03- Data Analysis of Police Report.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "5dfe853b",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "a33398ca",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['Solarize_Light2', '_classic_test_patch', '_mpl-gallery', '_mpl-gallery-nogrid', 'bmh', 'classic', 'dark_background', 'fast', 'fivethirtyeight', 'ggplot', 'grayscale', 'seaborn-v0_8', 'seaborn-v0_8-bright', 'seaborn-v0_8-colorblind', 'seaborn-v0_8-dark', 'seaborn-v0_8-dark-palette', 'seaborn-v0_8-darkgrid', 'seaborn-v0_8-deep', 'seaborn-v0_8-muted', 'seaborn-v0_8-notebook', 'seaborn-v0_8-paper', 'seaborn-v0_8-pastel', 'seaborn-v0_8-poster', 'seaborn-v0_8-talk', 'seaborn-v0_8-ticks', 'seaborn-v0_8-white', 'seaborn-v0_8-whitegrid', 'tableau-colorblind10']\n"
     ]
    }
   ],
   "source": [
    "print(plt.style.available)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "2a501e47",
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.style.use('ggplot')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "a70d2da0",
   "metadata": {},
   "outputs": [
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       "      <th>violation_raw</th>\n",
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       "      <th>is_arrested</th>\n",
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       "<p>65535 rows × 15 columns</p>\n",
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      "text/plain": [
       "       stop_date stop_time  country_name driver_gender  driver_age_raw  \\\n",
       "0       1/2/2005      1:55           NaN             M          1985.0   \n",
       "1      1/18/2005      8:15           NaN             M          1965.0   \n",
       "2      1/23/2005     23:15           NaN             M          1972.0   \n",
       "3      2/20/2005     17:15           NaN             M          1986.0   \n",
       "4      3/14/2005     10:00           NaN             F          1984.0   \n",
       "...          ...       ...           ...           ...             ...   \n",
       "65530  12/6/2012     17:54           NaN             F          1987.0   \n",
       "65531  12/6/2012     22:22           NaN             M          1954.0   \n",
       "65532  12/6/2012     23:20           NaN             M          1985.0   \n",
       "65533  12/7/2012      0:23           NaN           NaN             NaN   \n",
       "65534  12/7/2012      0:30           NaN             F          1985.0   \n",
       "\n",
       "       driver_age driver_race                   violation_raw  violation  \\\n",
       "0            20.0       White                        Speeding   Speeding   \n",
       "1            40.0       White                        Speeding   Speeding   \n",
       "2            33.0       White                        Speeding   Speeding   \n",
       "3            19.0       White                Call for Service      Other   \n",
       "4            21.0       White                        Speeding   Speeding   \n",
       "...           ...         ...                             ...        ...   \n",
       "65530        25.0       White                        Speeding   Speeding   \n",
       "65531        58.0       White                        Speeding   Speeding   \n",
       "65532        27.0       Black  Equipment/Inspection Violation  Equipment   \n",
       "65533         NaN         NaN                             NaN        NaN   \n",
       "65534        27.0       White                        Speeding   Speeding   \n",
       "\n",
       "       search_conducted search_type   stop_outcome is_arrested stop_duration  \\\n",
       "0                 False         NaN       Citation       False      0-15 Min   \n",
       "1                 False         NaN       Citation       False      0-15 Min   \n",
       "2                 False         NaN       Citation       False      0-15 Min   \n",
       "3                 False         NaN  Arrest Driver        True     16-30 Min   \n",
       "4                 False         NaN       Citation       False      0-15 Min   \n",
       "...                 ...         ...            ...         ...           ...   \n",
       "65530             False         NaN       Citation       False      0-15 Min   \n",
       "65531             False         NaN        Warning       False      0-15 Min   \n",
       "65532             False         NaN       Citation       False      0-15 Min   \n",
       "65533             False         NaN            NaN         NaN           NaN   \n",
       "65534             False         NaN       Citation       False      0-15 Min   \n",
       "\n",
       "       drugs_related_stop  \n",
       "0                   False  \n",
       "1                   False  \n",
       "2                   False  \n",
       "3                   False  \n",
       "4                   False  \n",
       "...                   ...  \n",
       "65530               False  \n",
       "65531               False  \n",
       "65532               False  \n",
       "65533               False  \n",
       "65534               False  \n",
       "\n",
       "[65535 rows x 15 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df=pd.read_csv(r\"C:\\Users\\Aayush\\Downloads\\3. Police Data.csv\")\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "45bd56c3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(65535, 15)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "1e3b6718",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 65535 entries, 0 to 65534\n",
      "Data columns (total 15 columns):\n",
      " #   Column              Non-Null Count  Dtype  \n",
      "---  ------              --------------  -----  \n",
      " 0   stop_date           65535 non-null  object \n",
      " 1   stop_time           65535 non-null  object \n",
      " 2   country_name        0 non-null      float64\n",
      " 3   driver_gender       61474 non-null  object \n",
      " 4   driver_age_raw      61481 non-null  float64\n",
      " 5   driver_age          61228 non-null  float64\n",
      " 6   driver_race         61475 non-null  object \n",
      " 7   violation_raw       61475 non-null  object \n",
      " 8   violation           61475 non-null  object \n",
      " 9   search_conducted    65535 non-null  bool   \n",
      " 10  search_type         2479 non-null   object \n",
      " 11  stop_outcome        61475 non-null  object \n",
      " 12  is_arrested         61475 non-null  object \n",
      " 13  stop_duration       61475 non-null  object \n",
      " 14  drugs_related_stop  65535 non-null  bool   \n",
      "dtypes: bool(2), float64(3), object(10)\n",
      "memory usage: 6.6+ MB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "e3402b51",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "stop_date              object\n",
       "stop_time              object\n",
       "country_name          float64\n",
       "driver_gender          object\n",
       "driver_age_raw        float64\n",
       "driver_age            float64\n",
       "driver_race            object\n",
       "violation_raw          object\n",
       "violation              object\n",
       "search_conducted         bool\n",
       "search_type            object\n",
       "stop_outcome           object\n",
       "is_arrested            object\n",
       "stop_duration          object\n",
       "drugs_related_stop       bool\n",
       "dtype: object"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "a458910f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "stop_date                 0\n",
       "stop_time                 0\n",
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       "driver_gender          4061\n",
       "driver_age_raw         4054\n",
       "driver_age             4307\n",
       "driver_race            4060\n",
       "violation_raw          4060\n",
       "violation              4060\n",
       "search_conducted          0\n",
       "search_type           63056\n",
       "stop_outcome           4060\n",
       "is_arrested            4060\n",
       "stop_duration          4060\n",
       "drugs_related_stop        0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
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   "source": [
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "7c4f647a",
   "metadata": {},
   "outputs": [
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       "      <td>19.0</td>\n",
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       "      <td>Other</td>\n",
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       "      <td>Arrest Driver</td>\n",
       "      <td>True</td>\n",
       "      <td>16-30 Min</td>\n",
       "      <td>False</td>\n",
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       "      <th>4</th>\n",
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       "      <td>1984.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>White</td>\n",
       "      <td>Speeding</td>\n",
       "      <td>Speeding</td>\n",
       "      <td>False</td>\n",
       "      <td>Citation</td>\n",
       "      <td>False</td>\n",
       "      <td>0-15 Min</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>65530</th>\n",
       "      <td>12/6/2012</td>\n",
       "      <td>17:54</td>\n",
       "      <td>F</td>\n",
       "      <td>1987.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>White</td>\n",
       "      <td>Speeding</td>\n",
       "      <td>Speeding</td>\n",
       "      <td>False</td>\n",
       "      <td>Citation</td>\n",
       "      <td>False</td>\n",
       "      <td>0-15 Min</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>65531</th>\n",
       "      <td>12/6/2012</td>\n",
       "      <td>22:22</td>\n",
       "      <td>M</td>\n",
       "      <td>1954.0</td>\n",
       "      <td>58.0</td>\n",
       "      <td>White</td>\n",
       "      <td>Speeding</td>\n",
       "      <td>Speeding</td>\n",
       "      <td>False</td>\n",
       "      <td>Warning</td>\n",
       "      <td>False</td>\n",
       "      <td>0-15 Min</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>65532</th>\n",
       "      <td>12/6/2012</td>\n",
       "      <td>23:20</td>\n",
       "      <td>M</td>\n",
       "      <td>1985.0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>Black</td>\n",
       "      <td>Equipment/Inspection Violation</td>\n",
       "      <td>Equipment</td>\n",
       "      <td>False</td>\n",
       "      <td>Citation</td>\n",
       "      <td>False</td>\n",
       "      <td>0-15 Min</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>65533</th>\n",
       "      <td>12/7/2012</td>\n",
       "      <td>0:23</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>65534</th>\n",
       "      <td>12/7/2012</td>\n",
       "      <td>0:30</td>\n",
       "      <td>F</td>\n",
       "      <td>1985.0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>White</td>\n",
       "      <td>Speeding</td>\n",
       "      <td>Speeding</td>\n",
       "      <td>False</td>\n",
       "      <td>Citation</td>\n",
       "      <td>False</td>\n",
       "      <td>0-15 Min</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>65535 rows × 13 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       stop_date stop_time driver_gender  driver_age_raw  driver_age  \\\n",
       "0       1/2/2005      1:55             M          1985.0        20.0   \n",
       "1      1/18/2005      8:15             M          1965.0        40.0   \n",
       "2      1/23/2005     23:15             M          1972.0        33.0   \n",
       "3      2/20/2005     17:15             M          1986.0        19.0   \n",
       "4      3/14/2005     10:00             F          1984.0        21.0   \n",
       "...          ...       ...           ...             ...         ...   \n",
       "65530  12/6/2012     17:54             F          1987.0        25.0   \n",
       "65531  12/6/2012     22:22             M          1954.0        58.0   \n",
       "65532  12/6/2012     23:20             M          1985.0        27.0   \n",
       "65533  12/7/2012      0:23           NaN             NaN         NaN   \n",
       "65534  12/7/2012      0:30             F          1985.0        27.0   \n",
       "\n",
       "      driver_race                   violation_raw  violation  \\\n",
       "0           White                        Speeding   Speeding   \n",
       "1           White                        Speeding   Speeding   \n",
       "2           White                        Speeding   Speeding   \n",
       "3           White                Call for Service      Other   \n",
       "4           White                        Speeding   Speeding   \n",
       "...           ...                             ...        ...   \n",
       "65530       White                        Speeding   Speeding   \n",
       "65531       White                        Speeding   Speeding   \n",
       "65532       Black  Equipment/Inspection Violation  Equipment   \n",
       "65533         NaN                             NaN        NaN   \n",
       "65534       White                        Speeding   Speeding   \n",
       "\n",
       "       search_conducted   stop_outcome is_arrested stop_duration  \\\n",
       "0                 False       Citation       False      0-15 Min   \n",
       "1                 False       Citation       False      0-15 Min   \n",
       "2                 False       Citation       False      0-15 Min   \n",
       "3                 False  Arrest Driver        True     16-30 Min   \n",
       "4                 False       Citation       False      0-15 Min   \n",
       "...                 ...            ...         ...           ...   \n",
       "65530             False       Citation       False      0-15 Min   \n",
       "65531             False        Warning       False      0-15 Min   \n",
       "65532             False       Citation       False      0-15 Min   \n",
       "65533             False            NaN         NaN           NaN   \n",
       "65534             False       Citation       False      0-15 Min   \n",
       "\n",
       "       drugs_related_stop  \n",
       "0                   False  \n",
       "1                   False  \n",
       "2                   False  \n",
       "3                   False  \n",
       "4                   False  \n",
       "...                   ...  \n",
       "65530               False  \n",
       "65531               False  \n",
       "65532               False  \n",
       "65533               False  \n",
       "65534               False  \n",
       "\n",
       "[65535 rows x 13 columns]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Removing useless column\n",
    "\n",
    "df=df.drop(columns='country_name')\n",
    "df = df.drop(columns='search_type')\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "36b9ed15",
   "metadata": {},
   "source": [
    "## For speeding, were Men or Women stopped more often?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "b2323497",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "driver_gender\n",
       "M    25517\n",
       "F    11686\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vs = df[df['violation']=='Speeding'].driver_gender.value_counts()\n",
    "vs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "4d1273d6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax= vs.plot(kind = 'bar',ylabel='count',figsize=(10,6))\n",
    "\n",
    "for p in ax.patches:\n",
    "    ax.annotate(str(p.get_height()), (p.get_x() + p.get_width() / 2., p.get_height()),\n",
    "                ha='center', va='center', xytext=(0, 5), textcoords='offset points')\n",
    "\n",
    "# Show plot\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8700259b",
   "metadata": {},
   "source": [
    "Male were stopped twice of Female for speeding"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "eae8d1d7",
   "metadata": {},
   "source": [
    "## Violation & Race"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "31d92fa3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "driver_race\n",
       "White       30186\n",
       "Black        3658\n",
       "Hispanic     2062\n",
       "Asian        1173\n",
       "Other         125\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df['violation']=='Speeding'].driver_race.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "ac6cd8a5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "driver_race = ['White','Black','Hispanic','Asian','Other']\n",
    "counts = [30186, 3658,2062,1173,125]\n",
    "COLORS =['blue','orange','yellow','silver','grey']\n",
    "\n",
    "plt.figure(figsize=(10,5))\n",
    "plt.bar(driver_race,counts,color=COLORS)\n",
    "plt.xlabel('Driver Race')\n",
    "plt.title('Counts of Speeding Violations by Driver Race')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d78a41a6",
   "metadata": {},
   "source": [
    "## Does gender affect who gets seached during a stop?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "80cb082b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "driver_gender  search_conducted\n",
       "F              False               15944\n",
       "               True                  366\n",
       "M              False               43051\n",
       "               True                 2113\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby('driver_gender').search_conducted.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "6751e0fc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "data = {'driver_gender': ['F', 'F', 'M', 'M'],\n",
    "        'search_conducted': [False, True, False, True],\n",
    "        'counts': [15944, 366, 43051, 2113]}\n",
    "\n",
    "AA = pd.DataFrame(data)\n",
    "\n",
    "# Plotting\n",
    "AA.pivot_table(index='driver_gender', columns='search_conducted', values='counts', aggfunc='sum').plot(kind='bar', stacked=True)\n",
    "\n",
    "# Labeling\n",
    "plt.xlabel('Driver Gender')\n",
    "plt.ylabel('Counts')\n",
    "plt.title('Search Conducted by Driver Gender')\n",
    "plt.legend(title='Search Conducted')\n",
    "plt.xticks(rotation=0)\n",
    "\n",
    "# Display plot\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "08ab0f51",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "search_conducted\n",
       "False    63056\n",
       "True      2479\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.search_conducted.value_counts()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c42f1f65",
   "metadata": {},
   "source": [
    "Search was conducted 2479 times. Out of which Male were searched 2113 times and Female were searched 366 times. It shows that \n",
    "Gender do effect search conducted."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fce20b2b",
   "metadata": {},
   "source": [
    "### What is the mean stop_duration?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "ae56ad72",
   "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>stop_date</th>\n",
       "      <th>stop_time</th>\n",
       "      <th>driver_gender</th>\n",
       "      <th>driver_age_raw</th>\n",
       "      <th>driver_age</th>\n",
       "      <th>driver_race</th>\n",
       "      <th>violation_raw</th>\n",
       "      <th>violation</th>\n",
       "      <th>search_conducted</th>\n",
       "      <th>stop_outcome</th>\n",
       "      <th>is_arrested</th>\n",
       "      <th>stop_duration</th>\n",
       "      <th>drugs_related_stop</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1/2/2005</td>\n",
       "      <td>1:55</td>\n",
       "      <td>M</td>\n",
       "      <td>1985.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>White</td>\n",
       "      <td>Speeding</td>\n",
       "      <td>Speeding</td>\n",
       "      <td>False</td>\n",
       "      <td>Citation</td>\n",
       "      <td>False</td>\n",
       "      <td>0-15 Min</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1/18/2005</td>\n",
       "      <td>8:15</td>\n",
       "      <td>M</td>\n",
       "      <td>1965.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>White</td>\n",
       "      <td>Speeding</td>\n",
       "      <td>Speeding</td>\n",
       "      <td>False</td>\n",
       "      <td>Citation</td>\n",
       "      <td>False</td>\n",
       "      <td>0-15 Min</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1/23/2005</td>\n",
       "      <td>23:15</td>\n",
       "      <td>M</td>\n",
       "      <td>1972.0</td>\n",
       "      <td>33.0</td>\n",
       "      <td>White</td>\n",
       "      <td>Speeding</td>\n",
       "      <td>Speeding</td>\n",
       "      <td>False</td>\n",
       "      <td>Citation</td>\n",
       "      <td>False</td>\n",
       "      <td>0-15 Min</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2/20/2005</td>\n",
       "      <td>17:15</td>\n",
       "      <td>M</td>\n",
       "      <td>1986.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>White</td>\n",
       "      <td>Call for Service</td>\n",
       "      <td>Other</td>\n",
       "      <td>False</td>\n",
       "      <td>Arrest Driver</td>\n",
       "      <td>True</td>\n",
       "      <td>16-30 Min</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3/14/2005</td>\n",
       "      <td>10:00</td>\n",
       "      <td>F</td>\n",
       "      <td>1984.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>White</td>\n",
       "      <td>Speeding</td>\n",
       "      <td>Speeding</td>\n",
       "      <td>False</td>\n",
       "      <td>Citation</td>\n",
       "      <td>False</td>\n",
       "      <td>0-15 Min</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   stop_date stop_time driver_gender  driver_age_raw  driver_age driver_race  \\\n",
       "0   1/2/2005      1:55             M          1985.0        20.0       White   \n",
       "1  1/18/2005      8:15             M          1965.0        40.0       White   \n",
       "2  1/23/2005     23:15             M          1972.0        33.0       White   \n",
       "3  2/20/2005     17:15             M          1986.0        19.0       White   \n",
       "4  3/14/2005     10:00             F          1984.0        21.0       White   \n",
       "\n",
       "      violation_raw violation  search_conducted   stop_outcome is_arrested  \\\n",
       "0          Speeding  Speeding             False       Citation       False   \n",
       "1          Speeding  Speeding             False       Citation       False   \n",
       "2          Speeding  Speeding             False       Citation       False   \n",
       "3  Call for Service     Other             False  Arrest Driver        True   \n",
       "4          Speeding  Speeding             False       Citation       False   \n",
       "\n",
       "  stop_duration  drugs_related_stop  \n",
       "0      0-15 Min               False  \n",
       "1      0-15 Min               False  \n",
       "2      0-15 Min               False  \n",
       "3     16-30 Min               False  \n",
       "4      0-15 Min               False  "
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "306a13d4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dtype('O')"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['stop_duration'].dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "f51d6826",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "stop_duration\n",
       "0-15 Min     47379\n",
       "16-30 Min    11448\n",
       "30+ Min       2647\n",
       "2                1\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['stop_duration'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "d79fddd2",
   "metadata": {},
   "outputs": [],
   "source": [
    "df['stop_duration']=df['stop_duration'].map({'0-15 Min':7.5,'16-30 Min':23,'30+ Min':30})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "6ea397a4",
   "metadata": {},
   "outputs": [
    {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
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       "      <th></th>\n",
       "      <th>stop_date</th>\n",
       "      <th>stop_time</th>\n",
       "      <th>driver_gender</th>\n",
       "      <th>driver_age_raw</th>\n",
       "      <th>driver_age</th>\n",
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       "      <th>stop_duration</th>\n",
       "      <th>drugs_related_stop</th>\n",
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       "      <th>0</th>\n",
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       "      <td>Speeding</td>\n",
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       "      <td>7.5</td>\n",
       "      <td>False</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1/18/2005</td>\n",
       "      <td>8:15</td>\n",
       "      <td>M</td>\n",
       "      <td>1965.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>White</td>\n",
       "      <td>Speeding</td>\n",
       "      <td>Speeding</td>\n",
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       "      <td>7.5</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1/23/2005</td>\n",
       "      <td>23:15</td>\n",
       "      <td>M</td>\n",
       "      <td>1972.0</td>\n",
       "      <td>33.0</td>\n",
       "      <td>White</td>\n",
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       "      <td>False</td>\n",
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       "      <td>False</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2/20/2005</td>\n",
       "      <td>17:15</td>\n",
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       "      <td>23.0</td>\n",
       "      <td>False</td>\n",
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       "      <th>4</th>\n",
       "      <td>3/14/2005</td>\n",
       "      <td>10:00</td>\n",
       "      <td>F</td>\n",
       "      <td>1984.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>White</td>\n",
       "      <td>Speeding</td>\n",
       "      <td>Speeding</td>\n",
       "      <td>False</td>\n",
       "      <td>Citation</td>\n",
       "      <td>False</td>\n",
       "      <td>7.5</td>\n",
       "      <td>False</td>\n",
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       "    <tr>\n",
       "      <th>...</th>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>65530</th>\n",
       "      <td>12/6/2012</td>\n",
       "      <td>17:54</td>\n",
       "      <td>F</td>\n",
       "      <td>1987.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>White</td>\n",
       "      <td>Speeding</td>\n",
       "      <td>Speeding</td>\n",
       "      <td>False</td>\n",
       "      <td>Citation</td>\n",
       "      <td>False</td>\n",
       "      <td>7.5</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>65531</th>\n",
       "      <td>12/6/2012</td>\n",
       "      <td>22:22</td>\n",
       "      <td>M</td>\n",
       "      <td>1954.0</td>\n",
       "      <td>58.0</td>\n",
       "      <td>White</td>\n",
       "      <td>Speeding</td>\n",
       "      <td>Speeding</td>\n",
       "      <td>False</td>\n",
       "      <td>Warning</td>\n",
       "      <td>False</td>\n",
       "      <td>7.5</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>65532</th>\n",
       "      <td>12/6/2012</td>\n",
       "      <td>23:20</td>\n",
       "      <td>M</td>\n",
       "      <td>1985.0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>Black</td>\n",
       "      <td>Equipment/Inspection Violation</td>\n",
       "      <td>Equipment</td>\n",
       "      <td>False</td>\n",
       "      <td>Citation</td>\n",
       "      <td>False</td>\n",
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       "      <td>False</td>\n",
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       "    <tr>\n",
       "      <th>65533</th>\n",
       "      <td>12/7/2012</td>\n",
       "      <td>0:23</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>False</td>\n",
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       "      <th>65534</th>\n",
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       "<p>65535 rows × 13 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       stop_date stop_time driver_gender  driver_age_raw  driver_age  \\\n",
       "0       1/2/2005      1:55             M          1985.0        20.0   \n",
       "1      1/18/2005      8:15             M          1965.0        40.0   \n",
       "2      1/23/2005     23:15             M          1972.0        33.0   \n",
       "3      2/20/2005     17:15             M          1986.0        19.0   \n",
       "4      3/14/2005     10:00             F          1984.0        21.0   \n",
       "...          ...       ...           ...             ...         ...   \n",
       "65530  12/6/2012     17:54             F          1987.0        25.0   \n",
       "65531  12/6/2012     22:22             M          1954.0        58.0   \n",
       "65532  12/6/2012     23:20             M          1985.0        27.0   \n",
       "65533  12/7/2012      0:23           NaN             NaN         NaN   \n",
       "65534  12/7/2012      0:30             F          1985.0        27.0   \n",
       "\n",
       "      driver_race                   violation_raw  violation  \\\n",
       "0           White                        Speeding   Speeding   \n",
       "1           White                        Speeding   Speeding   \n",
       "2           White                        Speeding   Speeding   \n",
       "3           White                Call for Service      Other   \n",
       "4           White                        Speeding   Speeding   \n",
       "...           ...                             ...        ...   \n",
       "65530       White                        Speeding   Speeding   \n",
       "65531       White                        Speeding   Speeding   \n",
       "65532       Black  Equipment/Inspection Violation  Equipment   \n",
       "65533         NaN                             NaN        NaN   \n",
       "65534       White                        Speeding   Speeding   \n",
       "\n",
       "       search_conducted   stop_outcome is_arrested  stop_duration  \\\n",
       "0                 False       Citation       False            7.5   \n",
       "1                 False       Citation       False            7.5   \n",
       "2                 False       Citation       False            7.5   \n",
       "3                 False  Arrest Driver        True           23.0   \n",
       "4                 False       Citation       False            7.5   \n",
       "...                 ...            ...         ...            ...   \n",
       "65530             False       Citation       False            7.5   \n",
       "65531             False        Warning       False            7.5   \n",
       "65532             False       Citation       False            7.5   \n",
       "65533             False            NaN         NaN            NaN   \n",
       "65534             False       Citation       False            7.5   \n",
       "\n",
       "       drugs_related_stop  \n",
       "0                   False  \n",
       "1                   False  \n",
       "2                   False  \n",
       "3                   False  \n",
       "4                   False  \n",
       "...                   ...  \n",
       "65530               False  \n",
       "65531               False  \n",
       "65532               False  \n",
       "65533               False  \n",
       "65534               False  \n",
       "\n",
       "[65535 rows x 13 columns]"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "ed5d8850",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "11.355312815173894"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['stop_duration'].mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "655add8a",
   "metadata": {},
   "source": [
    "### Analyze stop duration\n",
    "\n",
    "Who were stopped most?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "527a4b3c",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th colspan=\"4\" halign=\"left\">driver_age</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>mean</th>\n",
       "      <th>max</th>\n",
       "      <th>min</th>\n",
       "      <th>count</th>\n",
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       "    <tr>\n",
       "      <th>stop_duration</th>\n",
       "      <th></th>\n",
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       "      <th></th>\n",
       "      <th></th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0-15 Min</th>\n",
       "      <td>34.021884</td>\n",
       "      <td>88.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>47203</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16-30 Min</th>\n",
       "      <td>34.120067</td>\n",
       "      <td>88.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>11377</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>52.000000</td>\n",
       "      <td>52.0</td>\n",
       "      <td>52.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30+ Min</th>\n",
       "      <td>36.548448</td>\n",
       "      <td>86.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>2642</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              driver_age                   \n",
       "                    mean   max   min  count\n",
       "stop_duration                              \n",
       "0-15 Min       34.021884  88.0  15.0  47203\n",
       "16-30 Min      34.120067  88.0  15.0  11377\n",
       "2              52.000000  52.0  52.0      1\n",
       "30+ Min        36.548448  86.0  15.0   2642"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stop_duration_stats = df.groupby('stop_duration').agg({'driver_age':['mean','max','min','count']})\n",
    "stop_duration_stats"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "65527462",
   "metadata": {},
   "source": [
    "What were the outcome after stopping vehicle?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "97e3a595",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
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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>stop_outcome</th>\n",
       "      <th>Count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Arrest Driver</td>\n",
       "      <td>2009</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Arrest Passenger</td>\n",
       "      <td>251</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Citation</td>\n",
       "      <td>56564</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>N/D</td>\n",
       "      <td>423</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>No Action</td>\n",
       "      <td>339</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Warning</td>\n",
       "      <td>1889</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       stop_outcome  Count\n",
       "0     Arrest Driver   2009\n",
       "1  Arrest Passenger    251\n",
       "2          Citation  56564\n",
       "3               N/D    423\n",
       "4         No Action    339\n",
       "5           Warning   1889"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stop_outcome_distribution = df.groupby('stop_outcome').size().reset_index(name='Count')\n",
    "stop_outcome_distribution"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "cda912d9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.barplot(x='stop_outcome', y='Count',data = stop_outcome_distribution)\n",
    "\n",
    "plt.xlabel('Stop Outcome')\n",
    "plt.ylabel('Count')\n",
    "plt.title('Distribution of Stop Outcomes')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1112d552",
   "metadata": {},
   "source": [
    "A criminal citation is a method of charging a person with committing a criminal offense without going through the standard formalities of arrest, fingerprinting, processing, taking an individual before a judge to be arraigned, and posting of bond. If a person receives a citation, instead of being arrested and sent to jail, the person receives a summons to report to a specific location at a later date to handle the charge(s)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e9d58f8a",
   "metadata": {},
   "source": [
    "### Compare the age distributions for each violation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "83a8e933",
   "metadata": {},
   "outputs": [],
   "source": [
    "g=df.groupby('driver_age')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "aaa045bd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "driver_age  violation          \n",
       "15.0        Moving violation        4\n",
       "            Speeding                1\n",
       "16.0        Speeding               18\n",
       "            Moving violation        6\n",
       "            Registration/plates     5\n",
       "                                   ..\n",
       "85.0        Moving violation        1\n",
       "86.0        Speeding                3\n",
       "            Moving violation        2\n",
       "            Other                   1\n",
       "88.0        Speeding                2\n",
       "Name: count, Length: 333, dtype: int64"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "g.violation.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "6c1aeae2",
   "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>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>25%</th>\n",
       "      <th>50%</th>\n",
       "      <th>75%</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>violation</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Equipment</th>\n",
       "      <td>6507.0</td>\n",
       "      <td>31.682957</td>\n",
       "      <td>11.380671</td>\n",
       "      <td>16.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>39.0</td>\n",
       "      <td>81.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Moving violation</th>\n",
       "      <td>11876.0</td>\n",
       "      <td>36.736443</td>\n",
       "      <td>13.258350</td>\n",
       "      <td>15.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>47.0</td>\n",
       "      <td>86.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Other</th>\n",
       "      <td>3477.0</td>\n",
       "      <td>40.362381</td>\n",
       "      <td>12.754423</td>\n",
       "      <td>16.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>41.0</td>\n",
       "      <td>50.0</td>\n",
       "      <td>86.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Registration/plates</th>\n",
       "      <td>2240.0</td>\n",
       "      <td>32.656696</td>\n",
       "      <td>11.150780</td>\n",
       "      <td>16.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>74.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Seat belt</th>\n",
       "      <td>3.0</td>\n",
       "      <td>30.333333</td>\n",
       "      <td>10.214369</td>\n",
       "      <td>23.0</td>\n",
       "      <td>24.5</td>\n",
       "      <td>26.0</td>\n",
       "      <td>34.0</td>\n",
       "      <td>42.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Speeding</th>\n",
       "      <td>37120.0</td>\n",
       "      <td>33.262581</td>\n",
       "      <td>12.615781</td>\n",
       "      <td>15.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>42.0</td>\n",
       "      <td>88.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                       count       mean        std   min   25%   50%   75%  \\\n",
       "violation                                                                    \n",
       "Equipment             6507.0  31.682957  11.380671  16.0  23.0  28.0  39.0   \n",
       "Moving violation     11876.0  36.736443  13.258350  15.0  25.0  35.0  47.0   \n",
       "Other                 3477.0  40.362381  12.754423  16.0  30.0  41.0  50.0   \n",
       "Registration/plates   2240.0  32.656696  11.150780  16.0  24.0  30.0  40.0   \n",
       "Seat belt                3.0  30.333333  10.214369  23.0  24.5  26.0  34.0   \n",
       "Speeding             37120.0  33.262581  12.615781  15.0  23.0  30.0  42.0   \n",
       "\n",
       "                      max  \n",
       "violation                  \n",
       "Equipment            81.0  \n",
       "Moving violation     86.0  \n",
       "Other                86.0  \n",
       "Registration/plates  74.0  \n",
       "Seat belt            42.0  \n",
       "Speeding             88.0  "
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby('violation').driver_age.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "a8a22b94",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Speeding', 'Other', 'Equipment', 'Moving violation', nan,\n",
       "       'Registration/plates', 'Seat belt'], dtype=object)"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['violation'].unique()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "aafd227e",
   "metadata": {},
   "source": [
    "### Explore driver demographic"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "7f770bca",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>stop_date</th>\n",
       "      <th>stop_time</th>\n",
       "      <th>country_name</th>\n",
       "      <th>driver_gender</th>\n",
       "      <th>driver_age_raw</th>\n",
       "      <th>driver_age</th>\n",
       "      <th>driver_race</th>\n",
       "      <th>violation_raw</th>\n",
       "      <th>violation</th>\n",
       "      <th>search_conducted</th>\n",
       "      <th>search_type</th>\n",
       "      <th>stop_outcome</th>\n",
       "      <th>is_arrested</th>\n",
       "      <th>stop_duration</th>\n",
       "      <th>drugs_related_stop</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
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       "      <th>0</th>\n",
       "      <td>1/2/2005</td>\n",
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       "      <td>1/18/2005</td>\n",
       "      <td>8:15</td>\n",
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       "      <td>M</td>\n",
       "      <td>1965.0</td>\n",
       "      <td>40.0</td>\n",
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       "      <td>Speeding</td>\n",
       "      <td>Speeding</td>\n",
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       "      <td>Citation</td>\n",
       "      <td>False</td>\n",
       "      <td>0-15 Min</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1/23/2005</td>\n",
       "      <td>23:15</td>\n",
       "      <td>NaN</td>\n",
       "      <td>M</td>\n",
       "      <td>1972.0</td>\n",
       "      <td>33.0</td>\n",
       "      <td>White</td>\n",
       "      <td>Speeding</td>\n",
       "      <td>Speeding</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Citation</td>\n",
       "      <td>False</td>\n",
       "      <td>0-15 Min</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2/20/2005</td>\n",
       "      <td>17:15</td>\n",
       "      <td>NaN</td>\n",
       "      <td>M</td>\n",
       "      <td>1986.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>White</td>\n",
       "      <td>Call for Service</td>\n",
       "      <td>Other</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Arrest Driver</td>\n",
       "      <td>True</td>\n",
       "      <td>16-30 Min</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3/14/2005</td>\n",
       "      <td>10:00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>F</td>\n",
       "      <td>1984.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>White</td>\n",
       "      <td>Speeding</td>\n",
       "      <td>Speeding</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Citation</td>\n",
       "      <td>False</td>\n",
       "      <td>0-15 Min</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>65530</th>\n",
       "      <td>12/6/2012</td>\n",
       "      <td>17:54</td>\n",
       "      <td>NaN</td>\n",
       "      <td>F</td>\n",
       "      <td>1987.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>White</td>\n",
       "      <td>Speeding</td>\n",
       "      <td>Speeding</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Citation</td>\n",
       "      <td>False</td>\n",
       "      <td>0-15 Min</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>65531</th>\n",
       "      <td>12/6/2012</td>\n",
       "      <td>22:22</td>\n",
       "      <td>NaN</td>\n",
       "      <td>M</td>\n",
       "      <td>1954.0</td>\n",
       "      <td>58.0</td>\n",
       "      <td>White</td>\n",
       "      <td>Speeding</td>\n",
       "      <td>Speeding</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Warning</td>\n",
       "      <td>False</td>\n",
       "      <td>0-15 Min</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>65532</th>\n",
       "      <td>12/6/2012</td>\n",
       "      <td>23:20</td>\n",
       "      <td>NaN</td>\n",
       "      <td>M</td>\n",
       "      <td>1985.0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>Black</td>\n",
       "      <td>Equipment/Inspection Violation</td>\n",
       "      <td>Equipment</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Citation</td>\n",
       "      <td>False</td>\n",
       "      <td>0-15 Min</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>65533</th>\n",
       "      <td>12/7/2012</td>\n",
       "      <td>0:23</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>False</td>\n",
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       "    <tr>\n",
       "      <th>65534</th>\n",
       "      <td>12/7/2012</td>\n",
       "      <td>0:30</td>\n",
       "      <td>NaN</td>\n",
       "      <td>F</td>\n",
       "      <td>1985.0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>White</td>\n",
       "      <td>Speeding</td>\n",
       "      <td>Speeding</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Citation</td>\n",
       "      <td>False</td>\n",
       "      <td>0-15 Min</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>65535 rows × 15 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       stop_date stop_time  country_name driver_gender  driver_age_raw  \\\n",
       "0       1/2/2005      1:55           NaN             M          1985.0   \n",
       "1      1/18/2005      8:15           NaN             M          1965.0   \n",
       "2      1/23/2005     23:15           NaN             M          1972.0   \n",
       "3      2/20/2005     17:15           NaN             M          1986.0   \n",
       "4      3/14/2005     10:00           NaN             F          1984.0   \n",
       "...          ...       ...           ...           ...             ...   \n",
       "65530  12/6/2012     17:54           NaN             F          1987.0   \n",
       "65531  12/6/2012     22:22           NaN             M          1954.0   \n",
       "65532  12/6/2012     23:20           NaN             M          1985.0   \n",
       "65533  12/7/2012      0:23           NaN           NaN             NaN   \n",
       "65534  12/7/2012      0:30           NaN             F          1985.0   \n",
       "\n",
       "       driver_age driver_race                   violation_raw  violation  \\\n",
       "0            20.0       White                        Speeding   Speeding   \n",
       "1            40.0       White                        Speeding   Speeding   \n",
       "2            33.0       White                        Speeding   Speeding   \n",
       "3            19.0       White                Call for Service      Other   \n",
       "4            21.0       White                        Speeding   Speeding   \n",
       "...           ...         ...                             ...        ...   \n",
       "65530        25.0       White                        Speeding   Speeding   \n",
       "65531        58.0       White                        Speeding   Speeding   \n",
       "65532        27.0       Black  Equipment/Inspection Violation  Equipment   \n",
       "65533         NaN         NaN                             NaN        NaN   \n",
       "65534        27.0       White                        Speeding   Speeding   \n",
       "\n",
       "       search_conducted search_type   stop_outcome is_arrested stop_duration  \\\n",
       "0                 False         NaN       Citation       False      0-15 Min   \n",
       "1                 False         NaN       Citation       False      0-15 Min   \n",
       "2                 False         NaN       Citation       False      0-15 Min   \n",
       "3                 False         NaN  Arrest Driver        True     16-30 Min   \n",
       "4                 False         NaN       Citation       False      0-15 Min   \n",
       "...                 ...         ...            ...         ...           ...   \n",
       "65530             False         NaN       Citation       False      0-15 Min   \n",
       "65531             False         NaN        Warning       False      0-15 Min   \n",
       "65532             False         NaN       Citation       False      0-15 Min   \n",
       "65533             False         NaN            NaN         NaN           NaN   \n",
       "65534             False         NaN       Citation       False      0-15 Min   \n",
       "\n",
       "       drugs_related_stop  \n",
       "0                   False  \n",
       "1                   False  \n",
       "2                   False  \n",
       "3                   False  \n",
       "4                   False  \n",
       "...                   ...  \n",
       "65530               False  \n",
       "65531               False  \n",
       "65532               False  \n",
       "65533               False  \n",
       "65534               False  \n",
       "\n",
       "[65535 rows x 15 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "cfde9192",
   "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 tr th {\n",
       "        text-align: left;\n",
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       "\n",
       "    .dataframe thead tr:last-of-type th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th colspan=\"3\" halign=\"left\">driver_age</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>mean</th>\n",
       "      <th>median</th>\n",
       "      <th>count</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>driver_gender</th>\n",
       "      <th>driver_race</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">F</th>\n",
       "      <th>Asian</th>\n",
       "      <td>31.978082</td>\n",
       "      <td>29.0</td>\n",
       "      <td>365</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Black</th>\n",
       "      <td>31.091355</td>\n",
       "      <td>28.0</td>\n",
       "      <td>1631</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Hispanic</th>\n",
       "      <td>30.685149</td>\n",
       "      <td>28.0</td>\n",
       "      <td>1010</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Other</th>\n",
       "      <td>30.913043</td>\n",
       "      <td>26.0</td>\n",
       "      <td>23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>White</th>\n",
       "      <td>32.726408</td>\n",
       "      <td>29.0</td>\n",
       "      <td>13246</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">M</th>\n",
       "      <th>Asian</th>\n",
       "      <td>34.004728</td>\n",
       "      <td>31.0</td>\n",
       "      <td>1269</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Black</th>\n",
       "      <td>34.366808</td>\n",
       "      <td>32.0</td>\n",
       "      <td>6592</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Hispanic</th>\n",
       "      <td>32.291931</td>\n",
       "      <td>30.0</td>\n",
       "      <td>4573</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Other</th>\n",
       "      <td>35.390374</td>\n",
       "      <td>33.0</td>\n",
       "      <td>187</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>White</th>\n",
       "      <td>35.239157</td>\n",
       "      <td>32.0</td>\n",
       "      <td>32326</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                          driver_age              \n",
       "                                mean median  count\n",
       "driver_gender driver_race                         \n",
       "F             Asian        31.978082   29.0    365\n",
       "              Black        31.091355   28.0   1631\n",
       "              Hispanic     30.685149   28.0   1010\n",
       "              Other        30.913043   26.0     23\n",
       "              White        32.726408   29.0  13246\n",
       "M             Asian        34.004728   31.0   1269\n",
       "              Black        34.366808   32.0   6592\n",
       "              Hispanic     32.291931   30.0   4573\n",
       "              Other        35.390374   33.0    187\n",
       "              White        35.239157   32.0  32326"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1=df.groupby(['driver_gender','driver_race']).agg({'driver_age':['mean','median','count']})\n",
    "df1"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dbdb314d",
   "metadata": {},
   "source": [
    "### Search Conducted Analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "edd0f1fb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "search_conducted\n",
       "False    96.217288\n",
       "True      3.782712\n",
       "Name: proportion, dtype: float64"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Search Conducted Analysis:\n",
    "\n",
    "search_conducted_proportion = df['search_conducted'].value_counts(normalize=True)*100\n",
    "search_conducted_proportion"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "0d42dc26",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "search_conducted_proportion.plot.pie(figsize=(10, 6), autopct='%1.1f%%', startangle=140)\n",
    "\n",
    "# Adding title\n",
    "plt.title('Search Conducted Proportion')\n",
    "\n",
    "# Display plot\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4df1642b",
   "metadata": {},
   "source": [
    "### drugs_related stop Analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "2829b8c4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "drugs_related_stop\n",
       "False    65017\n",
       "True       518\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# drugs_related stop Analysis\n",
    "drugs_related_stop_frequency = df['drugs_related_stop'].value_counts()\n",
    "drugs_related_stop_frequency"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "8d357f47",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr:last-of-type th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>drugs_related_stop</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>count</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>driver_gender</th>\n",
       "      <th>drugs_related_stop</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">F</th>\n",
       "      <th>False</th>\n",
       "      <td>16254</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>True</th>\n",
       "      <td>56</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">M</th>\n",
       "      <th>False</th>\n",
       "      <td>44702</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>True</th>\n",
       "      <td>462</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                 drugs_related_stop\n",
       "                                              count\n",
       "driver_gender drugs_related_stop                   \n",
       "F             False                           16254\n",
       "              True                               56\n",
       "M             False                           44702\n",
       "              True                              462"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "drs= df.groupby(['driver_gender','drugs_related_stop']).agg({'drugs_related_stop':['count']})\n",
    "drs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "846cc27b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "is_arrested\n",
       "False    59215\n",
       "True      2260\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['is_arrested'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "b9ff78a2",
   "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 tr th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr:last-of-type th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>is_arrested</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>count</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>driver_gender</th>\n",
       "      <th>is_arrested</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">F</th>\n",
       "      <th>False</th>\n",
       "      <td>15846</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>True</th>\n",
       "      <td>464</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">M</th>\n",
       "      <th>False</th>\n",
       "      <td>43368</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>True</th>\n",
       "      <td>1796</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                          is_arrested\n",
       "                                count\n",
       "driver_gender is_arrested            \n",
       "F             False             15846\n",
       "              True                464\n",
       "M             False             43368\n",
       "              True               1796"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "drs= df.groupby(['driver_gender','is_arrested']).agg({'is_arrested':['count']})\n",
    "drs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6e2bd259",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.11.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}