project.ipynb

{
 "cells": [
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# About the data"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "On a typical day in the United States, police officers make more than 50,000 traffic stops. Our team is gathering, analyzing, and releasing records from millions of traffic stops by law enforcement agencies across the country. Our goal is to help researchers, journalists, and policymakers investigate and improve interactions between police and the public.\n",
    "\n",
    "https://openpolicing.stanford.edu/"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>stop_date</th>\n",
       "      <th>stop_time</th>\n",
       "      <th>county_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",
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       "      <td>2005-01-02</td>\n",
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       "      <td>0-15 Min</td>\n",
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       "      <th>2</th>\n",
       "      <td>2005-01-23</td>\n",
       "      <td>23:15</td>\n",
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       "      <td>M</td>\n",
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       "      <td>33.0</td>\n",
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       "      <td>Speeding</td>\n",
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       "      <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>2005-03-14</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",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    stop_date stop_time  county_name driver_gender  driver_age_raw  \\\n",
       "0  2005-01-02      1:55          NaN             M          1985.0   \n",
       "1  2005-01-18      8:15          NaN             M          1965.0   \n",
       "2  2005-01-23     23:15          NaN             M          1972.0   \n",
       "3  2005-02-20     17:15          NaN             M          1986.0   \n",
       "4  2005-03-14     10:00          NaN             F          1984.0   \n",
       "\n",
       "   driver_age driver_race     violation_raw violation  search_conducted  \\\n",
       "0        20.0       White          Speeding  Speeding             False   \n",
       "1        40.0       White          Speeding  Speeding             False   \n",
       "2        33.0       White          Speeding  Speeding             False   \n",
       "3        19.0       White  Call for Service     Other             False   \n",
       "4        21.0       White          Speeding  Speeding             False   \n",
       "\n",
       "  search_type   stop_outcome is_arrested stop_duration  drugs_related_stop  \n",
       "0         NaN       Citation       False      0-15 Min               False  \n",
       "1         NaN       Citation       False      0-15 Min               False  \n",
       "2         NaN       Citation       False      0-15 Min               False  \n",
       "3         NaN  Arrest Driver        True     16-30 Min               False  \n",
       "4         NaN       Citation       False      0-15 Min               False  "
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df=pd.read_csv('/Users/yangzongkun/Desktop/sd/police.csv')\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(91741, 15)"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
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   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "stop_date              object\n",
       "stop_time              object\n",
       "county_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": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
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       "<p>91741 rows × 15 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       stop_date  stop_time  county_name  driver_gender  driver_age_raw  \\\n",
       "0          False      False         True          False           False   \n",
       "1          False      False         True          False           False   \n",
       "2          False      False         True          False           False   \n",
       "3          False      False         True          False           False   \n",
       "4          False      False         True          False           False   \n",
       "...          ...        ...          ...            ...             ...   \n",
       "91736      False      False         True          False           False   \n",
       "91737      False      False         True          False           False   \n",
       "91738      False      False         True          False           False   \n",
       "91739      False      False         True          False           False   \n",
       "91740      False      False         True          False           False   \n",
       "\n",
       "       driver_age  driver_race  violation_raw  violation  search_conducted  \\\n",
       "0           False        False          False      False             False   \n",
       "1           False        False          False      False             False   \n",
       "2           False        False          False      False             False   \n",
       "3           False        False          False      False             False   \n",
       "4           False        False          False      False             False   \n",
       "...           ...          ...            ...        ...               ...   \n",
       "91736       False        False          False      False             False   \n",
       "91737       False        False          False      False             False   \n",
       "91738       False        False          False      False             False   \n",
       "91739       False        False          False      False             False   \n",
       "91740       False        False          False      False             False   \n",
       "\n",
       "       search_type  stop_outcome  is_arrested  stop_duration  \\\n",
       "0             True         False        False          False   \n",
       "1             True         False        False          False   \n",
       "2             True         False        False          False   \n",
       "3             True         False        False          False   \n",
       "4             True         False        False          False   \n",
       "...            ...           ...          ...            ...   \n",
       "91736         True         False        False          False   \n",
       "91737         True         False        False          False   \n",
       "91738         True         False        False          False   \n",
       "91739         True         False        False          False   \n",
       "91740         True         False        False          False   \n",
       "\n",
       "       drugs_related_stop  \n",
       "0                   False  \n",
       "1                   False  \n",
       "2                   False  \n",
       "3                   False  \n",
       "4                   False  \n",
       "...                   ...  \n",
       "91736               False  \n",
       "91737               False  \n",
       "91738               False  \n",
       "91739               False  \n",
       "91740               False  \n",
       "\n",
       "[91741 rows x 15 columns]"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "stop_date                 0\n",
       "stop_time                 0\n",
       "county_name           91741\n",
       "driver_gender          5335\n",
       "driver_age_raw         5327\n",
       "driver_age             5621\n",
       "driver_race            5333\n",
       "violation_raw          5333\n",
       "violation              5333\n",
       "search_conducted          0\n",
       "search_type           88545\n",
       "stop_outcome           5333\n",
       "is_arrested            5333\n",
       "stop_duration          5333\n",
       "drugs_related_stop        0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().sum()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 1.Remove the column that only contains missing values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "\n",
    "#There is more than one way to do everything in pandas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [],
   "source": [
    "#method 1\n",
    "\n",
    "df.drop('county_name',axis='columns',inplace=True)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
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       "<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>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",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2005-01-02</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>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>1</th>\n",
       "      <td>2005-01-18</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>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>2</th>\n",
       "      <td>2005-01-23</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>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>2005-02-20</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>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>2005-03-14</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>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",
       "</div>"
      ],
      "text/plain": [
       "    stop_date stop_time driver_gender  driver_age_raw  driver_age driver_race  \\\n",
       "0  2005-01-02      1:55             M          1985.0        20.0       White   \n",
       "1  2005-01-18      8:15             M          1965.0        40.0       White   \n",
       "2  2005-01-23     23:15             M          1972.0        33.0       White   \n",
       "3  2005-02-20     17:15             M          1986.0        19.0       White   \n",
       "4  2005-03-14     10:00             F          1984.0        21.0       White   \n",
       "\n",
       "      violation_raw violation  search_conducted search_type   stop_outcome  \\\n",
       "0          Speeding  Speeding             False         NaN       Citation   \n",
       "1          Speeding  Speeding             False         NaN       Citation   \n",
       "2          Speeding  Speeding             False         NaN       Citation   \n",
       "3  Call for Service     Other             False         NaN  Arrest Driver   \n",
       "4          Speeding  Speeding             False         NaN       Citation   \n",
       "\n",
       "  is_arrested stop_duration  drugs_related_stop  \n",
       "0       False      0-15 Min               False  \n",
       "1       False      0-15 Min               False  \n",
       "2       False      0-15 Min               False  \n",
       "3        True     16-30 Min               False  \n",
       "4       False      0-15 Min               False  "
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(91741, 14)"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape\n",
    "#it's gone"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['stop_date', 'stop_time', 'driver_gender', 'driver_age_raw',\n",
       "       'driver_age', 'driver_race', 'violation_raw', 'violation',\n",
       "       'search_conducted', 'search_type', 'stop_outcome', 'is_arrested',\n",
       "       'stop_duration', 'drugs_related_stop'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns\n",
    "#it's gone"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [],
   "source": [
    "#method 2\n",
    "# df.drop(axis='columns',how='all')"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 2. Do men or women speed more often"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "M    32979\n",
       "F    15482\n",
       "Name: driver_gender, dtype: int64"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# method 1    value_counts()\n",
    "df[df.violation=='Speeding'].driver_gender.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "M    0.680527\n",
       "F    0.319473\n",
       "Name: driver_gender, dtype: float64"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df.violation=='Speeding'].driver_gender.value_counts(normalize=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "driver_gender  violation          \n",
       "F              Speeding               0.658500\n",
       "               Moving violation       0.136277\n",
       "               Equipment              0.105780\n",
       "               Registration/plates    0.043086\n",
       "               Other                  0.029348\n",
       "               Seat belt              0.027009\n",
       "M              Speeding               0.524350\n",
       "               Moving violation       0.207012\n",
       "               Equipment              0.135671\n",
       "               Other                  0.057668\n",
       "               Registration/plates    0.038461\n",
       "               Seat belt              0.036839\n",
       "Name: violation, dtype: float64"
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby('driver_gender').violation.value_counts(normalize=True)\n",
    "#这是一个multiindex"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "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>violation</th>\n",
       "      <th>Equipment</th>\n",
       "      <th>Moving violation</th>\n",
       "      <th>Other</th>\n",
       "      <th>Registration/plates</th>\n",
       "      <th>Seat belt</th>\n",
       "      <th>Speeding</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>driver_gender</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>F</th>\n",
       "      <td>0.105780</td>\n",
       "      <td>0.136277</td>\n",
       "      <td>0.029348</td>\n",
       "      <td>0.043086</td>\n",
       "      <td>0.027009</td>\n",
       "      <td>0.65850</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>M</th>\n",
       "      <td>0.135671</td>\n",
       "      <td>0.207012</td>\n",
       "      <td>0.057668</td>\n",
       "      <td>0.038461</td>\n",
       "      <td>0.036839</td>\n",
       "      <td>0.52435</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "violation      Equipment  Moving violation     Other  Registration/plates  \\\n",
       "driver_gender                                                               \n",
       "F               0.105780          0.136277  0.029348             0.043086   \n",
       "M               0.135671          0.207012  0.057668             0.038461   \n",
       "\n",
       "violation      Seat belt  Speeding  \n",
       "driver_gender                       \n",
       "F               0.027009   0.65850  \n",
       "M               0.036839   0.52435  "
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "df.groupby('driver_gender').violation.value_counts(normalize=True).unstack()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 3.Does gender affect who gets searched during a stop?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "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>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",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2005-01-02</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>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>1</th>\n",
       "      <td>2005-01-18</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>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>2</th>\n",
       "      <td>2005-01-23</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>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>2005-02-20</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>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>2005-03-14</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>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",
       "</div>"
      ],
      "text/plain": [
       "    stop_date stop_time driver_gender  driver_age_raw  driver_age driver_race  \\\n",
       "0  2005-01-02      1:55             M          1985.0        20.0       White   \n",
       "1  2005-01-18      8:15             M          1965.0        40.0       White   \n",
       "2  2005-01-23     23:15             M          1972.0        33.0       White   \n",
       "3  2005-02-20     17:15             M          1986.0        19.0       White   \n",
       "4  2005-03-14     10:00             F          1984.0        21.0       White   \n",
       "\n",
       "      violation_raw violation  search_conducted search_type   stop_outcome  \\\n",
       "0          Speeding  Speeding             False         NaN       Citation   \n",
       "1          Speeding  Speeding             False         NaN       Citation   \n",
       "2          Speeding  Speeding             False         NaN       Citation   \n",
       "3  Call for Service     Other             False         NaN  Arrest Driver   \n",
       "4          Speeding  Speeding             False         NaN       Citation   \n",
       "\n",
       "  is_arrested stop_duration  drugs_related_stop  \n",
       "0       False      0-15 Min               False  \n",
       "1       False      0-15 Min               False  \n",
       "2       False      0-15 Min               False  \n",
       "3        True     16-30 Min               False  \n",
       "4       False      0-15 Min               False  "
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([False,  True])"
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.search_conducted.unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "driver_gender  search_conducted\n",
       "F              False               23040\n",
       "               True                  471\n",
       "M              False               60170\n",
       "               True                 2725\n",
       "Name: search_conducted, dtype: int64"
      ]
     },
     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#method 1\n",
    "\n",
    "df.groupby('driver_gender').search_conducted.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "driver_gender  search_conducted\n",
       "F              False               0.979967\n",
       "               True                0.020033\n",
       "M              False               0.956674\n",
       "               True                0.043326\n",
       "Name: search_conducted, dtype: float64"
      ]
     },
     "execution_count": 91,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby('driver_gender').search_conducted.value_counts(normalize=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "driver_gender\n",
       "F    0.020033\n",
       "M    0.043326\n",
       "Name: search_conducted, dtype: float64"
      ]
     },
     "execution_count": 92,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#method 2\n",
    "\n",
    "df.groupby('driver_gender').search_conducted.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [],
   "source": [
    "#lessons\n",
    "\n",
    "#causation is difficult to conclude, so focus on relationships\n",
    "\n",
    "#include all relevant factors when studing a relationship"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 4. Why is search_type missing so often?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\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>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",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2005-01-02</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>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>1</th>\n",
       "      <td>2005-01-18</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>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>2</th>\n",
       "      <td>2005-01-23</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>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>2005-02-20</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>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>2005-03-14</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>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",
       "</div>"
      ],
      "text/plain": [
       "    stop_date stop_time driver_gender  driver_age_raw  driver_age driver_race  \\\n",
       "0  2005-01-02      1:55             M          1985.0        20.0       White   \n",
       "1  2005-01-18      8:15             M          1965.0        40.0       White   \n",
       "2  2005-01-23     23:15             M          1972.0        33.0       White   \n",
       "3  2005-02-20     17:15             M          1986.0        19.0       White   \n",
       "4  2005-03-14     10:00             F          1984.0        21.0       White   \n",
       "\n",
       "      violation_raw violation  search_conducted search_type   stop_outcome  \\\n",
       "0          Speeding  Speeding             False         NaN       Citation   \n",
       "1          Speeding  Speeding             False         NaN       Citation   \n",
       "2          Speeding  Speeding             False         NaN       Citation   \n",
       "3  Call for Service     Other             False         NaN  Arrest Driver   \n",
       "4          Speeding  Speeding             False         NaN       Citation   \n",
       "\n",
       "  is_arrested stop_duration  drugs_related_stop  \n",
       "0       False      0-15 Min               False  \n",
       "1       False      0-15 Min               False  \n",
       "2       False      0-15 Min               False  \n",
       "3        True     16-30 Min               False  \n",
       "4       False      0-15 Min               False  "
      ]
     },
     "execution_count": 94,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "stop_date                 0\n",
       "stop_time                 0\n",
       "driver_gender          5335\n",
       "driver_age_raw         5327\n",
       "driver_age             5621\n",
       "driver_race            5333\n",
       "violation_raw          5333\n",
       "violation              5333\n",
       "search_conducted          0\n",
       "search_type           88545\n",
       "stop_outcome           5333\n",
       "is_arrested            5333\n",
       "stop_duration          5333\n",
       "drugs_related_stop        0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 95,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False    88545\n",
       "True      3196\n",
       "Name: search_conducted, dtype: int64"
      ]
     },
     "execution_count": 96,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.search_conducted.value_counts()\n",
    "\n",
    "#search type is missing anytime search conducted is not done"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Series([], Name: search_type, dtype: int64)"
      ]
     },
     "execution_count": 97,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df.search_conducted==False].search_type.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "NaN    88545\n",
       "Name: search_type, dtype: int64"
      ]
     },
     "execution_count": 98,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df.search_conducted==False].search_type.value_counts(dropna=False)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 5. During a search, how often is the driver frisked?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "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>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",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2005-01-02</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>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>1</th>\n",
       "      <td>2005-01-18</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>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>2</th>\n",
       "      <td>2005-01-23</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>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>2005-02-20</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>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>2005-03-14</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>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",
       "</div>"
      ],
      "text/plain": [
       "    stop_date stop_time driver_gender  driver_age_raw  driver_age driver_race  \\\n",
       "0  2005-01-02      1:55             M          1985.0        20.0       White   \n",
       "1  2005-01-18      8:15             M          1965.0        40.0       White   \n",
       "2  2005-01-23     23:15             M          1972.0        33.0       White   \n",
       "3  2005-02-20     17:15             M          1986.0        19.0       White   \n",
       "4  2005-03-14     10:00             F          1984.0        21.0       White   \n",
       "\n",
       "      violation_raw violation  search_conducted search_type   stop_outcome  \\\n",
       "0          Speeding  Speeding             False         NaN       Citation   \n",
       "1          Speeding  Speeding             False         NaN       Citation   \n",
       "2          Speeding  Speeding             False         NaN       Citation   \n",
       "3  Call for Service     Other             False         NaN  Arrest Driver   \n",
       "4          Speeding  Speeding             False         NaN       Citation   \n",
       "\n",
       "  is_arrested stop_duration  drugs_related_stop  \n",
       "0       False      0-15 Min               False  \n",
       "1       False      0-15 Min               False  \n",
       "2       False      0-15 Min               False  \n",
       "3        True     16-30 Min               False  \n",
       "4       False      0-15 Min               False  "
      ]
     },
     "execution_count": 99,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "NaN                                                         88545\n",
       "Incident to Arrest                                           1219\n",
       "Probable Cause                                                891\n",
       "Inventory                                                     220\n",
       "Reasonable Suspicion                                          197\n",
       "Protective Frisk                                              161\n",
       "Incident to Arrest,Inventory                                  129\n",
       "Incident to Arrest,Probable Cause                             106\n",
       "Probable Cause,Reasonable Suspicion                            75\n",
       "Incident to Arrest,Inventory,Probable Cause                    34\n",
       "Probable Cause,Protective Frisk                                33\n",
       "Incident to Arrest,Protective Frisk                            33\n",
       "Inventory,Probable Cause                                       22\n",
       "Incident to Arrest,Reasonable Suspicion                        13\n",
       "Incident to Arrest,Inventory,Protective Frisk                  11\n",
       "Protective Frisk,Reasonable Suspicion                          11\n",
       "Inventory,Protective Frisk                                     11\n",
       "Incident to Arrest,Probable Cause,Protective Frisk             10\n",
       "Incident to Arrest,Probable Cause,Reasonable Suspicion          6\n",
       "Incident to Arrest,Inventory,Reasonable Suspicion               4\n",
       "Inventory,Reasonable Suspicion                                  4\n",
       "Inventory,Probable Cause,Protective Frisk                       2\n",
       "Inventory,Probable Cause,Reasonable Suspicion                   2\n",
       "Incident to Arrest,Protective Frisk,Reasonable Suspicion        1\n",
       "Probable Cause,Protective Frisk,Reasonable Suspicion            1\n",
       "Name: search_type, dtype: int64"
      ]
     },
     "execution_count": 100,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.search_type.value_counts(dropna=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0        NaN\n",
       "1        NaN\n",
       "2        NaN\n",
       "3        NaN\n",
       "4        NaN\n",
       "        ... \n",
       "91736    NaN\n",
       "91737    NaN\n",
       "91738    NaN\n",
       "91739    NaN\n",
       "91740    NaN\n",
       "Name: search_type, Length: 91741, dtype: object"
      ]
     },
     "execution_count": 102,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.search_type.str.contains('Protective Frisk')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['frisk']=df.search_type.str.contains('Protective Frisk')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "NaN      88545\n",
       "False     2922\n",
       "True       274\n",
       "Name: frisk, dtype: int64"
      ]
     },
     "execution_count": 104,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.frisk.value_counts(dropna=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "274"
      ]
     },
     "execution_count": 105,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.frisk.sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.08573216520650813"
      ]
     },
     "execution_count": 106,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.frisk.mean()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 6.Which year had the least number of stops?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    .dataframe tbody tr th:only-of-type {\n",
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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>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",
       "      <th>frisk</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2005-01-02</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>NaN</td>\n",
       "      <td>Citation</td>\n",
       "      <td>False</td>\n",
       "      <td>0-15 Min</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2005-01-18</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>NaN</td>\n",
       "      <td>Citation</td>\n",
       "      <td>False</td>\n",
       "      <td>0-15 Min</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2005-01-23</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>NaN</td>\n",
       "      <td>Citation</td>\n",
       "      <td>False</td>\n",
       "      <td>0-15 Min</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2005-02-20</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>NaN</td>\n",
       "      <td>Arrest Driver</td>\n",
       "      <td>True</td>\n",
       "      <td>16-30 Min</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2005-03-14</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>NaN</td>\n",
       "      <td>Citation</td>\n",
       "      <td>False</td>\n",
       "      <td>0-15 Min</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</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  2005-01-02      1:55             M          1985.0        20.0       White   \n",
       "1  2005-01-18      8:15             M          1965.0        40.0       White   \n",
       "2  2005-01-23     23:15             M          1972.0        33.0       White   \n",
       "3  2005-02-20     17:15             M          1986.0        19.0       White   \n",
       "4  2005-03-14     10:00             F          1984.0        21.0       White   \n",
       "\n",
       "      violation_raw violation  search_conducted search_type   stop_outcome  \\\n",
       "0          Speeding  Speeding             False         NaN       Citation   \n",
       "1          Speeding  Speeding             False         NaN       Citation   \n",
       "2          Speeding  Speeding             False         NaN       Citation   \n",
       "3  Call for Service     Other             False         NaN  Arrest Driver   \n",
       "4          Speeding  Speeding             False         NaN       Citation   \n",
       "\n",
       "  is_arrested stop_duration  drugs_related_stop frisk  \n",
       "0       False      0-15 Min               False   NaN  \n",
       "1       False      0-15 Min               False   NaN  \n",
       "2       False      0-15 Min               False   NaN  \n",
       "3        True     16-30 Min               False   NaN  \n",
       "4       False      0-15 Min               False   NaN  "
      ]
     },
     "execution_count": 107,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2012    10970\n",
       "2006    10639\n",
       "2007     9476\n",
       "2014     9228\n",
       "2008     8752\n",
       "2015     8599\n",
       "2011     8126\n",
       "2013     7924\n",
       "2009     7908\n",
       "2010     7561\n",
       "2005     2558\n",
       "Name: stop_date, dtype: int64"
      ]
     },
     "execution_count": 108,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#method 1\n",
    "df.stop_date.str.slice(0,4).value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {},
   "outputs": [],
   "source": [
    "#method 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "stop_date              object\n",
       "stop_time              object\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",
       "frisk                  object\n",
       "dtype: object"
      ]
     },
     "execution_count": 110,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {},
   "outputs": [],
   "source": [
    "combined=df.stop_date.str.cat(df.stop_time,sep=' ')\n",
    "\n",
    "#combine two columns that are strings\n",
    "\n",
    "#concatnate the stop date and stop time to series and sepreate by space"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0         2005-01-02 1:55\n",
       "1         2005-01-18 8:15\n",
       "2        2005-01-23 23:15\n",
       "3        2005-02-20 17:15\n",
       "4        2005-03-14 10:00\n",
       "               ...       \n",
       "91736    2015-12-31 20:27\n",
       "91737    2015-12-31 20:35\n",
       "91738    2015-12-31 20:45\n",
       "91739    2015-12-31 21:42\n",
       "91740    2015-12-31 22:46\n",
       "Name: stop_date, Length: 91741, dtype: object"
      ]
     },
     "execution_count": 112,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "combined"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['stop_datetime']=pd.to_datetime(combined)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "stop_date                     object\n",
       "stop_time                     object\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",
       "frisk                         object\n",
       "stop_datetime         datetime64[ns]\n",
       "dtype: object"
      ]
     },
     "execution_count": 114,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0        2005\n",
       "1        2005\n",
       "2        2005\n",
       "3        2005\n",
       "4        2005\n",
       "         ... \n",
       "91736    2015\n",
       "91737    2015\n",
       "91738    2015\n",
       "91739    2015\n",
       "91740    2015\n",
       "Name: stop_datetime, Length: 91741, dtype: int64"
      ]
     },
     "execution_count": 116,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.stop_datetime.dt.year"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2012    10970\n",
       "2006    10639\n",
       "2007     9476\n",
       "2014     9228\n",
       "2008     8752\n",
       "2015     8599\n",
       "2011     8126\n",
       "2013     7924\n",
       "2009     7908\n",
       "2010     7561\n",
       "2005     2558\n",
       "Name: stop_datetime, dtype: int64"
      ]
     },
     "execution_count": 117,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.stop_datetime.dt.year.value_counts()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 7. How does drug activity change by time of day?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "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>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",
       "      <th>frisk</th>\n",
       "      <th>stop_datetime</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2005-01-02</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>NaN</td>\n",
       "      <td>Citation</td>\n",
       "      <td>False</td>\n",
       "      <td>0-15 Min</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2005-01-02 01:55:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2005-01-18</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>NaN</td>\n",
       "      <td>Citation</td>\n",
       "      <td>False</td>\n",
       "      <td>0-15 Min</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2005-01-18 08:15:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2005-01-23</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>NaN</td>\n",
       "      <td>Citation</td>\n",
       "      <td>False</td>\n",
       "      <td>0-15 Min</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2005-01-23 23:15:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2005-02-20</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>NaN</td>\n",
       "      <td>Arrest Driver</td>\n",
       "      <td>True</td>\n",
       "      <td>16-30 Min</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2005-02-20 17:15:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2005-03-14</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>NaN</td>\n",
       "      <td>Citation</td>\n",
       "      <td>False</td>\n",
       "      <td>0-15 Min</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2005-03-14 10:00:00</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  2005-01-02      1:55             M          1985.0        20.0       White   \n",
       "1  2005-01-18      8:15             M          1965.0        40.0       White   \n",
       "2  2005-01-23     23:15             M          1972.0        33.0       White   \n",
       "3  2005-02-20     17:15             M          1986.0        19.0       White   \n",
       "4  2005-03-14     10:00             F          1984.0        21.0       White   \n",
       "\n",
       "      violation_raw violation  search_conducted search_type   stop_outcome  \\\n",
       "0          Speeding  Speeding             False         NaN       Citation   \n",
       "1          Speeding  Speeding             False         NaN       Citation   \n",
       "2          Speeding  Speeding             False         NaN       Citation   \n",
       "3  Call for Service     Other             False         NaN  Arrest Driver   \n",
       "4          Speeding  Speeding             False         NaN       Citation   \n",
       "\n",
       "  is_arrested stop_duration  drugs_related_stop frisk       stop_datetime  \n",
       "0       False      0-15 Min               False   NaN 2005-01-02 01:55:00  \n",
       "1       False      0-15 Min               False   NaN 2005-01-18 08:15:00  \n",
       "2       False      0-15 Min               False   NaN 2005-01-23 23:15:00  \n",
       "3        True     16-30 Min               False   NaN 2005-02-20 17:15:00  \n",
       "4       False      0-15 Min               False   NaN 2005-03-14 10:00:00  "
      ]
     },
     "execution_count": 118,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.008883705213590434"
      ]
     },
     "execution_count": 119,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.drugs_related_stop.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "stop_datetime\n",
       "0     0.019728\n",
       "1     0.013507\n",
       "2     0.015462\n",
       "3     0.017065\n",
       "4     0.011811\n",
       "5     0.004762\n",
       "6     0.003040\n",
       "7     0.003281\n",
       "8     0.002687\n",
       "9     0.006288\n",
       "10    0.005714\n",
       "11    0.006976\n",
       "12    0.004467\n",
       "13    0.010326\n",
       "14    0.007810\n",
       "15    0.006416\n",
       "16    0.005723\n",
       "17    0.005517\n",
       "18    0.010148\n",
       "19    0.011596\n",
       "20    0.008084\n",
       "21    0.013342\n",
       "22    0.013533\n",
       "23    0.016344\n",
       "Name: drugs_related_stop, dtype: float64"
      ]
     },
     "execution_count": 121,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby(df.stop_datetime.dt.hour).drugs_related_stop.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='stop_datetime'>"
      ]
     },
     "execution_count": 122,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.groupby(df.stop_datetime.dt.hour).drugs_related_stop.mean().plot()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 8. Do most stops occur at night?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "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>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",
       "      <th>frisk</th>\n",
       "      <th>stop_datetime</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2005-01-02</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>NaN</td>\n",
       "      <td>Citation</td>\n",
       "      <td>False</td>\n",
       "      <td>0-15 Min</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2005-01-02 01:55:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2005-01-18</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>NaN</td>\n",
       "      <td>Citation</td>\n",
       "      <td>False</td>\n",
       "      <td>0-15 Min</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2005-01-18 08:15:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2005-01-23</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>NaN</td>\n",
       "      <td>Citation</td>\n",
       "      <td>False</td>\n",
       "      <td>0-15 Min</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2005-01-23 23:15:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2005-02-20</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>NaN</td>\n",
       "      <td>Arrest Driver</td>\n",
       "      <td>True</td>\n",
       "      <td>16-30 Min</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2005-02-20 17:15:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2005-03-14</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>NaN</td>\n",
       "      <td>Citation</td>\n",
       "      <td>False</td>\n",
       "      <td>0-15 Min</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2005-03-14 10:00:00</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  2005-01-02      1:55             M          1985.0        20.0       White   \n",
       "1  2005-01-18      8:15             M          1965.0        40.0       White   \n",
       "2  2005-01-23     23:15             M          1972.0        33.0       White   \n",
       "3  2005-02-20     17:15             M          1986.0        19.0       White   \n",
       "4  2005-03-14     10:00             F          1984.0        21.0       White   \n",
       "\n",
       "      violation_raw violation  search_conducted search_type   stop_outcome  \\\n",
       "0          Speeding  Speeding             False         NaN       Citation   \n",
       "1          Speeding  Speeding             False         NaN       Citation   \n",
       "2          Speeding  Speeding             False         NaN       Citation   \n",
       "3  Call for Service     Other             False         NaN  Arrest Driver   \n",
       "4          Speeding  Speeding             False         NaN       Citation   \n",
       "\n",
       "  is_arrested stop_duration  drugs_related_stop frisk       stop_datetime  \n",
       "0       False      0-15 Min               False   NaN 2005-01-02 01:55:00  \n",
       "1       False      0-15 Min               False   NaN 2005-01-18 08:15:00  \n",
       "2       False      0-15 Min               False   NaN 2005-01-23 23:15:00  \n",
       "3        True     16-30 Min               False   NaN 2005-02-20 17:15:00  \n",
       "4       False      0-15 Min               False   NaN 2005-03-14 10:00:00  "
      ]
     },
     "execution_count": 123,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "stop_datetime\n",
       "10    7350\n",
       "9     6838\n",
       "11    5877\n",
       "14    5634\n",
       "23    5629\n",
       "0     5221\n",
       "8     5210\n",
       "13    4842\n",
       "15    4832\n",
       "7     4572\n",
       "1     4442\n",
       "16    3844\n",
       "12    3582\n",
       "22    3473\n",
       "20    3340\n",
       "6     3290\n",
       "2     2975\n",
       "17    2900\n",
       "19    2587\n",
       "18    2168\n",
       "21    1499\n",
       "3     1172\n",
       "4      254\n",
       "5      210\n",
       "Name: stop_time, dtype: int64"
      ]
     },
     "execution_count": 131,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#method 1\n",
    "df.groupby(df.stop_datetime.dt.hour).stop_time.count().sort_values(ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='stop_datetime'>"
      ]
     },
     "execution_count": 128,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.groupby(df.stop_datetime.dt.hour).stop_time.count().plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "10    7350\n",
       "9     6838\n",
       "11    5877\n",
       "14    5634\n",
       "23    5629\n",
       "0     5221\n",
       "8     5210\n",
       "13    4842\n",
       "15    4832\n",
       "7     4572\n",
       "1     4442\n",
       "16    3844\n",
       "12    3582\n",
       "22    3473\n",
       "20    3340\n",
       "6     3290\n",
       "2     2975\n",
       "17    2900\n",
       "19    2587\n",
       "18    2168\n",
       "21    1499\n",
       "3     1172\n",
       "4      254\n",
       "5      210\n",
       "Name: stop_datetime, dtype: int64"
      ]
     },
     "execution_count": 129,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#method 2\n",
    "\n",
    "df.stop_datetime.dt.hour.value_counts()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 134,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "df.stop_datetime.dt.hour.value_counts().sort_index().plot()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 9.  Find the bad data in the stop_duration column and fix it"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "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>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",
       "      <th>frisk</th>\n",
       "      <th>stop_datetime</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2005-01-02</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>NaN</td>\n",
       "      <td>Citation</td>\n",
       "      <td>False</td>\n",
       "      <td>0-15 Min</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2005-01-02 01:55:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2005-01-18</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>NaN</td>\n",
       "      <td>Citation</td>\n",
       "      <td>False</td>\n",
       "      <td>0-15 Min</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2005-01-18 08:15:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2005-01-23</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>NaN</td>\n",
       "      <td>Citation</td>\n",
       "      <td>False</td>\n",
       "      <td>0-15 Min</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2005-01-23 23:15:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2005-02-20</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>NaN</td>\n",
       "      <td>Arrest Driver</td>\n",
       "      <td>True</td>\n",
       "      <td>16-30 Min</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2005-02-20 17:15:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2005-03-14</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>NaN</td>\n",
       "      <td>Citation</td>\n",
       "      <td>False</td>\n",
       "      <td>0-15 Min</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2005-03-14 10:00:00</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  2005-01-02      1:55             M          1985.0        20.0       White   \n",
       "1  2005-01-18      8:15             M          1965.0        40.0       White   \n",
       "2  2005-01-23     23:15             M          1972.0        33.0       White   \n",
       "3  2005-02-20     17:15             M          1986.0        19.0       White   \n",
       "4  2005-03-14     10:00             F          1984.0        21.0       White   \n",
       "\n",
       "      violation_raw violation  search_conducted search_type   stop_outcome  \\\n",
       "0          Speeding  Speeding             False         NaN       Citation   \n",
       "1          Speeding  Speeding             False         NaN       Citation   \n",
       "2          Speeding  Speeding             False         NaN       Citation   \n",
       "3  Call for Service     Other             False         NaN  Arrest Driver   \n",
       "4          Speeding  Speeding             False         NaN       Citation   \n",
       "\n",
       "  is_arrested stop_duration  drugs_related_stop frisk       stop_datetime  \n",
       "0       False      0-15 Min               False   NaN 2005-01-02 01:55:00  \n",
       "1       False      0-15 Min               False   NaN 2005-01-18 08:15:00  \n",
       "2       False      0-15 Min               False   NaN 2005-01-23 23:15:00  \n",
       "3        True     16-30 Min               False   NaN 2005-02-20 17:15:00  \n",
       "4       False      0-15 Min               False   NaN 2005-03-14 10:00:00  "
      ]
     },
     "execution_count": 135,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0-15 Min     69543\n",
       "16-30 Min    13635\n",
       "NaN           5333\n",
       "30+ Min       3228\n",
       "2                1\n",
       "1                1\n",
       "Name: stop_duration, dtype: int64"
      ]
     },
     "execution_count": 138,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.stop_duration.value_counts(dropna=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Do I think the missing data is the bad data? no"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0-15 Min     69543\n",
       "16-30 Min    13635\n",
       "30+ Min       3228\n",
       "2                1\n",
       "1                1\n",
       "Name: stop_duration, dtype: int64"
      ]
     },
     "execution_count": 139,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.stop_duration.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# What does it means to fix it?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dtype('O')"
      ]
     },
     "execution_count": 140,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.stop_duration.dtype\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/kj/gjbr9xrs6ns8dc9jgs1f619h0000gn/T/ipykernel_75805/718023882.py:1: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  df[(df.stop_duration=='1')|(df.stop_duration=='2')].stop_duration='NaN'\n"
     ]
    }
   ],
   "source": [
    "df[(df.stop_duration=='1')|(df.stop_duration=='2')].stop_duration='NaN'\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 142,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.loc[(df.stop_duration=='1')|(df.stop_duration=='2'),'stop_duration']='NaN'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 143,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0-15 Min     69543\n",
       "16-30 Min    13635\n",
       "30+ Min       3228\n",
       "NaN              2\n",
       "Name: stop_duration, dtype: int64"
      ]
     },
     "execution_count": 143,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.stop_duration.value_counts()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 144,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0-15 Min     69543\n",
       "16-30 Min    13635\n",
       "NaN           5333\n",
       "30+ Min       3228\n",
       "NaN              2\n",
       "Name: stop_duration, dtype: int64"
      ]
     },
     "execution_count": 144,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.stop_duration.value_counts(dropna=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 146,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.loc[df.stop_duration=='NaN','stop_duration']=np.nan"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 147,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0-15 Min     69543\n",
       "16-30 Min    13635\n",
       "NaN           5335\n",
       "30+ Min       3228\n",
       "Name: stop_duration, dtype: int64"
      ]
     },
     "execution_count": 147,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.stop_duration.value_counts(dropna=False)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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