Stanford Open Policing Project.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "da3a1f9b",
   "metadata": {},
   "source": [
    "## Stanford Open Policing Project\n",
    "* traffic stops by police officers\n",
    "* collected data from 31 US states, but only 'Rhode Island' in this case\n",
    "* some of the columns and rows have been removed, full dataset can be downloaded at https://openpolicing.stanford.edu/"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "88a0ec5b",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "42b090ec",
   "metadata": {},
   "source": [
    "#### Read the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "0cbe440e",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "ri = pd.read_csv('ri_police.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "f1bf1b25",
   "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>state</th>\n",
       "      <th>stop_date</th>\n",
       "      <th>stop_time</th>\n",
       "      <th>county_name</th>\n",
       "      <th>driver_gender</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>district</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>RI</td>\n",
       "      <td>2005/1/4</td>\n",
       "      <td>12:55</td>\n",
       "      <td>NaN</td>\n",
       "      <td>M</td>\n",
       "      <td>White</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",
       "      <td>Zone X4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>RI</td>\n",
       "      <td>2005/1/23</td>\n",
       "      <td>23:15</td>\n",
       "      <td>NaN</td>\n",
       "      <td>M</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>Zone K3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>RI</td>\n",
       "      <td>2005/2/17</td>\n",
       "      <td>04:15</td>\n",
       "      <td>NaN</td>\n",
       "      <td>M</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>Zone X4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>RI</td>\n",
       "      <td>2005/2/20</td>\n",
       "      <td>17:15</td>\n",
       "      <td>NaN</td>\n",
       "      <td>M</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>Zone X1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>RI</td>\n",
       "      <td>2005/2/24</td>\n",
       "      <td>01:20</td>\n",
       "      <td>NaN</td>\n",
       "      <td>F</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>Zone X3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  state  stop_date stop_time  county_name driver_gender driver_race  \\\n",
       "0    RI   2005/1/4     12:55          NaN             M       White   \n",
       "1    RI  2005/1/23     23:15          NaN             M       White   \n",
       "2    RI  2005/2/17     04:15          NaN             M       White   \n",
       "3    RI  2005/2/20     17:15          NaN             M       White   \n",
       "4    RI  2005/2/24     01:20          NaN             F       White   \n",
       "\n",
       "                    violation_raw  violation  search_conducted search_type  \\\n",
       "0  Equipment/Inspection Violation  Equipment             False         NaN   \n",
       "1                        Speeding   Speeding             False         NaN   \n",
       "2                        Speeding   Speeding             False         NaN   \n",
       "3                Call for Service      Other             False         NaN   \n",
       "4                        Speeding   Speeding             False         NaN   \n",
       "\n",
       "    stop_outcome is_arrested stop_duration  drugs_related_stop district  \n",
       "0       Citation       False      0-15 Min               False  Zone X4  \n",
       "1       Citation       False      0-15 Min               False  Zone K3  \n",
       "2       Citation       False      0-15 Min               False  Zone X4  \n",
       "3  Arrest Driver        True     16-30 Min               False  Zone X1  \n",
       "4       Citation       False      0-15 Min               False  Zone X3  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Examine the head of the DataFrame\n",
    "display(ri.head())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a1818628",
   "metadata": {},
   "source": [
    "#### Handle missing value and drop\n",
    "* county_name only contains missing valuee, state info is all the same, so drop them using .drop()\n",
    "* dropna() is for dropping rows"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "e7971b95",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "state                     0\n",
      "stop_date                 0\n",
      "stop_time                 0\n",
      "county_name           91741\n",
      "driver_gender          5205\n",
      "driver_race            5202\n",
      "violation_raw          5202\n",
      "violation              5202\n",
      "search_conducted          0\n",
      "search_type           88434\n",
      "stop_outcome           5202\n",
      "is_arrested            5202\n",
      "stop_duration          5202\n",
      "drugs_related_stop        0\n",
      "district                  0\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "# Count the number of missing values in each column\n",
    "print(ri.isnull().sum())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "1717a32f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(91741, 15)\n"
     ]
    }
   ],
   "source": [
    "# Examine the shape of the DataFrame\n",
    "print(ri.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "6a66541b",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(91741, 13)\n"
     ]
    }
   ],
   "source": [
    "# Drop the 'county_name' and 'state' columns\n",
    "ri.drop(['county_name', 'state'], axis='columns', inplace=True)\n",
    "\n",
    "# Examine the shape of the DataFrame (again)\n",
    "print(ri.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "b9155844",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "stop_date                 0\n",
      "stop_time                 0\n",
      "driver_gender          5205\n",
      "driver_race            5202\n",
      "violation_raw          5202\n",
      "violation              5202\n",
      "search_conducted          0\n",
      "search_type           88434\n",
      "stop_outcome           5202\n",
      "is_arrested            5202\n",
      "stop_duration          5202\n",
      "drugs_related_stop        0\n",
      "district                  0\n",
      "dtype: int64\n",
      "stop_date                 0\n",
      "stop_time                 0\n",
      "driver_gender             0\n",
      "driver_race               0\n",
      "violation_raw             0\n",
      "violation                 0\n",
      "search_conducted          0\n",
      "search_type           83229\n",
      "stop_outcome              0\n",
      "is_arrested               0\n",
      "stop_duration             0\n",
      "drugs_related_stop        0\n",
      "district                  0\n",
      "dtype: int64\n",
      "(86536, 13)\n"
     ]
    }
   ],
   "source": [
    "# drop rows\n",
    "# Count the number of missing values in each column\n",
    "print(ri.isnull().sum())\n",
    "\n",
    "# Drop all rows that are missing 'driver_gender'\n",
    "ri.dropna(subset=['driver_gender'], inplace=True)\n",
    "\n",
    "# Count the number of missing values in each column (again)\n",
    "print(ri.isnull().sum())\n",
    "\n",
    "# Examine the shape of the DataFrame\n",
    "print(ri.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a6f2bc64",
   "metadata": {},
   "source": [
    "#### Explore the data type\n",
    "* Object: Series/ list/ string\n",
    "* Bool (Boolean): True and False values\n",
    "* Int:integers\n",
    "* Float: floating point values\n",
    "* Datetime for dates and times\n",
    "* Category: categorical variables\n",
    "\n",
    "using .dtypes to check data type and .astype() to change data type"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "8fc3e4b7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0    False\n",
      "1    False\n",
      "2    False\n",
      "3     True\n",
      "4    False\n",
      "Name: is_arrested, dtype: object\n",
      "bool\n"
     ]
    }
   ],
   "source": [
    "# Examine the head of the 'is_arrested' column\n",
    "print(ri.is_arrested.head())\n",
    "\n",
    "# Change the data type of 'is_arrested' to 'bool'\n",
    "ri['is_arrested'] = ri.is_arrested.astype('bool')\n",
    "\n",
    "# Check the data type of 'is_arrested' \n",
    "print(ri.is_arrested.dtypes)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a022200d",
   "metadata": {},
   "source": [
    "#### Build a DatetimeIndex\n",
    "* .replace(before replacement, after replacement)\n",
    "* df.column1.cal(df.column2, sep=' ')\n",
    "* pd.to_datetime(series name)\n",
    "* df.set_index(column name) for filtering and plotting, then this index is not longer be seen as the columns of the dataframe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "fde995c5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "stop_date                     object\n",
      "stop_time                     object\n",
      "driver_gender                 object\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                     bool\n",
      "stop_duration                 object\n",
      "drugs_related_stop              bool\n",
      "district                      object\n",
      "stop_datetime         datetime64[ns]\n",
      "dtype: object\n"
     ]
    }
   ],
   "source": [
    "# Concatenate 'stop_date' and 'stop_time' (separated by a space)\n",
    "combined = ri.stop_date.str.cat(ri.stop_time, sep=' ')\n",
    "\n",
    "# Convert 'combined' to datetime format\n",
    "ri['stop_datetime'] = pd.to_datetime(combined)\n",
    "\n",
    "# Examine the data types of the DataFrame\n",
    "print(ri.dtypes)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "59f61297",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "DatetimeIndex(['2005-01-04 12:55:00', '2005-01-23 23:15:00',\n",
      "               '2005-02-17 04:15:00', '2005-02-20 17:15:00',\n",
      "               '2005-02-24 01:20:00', '2005-03-14 10:00:00',\n",
      "               '2005-03-29 21:55:00', '2005-04-04 21:25:00',\n",
      "               '2005-07-14 11:20:00', '2005-07-14 19:55:00',\n",
      "               ...\n",
      "               '2015-12-31 13:23:00', '2015-12-31 18:59:00',\n",
      "               '2015-12-31 19:13:00', '2015-12-31 20:20:00',\n",
      "               '2015-12-31 20:50:00', '2015-12-31 21:21:00',\n",
      "               '2015-12-31 21:59:00', '2015-12-31 22:04:00',\n",
      "               '2015-12-31 22:09:00', '2015-12-31 22:47:00'],\n",
      "              dtype='datetime64[ns]', name='stop_datetime', length=86536, freq=None)\n",
      "Index(['stop_date', 'stop_time', 'driver_gender', 'driver_race',\n",
      "       'violation_raw', 'violation', 'search_conducted', 'search_type',\n",
      "       'stop_outcome', 'is_arrested', 'stop_duration', 'drugs_related_stop',\n",
      "       'district'],\n",
      "      dtype='object')\n"
     ]
    }
   ],
   "source": [
    "# Set 'stop_datetime' as the index\n",
    "ri.set_index('stop_datetime', inplace=True)\n",
    "\n",
    "# Examine the index\n",
    "print(ri.index)\n",
    "\n",
    "# Examine the columns\n",
    "print(ri.columns)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "73773f29",
   "metadata": {},
   "source": [
    "#### Counting Unique Values\n",
    "* .value_counts(): suitable for categorical variables with descending order\n",
    "* .sum() = number of rows with not null values\n",
    "* normalize = True: show proportion\n",
    "* can filter the dataset and then do the unique values counting"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "5c2e4d1f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Speeding               48423\n",
      "Moving violation       16224\n",
      "Equipment              10921\n",
      "Other                   4409\n",
      "Registration/plates     3703\n",
      "Seat belt               2856\n",
      "Name: violation, dtype: int64\n",
      "Speeding               0.559571\n",
      "Moving violation       0.187483\n",
      "Equipment              0.126202\n",
      "Other                  0.050950\n",
      "Registration/plates    0.042791\n",
      "Seat belt              0.033004\n",
      "Name: violation, dtype: float64\n"
     ]
    }
   ],
   "source": [
    "# Count the unique values in 'violation'\n",
    "print(ri['violation'].value_counts())\n",
    "\n",
    "# Express the counts as proportions\n",
    "print(ri['violation'].value_counts(normalize=True))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "19358ae1",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Speeding               0.658114\n",
      "Moving violation       0.138218\n",
      "Equipment              0.105199\n",
      "Registration/plates    0.044418\n",
      "Other                  0.029738\n",
      "Seat belt              0.024312\n",
      "Name: violation, dtype: float64\n",
      "Speeding               0.522243\n",
      "Moving violation       0.206144\n",
      "Equipment              0.134158\n",
      "Other                  0.058985\n",
      "Registration/plates    0.042175\n",
      "Seat belt              0.036296\n",
      "Name: violation, dtype: float64\n"
     ]
    }
   ],
   "source": [
    "# Create a DataFrame of female drivers\n",
    "female = ri[ri['driver_gender']=='F']\n",
    "\n",
    "# Create a DataFrame of male drivers\n",
    "male = ri[ri['driver_gender']=='M']\n",
    "\n",
    "# Compute the violations by female drivers (as proportions)\n",
    "print(female.violation.value_counts(normalize=True))\n",
    "\n",
    "# Compute the violations by male drivers (as proportions)\n",
    "print(male.violation.value_counts(normalize=True))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "284b9b17",
   "metadata": {},
   "source": [
    "#### Does gender affect who gets a ticket for speeding?\n",
    "* multiple condtions with () in each condition\n",
    "* &, |\n",
    "* ==, !=\n",
    "* correlation does not imply causation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "630ddee2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Citation            0.952192\n",
      "Warning             0.040074\n",
      "Arrest Driver       0.005752\n",
      "N/D                 0.000959\n",
      "Arrest Passenger    0.000639\n",
      "No Action           0.000383\n",
      "Name: stop_outcome, dtype: float64\n",
      "Citation            0.944595\n",
      "Warning             0.036184\n",
      "Arrest Driver       0.015895\n",
      "Arrest Passenger    0.001281\n",
      "No Action           0.001068\n",
      "N/D                 0.000976\n",
      "Name: stop_outcome, dtype: float64\n"
     ]
    }
   ],
   "source": [
    "# Create a DataFrame of female drivers stopped for speeding\n",
    "female_and_speeding = ri[(ri.driver_gender=='F') & (ri.violation=='Speeding')]\n",
    "\n",
    "# Create a DataFrame of male drivers stopped for speeding\n",
    "male_and_speeding = ri[(ri.driver_gender=='M') & (ri.violation=='Speeding')]\n",
    "\n",
    "# Compute the stop outcomes for female drivers (as proportions)\n",
    "print(female_and_speeding.stop_outcome.value_counts(normalize = True))\n",
    "\n",
    "# Compute the stop outcomes for male drivers (as proportions)\n",
    "print(male_and_speeding.stop_outcome.value_counts(normalize = True))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "21d0d7a5",
   "metadata": {},
   "source": [
    "#### Does gender affect whose vehicle is searched?\n",
    "* True: 1/ False: 0\n",
    "* use numpy to take mean: np.mean()\n",
    "* .unique() to check the unique values in specific columns\n",
    "* combine with groupby() to get the each mean(), can groupby multiple categories using ['col1', 'col2']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "5d280292",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "bool\n",
      "False    0.961785\n",
      "True     0.038215\n",
      "Name: search_conducted, dtype: float64\n",
      "0.0382153092354627\n"
     ]
    }
   ],
   "source": [
    "# Check the data type of 'search_conducted'\n",
    "print(ri.search_conducted.dtypes)\n",
    "\n",
    "# Calculate the search rate by counting the values\n",
    "print(ri.search_conducted.value_counts(normalize=True))\n",
    "\n",
    "# Calculate the search rate by taking the mean\n",
    "print(ri.search_conducted.mean())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "58a8b8a5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.019180617481282074\n",
      "0.04542557598546892\n"
     ]
    }
   ],
   "source": [
    "# Calculate the search rate for female drivers\n",
    "print(ri[ri.driver_gender=='F'].search_conducted.mean())\n",
    "\n",
    "# Calculate the search rate for male drivers\n",
    "print(ri[ri.driver_gender=='M'].search_conducted.mean())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "d2d2b201",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "driver_gender\n",
      "F    0.019181\n",
      "M    0.045426\n",
      "Name: search_conducted, dtype: float64\n"
     ]
    }
   ],
   "source": [
    "# Calculate the search rate for both groups simultaneously\n",
    "print(ri.groupby('driver_gender').search_conducted.mean())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "712c8e53",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "driver_gender  violation          \n",
      "F              Equipment              0.039984\n",
      "               Moving violation       0.039257\n",
      "               Other                  0.041018\n",
      "               Registration/plates    0.054924\n",
      "               Seat belt              0.017301\n",
      "               Speeding               0.008309\n",
      "M              Equipment              0.071496\n",
      "               Moving violation       0.061524\n",
      "               Other                  0.046191\n",
      "               Registration/plates    0.108802\n",
      "               Seat belt              0.035119\n",
      "               Speeding               0.027885\n",
      "Name: search_conducted, dtype: float64\n"
     ]
    }
   ],
   "source": [
    "# Calculate the search rate for each combination of gender and violation\n",
    "print(ri.groupby(['driver_gender', 'violation']).search_conducted.mean())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "d684fe88",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "violation            driver_gender\n",
      "Equipment            F                0.039984\n",
      "                     M                0.071496\n",
      "Moving violation     F                0.039257\n",
      "                     M                0.061524\n",
      "Other                F                0.041018\n",
      "                     M                0.046191\n",
      "Registration/plates  F                0.054924\n",
      "                     M                0.108802\n",
      "Seat belt            F                0.017301\n",
      "                     M                0.035119\n",
      "Speeding             F                0.008309\n",
      "                     M                0.027885\n",
      "Name: search_conducted, dtype: float64\n"
     ]
    }
   ],
   "source": [
    "# Reverse the ordering to group by violation before gender\n",
    "print(ri.groupby(['violation', 'driver_gender']).search_conducted.mean())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f7d1850b",
   "metadata": {},
   "source": [
    "#### Does gender affect who is frisked during a search?\n",
    "* use contains() to check whether a string is present in each element of a given column\n",
    "* True: found/ False: not found or na\n",
    "* we can add one new column by detecting some strings\n",
    "* the data type is bool --> can use .sum() and .mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "7067e744",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Incident to Arrest                                          1290\n",
      "Probable Cause                                               924\n",
      "Inventory                                                    219\n",
      "Reasonable Suspicion                                         214\n",
      "Protective Frisk                                             164\n",
      "Incident to Arrest,Inventory                                 123\n",
      "Incident to Arrest,Probable Cause                            100\n",
      "Probable Cause,Reasonable Suspicion                           54\n",
      "Incident to Arrest,Inventory,Probable Cause                   35\n",
      "Probable Cause,Protective Frisk                               35\n",
      "Incident to Arrest,Protective Frisk                           33\n",
      "Inventory,Probable Cause                                      25\n",
      "Protective Frisk,Reasonable Suspicion                         19\n",
      "Incident to Arrest,Inventory,Protective Frisk                 18\n",
      "Incident to Arrest,Probable Cause,Protective Frisk            13\n",
      "Inventory,Protective Frisk                                    12\n",
      "Incident to Arrest,Reasonable Suspicion                        8\n",
      "Incident to Arrest,Probable Cause,Reasonable Suspicion         5\n",
      "Probable Cause,Protective Frisk,Reasonable Suspicion           5\n",
      "Incident to Arrest,Inventory,Reasonable Suspicion              4\n",
      "Incident to Arrest,Protective Frisk,Reasonable Suspicion       2\n",
      "Inventory,Reasonable Suspicion                                 2\n",
      "Inventory,Probable Cause,Reasonable Suspicion                  1\n",
      "Inventory,Protective Frisk,Reasonable Suspicion                1\n",
      "Inventory,Probable Cause,Protective Frisk                      1\n",
      "Name: search_type, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "# Count the 'search_type' values\n",
    "print(ri.search_type.value_counts())\n",
    "\n",
    "# Check if 'search_type' contains the string 'Protective Frisk'\n",
    "ri['frisk'] = ri.search_type.str.contains('Protective Frisk', na=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "2b1c8ed4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "bool\n",
      "303\n"
     ]
    }
   ],
   "source": [
    "# Check the data type of 'frisk'\n",
    "print(ri['frisk'].dtypes)\n",
    "\n",
    "# Take the sum of 'frisk'\n",
    "print(ri['frisk'].sum())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "51c67a3b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.09162382824312065\n",
      "driver_gender\n",
      "F    0.074561\n",
      "M    0.094353\n",
      "Name: frisk, dtype: float64\n"
     ]
    }
   ],
   "source": [
    "# Create a DataFrame of stops in which a search was conducted\n",
    "searched = ri[ri.search_conducted == True]\n",
    "\n",
    "# Calculate the overall frisk rate by taking the mean of 'frisk'\n",
    "print(searched.frisk.mean())\n",
    "\n",
    "# Calculate the frisk rate for each gender\n",
    "print(searched.groupby('driver_gender').frisk.mean())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a8301b88",
   "metadata": {},
   "source": [
    "#### Does time of day affect arrest rate\n",
    "* datetime object: dt.year/ dt.month/ dt.date\n",
    "* set_index( , inplace = True)\n",
    "* after setting the index = datetime, we can groupby .index.month and calculate the mean\n",
    "* plt.xlabel(''), plt.ylabel(''), plt.title('')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "c03f427b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.0355690117407784\n",
      "stop_datetime\n",
      "0     0.051431\n",
      "1     0.064932\n",
      "2     0.060798\n",
      "3     0.060549\n",
      "4     0.048000\n",
      "5     0.042781\n",
      "6     0.013813\n",
      "7     0.013032\n",
      "8     0.021854\n",
      "9     0.025206\n",
      "10    0.028213\n",
      "11    0.028897\n",
      "12    0.037399\n",
      "13    0.030776\n",
      "14    0.030605\n",
      "15    0.030679\n",
      "16    0.035281\n",
      "17    0.040619\n",
      "18    0.038204\n",
      "19    0.032245\n",
      "20    0.038107\n",
      "21    0.064541\n",
      "22    0.048666\n",
      "23    0.047592\n",
      "Name: is_arrested, dtype: float64\n"
     ]
    }
   ],
   "source": [
    "# Calculate the overall arrest rate\n",
    "print(ri.is_arrested.mean())\n",
    "\n",
    "# Calculate the hourly arrest rate\n",
    "print(ri.groupby(ri.index.hour).is_arrested.mean())\n",
    "\n",
    "# Save the hourly arrest rate\n",
    "hourly_arrest_rate = ri.groupby(ri.index.hour).is_arrested.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "df0d6cac",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Import matplotlib.pyplot as plt\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Create a line plot of 'hourly_arrest_rate'\n",
    "hourly_arrest_rate.plot()\n",
    "\n",
    "# Add the xlabel, ylabel, and title\n",
    "plt.xlabel('Hour')\n",
    "plt.ylabel('Arrest Rate')\n",
    "plt.title('Arrest Rate by Time of Day')\n",
    "\n",
    "# Display the plot\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "82d10105",
   "metadata": {},
   "source": [
    "#### Are drug-related stops on the rise?\n",
    "* .resample(): change the frequency of the time series observations\n",
    " * .resample('M'): by month\n",
    " * concatentate: .concat([compontent1, compontent2], axis ='') --> shared index\n",
    " * .plot(subplots=True): avoid sharing the same index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "6fcb137b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "stop_datetime\n",
      "2005-12-31    0.006501\n",
      "2006-12-31    0.007258\n",
      "2007-12-31    0.007970\n",
      "2008-12-31    0.007505\n",
      "2009-12-31    0.009889\n",
      "2010-12-31    0.010081\n",
      "2011-12-31    0.009731\n",
      "2012-12-31    0.009921\n",
      "2013-12-31    0.013094\n",
      "2014-12-31    0.013826\n",
      "2015-12-31    0.012266\n",
      "Freq: A-DEC, Name: drugs_related_stop, dtype: float64\n"
     ]
    }
   ],
   "source": [
    "# Calculate the annual rate of drug-related stops\n",
    "# Annual: 'A'\n",
    "print(ri.drugs_related_stop.resample('A').mean())\n",
    "\n",
    "# Save the annual rate of drug-related stops\n",
    "annual_drug_rate = ri.drugs_related_stop.resample('A').mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "365bcd69",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create a line plot of 'annual_drug_rate'\n",
    "annual_drug_rate.plot()\n",
    "\n",
    "# Display the plot\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "e6f0190e",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Calculate and save the annual search rate\n",
    "annual_search_rate = ri.search_conducted.resample('A').mean()\n",
    "\n",
    "# Concatenate 'annual_drug_rate' and 'annual_search_rate'\n",
    "annual = pd.concat([annual_drug_rate, annual_search_rate], axis='columns')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "fcd60b7d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create subplots from 'annual'\n",
    "annual.plot(subplots=True)\n",
    "\n",
    "# Display the subplots\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ba41cd30",
   "metadata": {},
   "source": [
    "#### What violations are caught in each district?\n",
    "* frequency table: pd.crosstab(col1, col2), the col1 will be taken as gender\n",
    "* .loc[]: select from a DataFrame by label\n",
    "* default setting in plot() is lineplot, can us 'kind= ' to change\n",
    "* in bar: stacked = True"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "70797cc5",
   "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>district</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>Zone K1</th>\n",
       "      <td>672</td>\n",
       "      <td>1254</td>\n",
       "      <td>290</td>\n",
       "      <td>120</td>\n",
       "      <td>0</td>\n",
       "      <td>5960</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Zone K2</th>\n",
       "      <td>2061</td>\n",
       "      <td>2962</td>\n",
       "      <td>942</td>\n",
       "      <td>768</td>\n",
       "      <td>481</td>\n",
       "      <td>10448</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Zone K3</th>\n",
       "      <td>2302</td>\n",
       "      <td>2898</td>\n",
       "      <td>705</td>\n",
       "      <td>695</td>\n",
       "      <td>638</td>\n",
       "      <td>12322</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Zone X1</th>\n",
       "      <td>296</td>\n",
       "      <td>671</td>\n",
       "      <td>143</td>\n",
       "      <td>38</td>\n",
       "      <td>74</td>\n",
       "      <td>1119</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Zone X3</th>\n",
       "      <td>2049</td>\n",
       "      <td>3086</td>\n",
       "      <td>769</td>\n",
       "      <td>671</td>\n",
       "      <td>820</td>\n",
       "      <td>8779</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Zone X4</th>\n",
       "      <td>3541</td>\n",
       "      <td>5353</td>\n",
       "      <td>1560</td>\n",
       "      <td>1411</td>\n",
       "      <td>843</td>\n",
       "      <td>9795</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "violation  Equipment  Moving violation  Other  Registration/plates  Seat belt  \\\n",
       "district                                                                        \n",
       "Zone K1          672              1254    290                  120          0   \n",
       "Zone K2         2061              2962    942                  768        481   \n",
       "Zone K3         2302              2898    705                  695        638   \n",
       "Zone X1          296               671    143                   38         74   \n",
       "Zone X3         2049              3086    769                  671        820   \n",
       "Zone X4         3541              5353   1560                 1411        843   \n",
       "\n",
       "violation  Speeding  \n",
       "district             \n",
       "Zone K1        5960  \n",
       "Zone K2       10448  \n",
       "Zone K3       12322  \n",
       "Zone X1        1119  \n",
       "Zone X3        8779  \n",
       "Zone X4        9795  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create a frequency table of districts and violations\n",
    "display(pd.crosstab(ri.district, ri.violation))\n",
    "\n",
    "# Save the frequency table as 'all_zones'\n",
    "all_zones = pd.crosstab(ri.district, ri.violation)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "b540075d",
   "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>district</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>Zone K1</th>\n",
       "      <td>672</td>\n",
       "      <td>1254</td>\n",
       "      <td>290</td>\n",
       "      <td>120</td>\n",
       "      <td>0</td>\n",
       "      <td>5960</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Zone K2</th>\n",
       "      <td>2061</td>\n",
       "      <td>2962</td>\n",
       "      <td>942</td>\n",
       "      <td>768</td>\n",
       "      <td>481</td>\n",
       "      <td>10448</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Zone K3</th>\n",
       "      <td>2302</td>\n",
       "      <td>2898</td>\n",
       "      <td>705</td>\n",
       "      <td>695</td>\n",
       "      <td>638</td>\n",
       "      <td>12322</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "violation  Equipment  Moving violation  Other  Registration/plates  Seat belt  \\\n",
       "district                                                                        \n",
       "Zone K1          672              1254    290                  120          0   \n",
       "Zone K2         2061              2962    942                  768        481   \n",
       "Zone K3         2302              2898    705                  695        638   \n",
       "\n",
       "violation  Speeding  \n",
       "district             \n",
       "Zone K1        5960  \n",
       "Zone K2       10448  \n",
       "Zone K3       12322  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Select rows 'Zone K1' through 'Zone K3'\n",
    "display(all_zones.loc['Zone K1': 'Zone K31'])\n",
    "\n",
    "# Save the smaller table as 'k_zones'\n",
    "k_zones = all_zones.loc['Zone K1': 'Zone K31']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "53cb1714",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create a bar plot of 'k_zones'\n",
    "k_zones.plot(kind='bar')\n",
    "\n",
    "# Display the plot\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "a1b76085",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create a stacked bar plot of 'k_zones'\n",
    "k_zones.plot(kind='bar', stacked= True)\n",
    "\n",
    "# Display the plot\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "03fd3972",
   "metadata": {},
   "source": [
    "#### How long might you be stopped for a violation?\n",
    "* use dictionary to map one set of values to another: map(dict)\n",
    "* and then plot bar chart\n",
    "* plot improvement:\n",
    " * 1) order by size: .sort_values() (ascending)\n",
    " * 2) horizontal bar: kind='barh'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "943c223e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['0-15 Min' '16-30 Min' '30+ Min']\n"
     ]
    }
   ],
   "source": [
    "# Print the unique values in 'stop_duration'\n",
    "print(ri.stop_duration.unique())\n",
    "\n",
    "# Create a dictionary that maps strings to integers\n",
    "mapping = {'0-15 Min': 8, '16-30 Min': 23, '30+ Min': 45}\n",
    "\n",
    "# Convert the 'stop_duration' strings to integers using the 'mapping'\n",
    "ri['stop_minutes'] = ri.stop_duration.map(mapping)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "451ae3e4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ 8 23 45]\n"
     ]
    }
   ],
   "source": [
    "# Print the unique values in 'stop_minutes'\n",
    "print(ri['stop_minutes'].unique())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "703ddfee",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "violation_raw\n",
      "APB                                 17.967033\n",
      "Call for Service                    22.124371\n",
      "Equipment/Inspection Violation      11.445655\n",
      "Motorist Assist/Courtesy            17.741463\n",
      "Other Traffic Violation             13.844490\n",
      "Registration Violation              13.736970\n",
      "Seatbelt Violation                   9.662815\n",
      "Special Detail/Directed Patrol      15.123632\n",
      "Speeding                            10.581562\n",
      "Suspicious Person                   14.910714\n",
      "Violation of City/Town Ordinance    13.254144\n",
      "Warrant                             24.055556\n",
      "Name: stop_minutes, dtype: float64\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "violation_raw\n",
       "Seatbelt Violation                   9.662815\n",
       "Speeding                            10.581562\n",
       "Equipment/Inspection Violation      11.445655\n",
       "Violation of City/Town Ordinance    13.254144\n",
       "Registration Violation              13.736970\n",
       "Other Traffic Violation             13.844490\n",
       "Suspicious Person                   14.910714\n",
       "Special Detail/Directed Patrol      15.123632\n",
       "Motorist Assist/Courtesy            17.741463\n",
       "APB                                 17.967033\n",
       "Call for Service                    22.124371\n",
       "Warrant                             24.055556\n",
       "Name: stop_minutes, dtype: float64"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Calculate the mean 'stop_minutes' for each value in 'violation_raw'\n",
    "print(ri.groupby('violation_raw').stop_minutes.mean())\n",
    "\n",
    "# Save the resulting Series as 'stop_length'\n",
    "stop_length = ri.groupby('violation_raw').stop_minutes.mean()\n",
    "\n",
    "# Sort 'stop_length' by its values and create a horizontal bar plot\n",
    "stop_length.sort_values()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "55f9c95e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Display the plot\n",
    "stop_length.sort_values().plot(kind='barh')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e03815f1",
   "metadata": {},
   "source": [
    "## Exploring the weather dataset\n",
    "* from NOAA: National Centers For Environmental Information"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "7720d592",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              TMIN         TAVG         TMAX\n",
      "count  4017.000000  1217.000000  4017.000000\n",
      "mean     43.484441    52.493016    61.268608\n",
      "std      17.020298    17.830714    18.199517\n",
      "min      -5.000000     6.000000    15.000000\n",
      "25%      30.000000    39.000000    47.000000\n",
      "50%      44.000000    54.000000    62.000000\n",
      "75%      58.000000    68.000000    77.000000\n",
      "max      77.000000    86.000000   102.000000\n"
     ]
    }
   ],
   "source": [
    "# Read 'weather.csv' into a DataFrame named 'weather'\n",
    "weather = pd.read_csv('Weather in ri.csv')\n",
    "\n",
    "# Describe the temperature columns\n",
    "print(weather[['TMIN','TAVG', 'TMAX']].describe())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "3d4c6a01",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create a box plot of the temperature columns\n",
    "weather[['TMIN','TAVG', 'TMAX']].plot(kind='box')\n",
    "\n",
    "# Display the plotweather[['TMIN','TAVG', 'TMAX']]\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "b4cd29be",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "count    4017.000000\n",
      "mean       17.784167\n",
      "std         6.350720\n",
      "min         2.000000\n",
      "25%        14.000000\n",
      "50%        18.000000\n",
      "75%        22.000000\n",
      "max        43.000000\n",
      "Name: TDIFF, dtype: float64\n"
     ]
    }
   ],
   "source": [
    "# Create a 'TDIFF' column that represents temperature difference\n",
    "weather['TDIFF'] = weather.TMAX - weather.TMIN\n",
    "\n",
    "# Describe the 'TDIFF' column\n",
    "print(weather['TDIFF'].describe())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "9ef2e098",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create a histogram with 20 bins to visualize 'TDIFF'\n",
    "weather['TDIFF'].plot(kind='hist', bins=20)\n",
    "\n",
    "# Display the plot\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "44e2002d",
   "metadata": {},
   "source": [
    "#### Categorizing the weather\n",
    "* .sum() is for each columns (in row way), if want by each row --> .sum(axis = 'columns')\n",
    "* .astype('category', ordered = True, categories=cats)\n",
    " * cats = ['ca1', 'ca2', 'ca3']\n",
    "* since the column is ordered, we can use >, < to filter the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "0f166230",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Copy 'WT01' through 'WT22' to a new DataFrame\n",
    "WT = weather.loc[:, 'WT01':'WT22']\n",
    "\n",
    "# Calculate the sum of each row in 'WT'\n",
    "weather['bad_conditions'] = WT.sum(axis='columns')\n",
    "\n",
    "# Replace missing values in 'bad_conditions' with '0'\n",
    "weather['bad_conditions'] = weather.bad_conditions.fillna(0).astype('int')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "a6f9bd9f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create a histogram to visualize 'bad_conditions'\n",
    "weather['bad_conditions'].plot(kind='hist')\n",
    "\n",
    "# Display the plot\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "03804348",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0    1749\n",
      "1     613\n",
      "2     367\n",
      "3     380\n",
      "4     476\n",
      "5     282\n",
      "6     101\n",
      "7      41\n",
      "8       4\n",
      "9       4\n",
      "Name: bad_conditions, dtype: int64\n",
      "bad      1836\n",
      "good     1749\n",
      "worse     432\n",
      "Name: rating, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "# Count the unique values in 'bad_conditions' and sort the index\n",
    "print(weather.bad_conditions.value_counts().sort_index())\n",
    "\n",
    "# Create a dictionary that maps integers to strings\n",
    "mapping = {0:'good', 1:'bad', 2:'bad', 3:'bad', 4:'bad', 5:'worse',\n",
    "6:'worse', 7:'worse', 8:'worse', 9:'worse'}\n",
    "\n",
    "# Convert the 'bad_conditions' integers to strings using the 'mapping'\n",
    "weather['rating'] = weather.bad_conditions.map(mapping)\n",
    "\n",
    "# Count the unique values in 'rating'\n",
    "print(weather['rating'].value_counts())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "044ec326",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0    bad\n",
      "1    bad\n",
      "2    bad\n",
      "3    bad\n",
      "4    bad\n",
      "Name: rating, dtype: category\n",
      "Categories (3, object): ['bad', 'good', 'worse']\n"
     ]
    }
   ],
   "source": [
    "# Create a list of weather ratings in logical order\n",
    "cats = ['good', 'bad', 'worse']\n",
    "\n",
    "# Change the data type of 'rating' to category\n",
    "weather['rating'] = weather.rating.astype('category', #ordered=True,\n",
    "                                          #categories = cats\n",
    "                                         )\n",
    "\n",
    "# Examine the head of 'rating'\n",
    "print(weather['rating'].head())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0d2fe833",
   "metadata": {},
   "source": [
    "#### Merging Datasets\n",
    "* reset_index(inplace = True)\n",
    "* pd.merge(left=df1, right=df2, left_on=' ', right_on=' ', how='left')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "90298ddd",
   "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_datetime</th>\n",
       "      <th>stop_date</th>\n",
       "      <th>stop_time</th>\n",
       "      <th>driver_gender</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>district</th>\n",
       "      <th>frisk</th>\n",
       "      <th>stop_minutes</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2005-01-04 12:55:00</td>\n",
       "      <td>2005/1/4</td>\n",
       "      <td>12:55</td>\n",
       "      <td>M</td>\n",
       "      <td>White</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",
       "      <td>Zone X4</td>\n",
       "      <td>False</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2005-01-23 23:15:00</td>\n",
       "      <td>2005/1/23</td>\n",
       "      <td>23:15</td>\n",
       "      <td>M</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>Zone K3</td>\n",
       "      <td>False</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2005-02-17 04:15:00</td>\n",
       "      <td>2005/2/17</td>\n",
       "      <td>04:15</td>\n",
       "      <td>M</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>Zone X4</td>\n",
       "      <td>False</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2005-02-20 17:15:00</td>\n",
       "      <td>2005/2/20</td>\n",
       "      <td>17:15</td>\n",
       "      <td>M</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>Zone X1</td>\n",
       "      <td>False</td>\n",
       "      <td>23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2005-02-24 01:20:00</td>\n",
       "      <td>2005/2/24</td>\n",
       "      <td>01:20</td>\n",
       "      <td>F</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>Zone X3</td>\n",
       "      <td>False</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        stop_datetime  stop_date stop_time driver_gender driver_race  \\\n",
       "0 2005-01-04 12:55:00   2005/1/4     12:55             M       White   \n",
       "1 2005-01-23 23:15:00  2005/1/23     23:15             M       White   \n",
       "2 2005-02-17 04:15:00  2005/2/17     04:15             M       White   \n",
       "3 2005-02-20 17:15:00  2005/2/20     17:15             M       White   \n",
       "4 2005-02-24 01:20:00  2005/2/24     01:20             F       White   \n",
       "\n",
       "                    violation_raw  violation  search_conducted search_type  \\\n",
       "0  Equipment/Inspection Violation  Equipment             False         NaN   \n",
       "1                        Speeding   Speeding             False         NaN   \n",
       "2                        Speeding   Speeding             False         NaN   \n",
       "3                Call for Service      Other             False         NaN   \n",
       "4                        Speeding   Speeding             False         NaN   \n",
       "\n",
       "    stop_outcome  is_arrested stop_duration  drugs_related_stop district  \\\n",
       "0       Citation        False      0-15 Min               False  Zone X4   \n",
       "1       Citation        False      0-15 Min               False  Zone K3   \n",
       "2       Citation        False      0-15 Min               False  Zone X4   \n",
       "3  Arrest Driver         True     16-30 Min               False  Zone X1   \n",
       "4       Citation        False      0-15 Min               False  Zone X3   \n",
       "\n",
       "   frisk  stop_minutes  \n",
       "0  False             8  \n",
       "1  False             8  \n",
       "2  False             8  \n",
       "3  False            23  \n",
       "4  False             8  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Reset the index of 'ri'\n",
    "ri.reset_index(inplace=True)\n",
    "\n",
    "# Examine the head of 'ri'\n",
    "display(ri.head())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "a7a5bcba",
   "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>DATE</th>\n",
       "      <th>rating</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2005/1/1</td>\n",
       "      <td>bad</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2005/1/2</td>\n",
       "      <td>bad</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2005/1/3</td>\n",
       "      <td>bad</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2005/1/4</td>\n",
       "      <td>bad</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2005/1/5</td>\n",
       "      <td>bad</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       DATE rating\n",
       "0  2005/1/1    bad\n",
       "1  2005/1/2    bad\n",
       "2  2005/1/3    bad\n",
       "3  2005/1/4    bad\n",
       "4  2005/1/5    bad"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create a DataFrame from the 'DATE' and 'rating' columns\n",
    "weather_rating = weather[['DATE', 'rating']]\n",
    "\n",
    "# Examine the head of 'weather_rating'\n",
    "display(weather_rating.head())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "83afbdad",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(86536, 16)\n",
      "(86536, 18)\n"
     ]
    }
   ],
   "source": [
    "# Examine the shape of 'ri'\n",
    "print(ri.shape)\n",
    "\n",
    "# Merge 'ri' and 'weather_rating' using a left join\n",
    "ri_weather = pd.merge(left=ri, right=weather_rating, left_on='stop_date', right_on='DATE', how='left')\n",
    "\n",
    "# Examine the shape of 'ri_weather'\n",
    "print(ri_weather.shape)\n",
    "\n",
    "# Set 'stop_datetime' as the index of 'ri_weather'\n",
    "ri_weather.set_index('stop_datetime', inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4179e3bb",
   "metadata": {},
   "source": [
    "#### Does weather affect the arrest rate?\n",
    "* .unstack() convert a multi-indexed series to a dataframe\n",
    "* .pivot_table(index=' ', columns=' ', value=' ')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "162b8b77",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "search_conducted       0.038215\n",
      "is_arrested            0.035569\n",
      "drugs_related_stop     0.009961\n",
      "frisk                  0.003501\n",
      "stop_minutes          11.758008\n",
      "dtype: float64\n"
     ]
    }
   ],
   "source": [
    "# Calculate the overall arrest rate\n",
    "print(ri_weather.mean())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "121b9529",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.0355690117407784\n"
     ]
    }
   ],
   "source": [
    "# Calculate the overall arrest rate\n",
    "print(ri_weather.is_arrested.mean())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "cab988c9",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "rating\n",
      "bad      0.036261\n",
      "good     0.033715\n",
      "worse    0.041667\n",
      "Name: is_arrested, dtype: float64\n"
     ]
    }
   ],
   "source": [
    "# Calculate the arrest rate for each 'rating'\n",
    "print(ri_weather.groupby('rating').is_arrested.mean())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "8b666f4d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "violation            rating\n",
      "Equipment            bad       0.066311\n",
      "                     good      0.059007\n",
      "                     worse     0.097357\n",
      "Moving violation     bad       0.058050\n",
      "                     good      0.056227\n",
      "                     worse     0.065860\n",
      "Other                bad       0.087443\n",
      "                     good      0.076966\n",
      "                     worse     0.062893\n",
      "Registration/plates  bad       0.098160\n",
      "                     good      0.081574\n",
      "                     worse     0.115625\n",
      "Seat belt            bad       0.022493\n",
      "                     good      0.028587\n",
      "                     worse     0.000000\n",
      "Speeding             bad       0.013314\n",
      "                     good      0.013405\n",
      "                     worse     0.016886\n",
      "Name: is_arrested, dtype: float64\n"
     ]
    }
   ],
   "source": [
    "# Calculate the arrest rate for each 'violation' and 'rating'\n",
    "print(ri_weather.groupby(['violation', 'rating']).is_arrested.mean())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "51576b76",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "violation            rating\n",
      "Equipment            bad       0.066311\n",
      "                     good      0.059007\n",
      "                     worse     0.097357\n",
      "Moving violation     bad       0.058050\n",
      "                     good      0.056227\n",
      "                     worse     0.065860\n",
      "Other                bad       0.087443\n",
      "                     good      0.076966\n",
      "                     worse     0.062893\n",
      "Registration/plates  bad       0.098160\n",
      "                     good      0.081574\n",
      "                     worse     0.115625\n",
      "Seat belt            bad       0.022493\n",
      "                     good      0.028587\n",
      "                     worse     0.000000\n",
      "Speeding             bad       0.013314\n",
      "                     good      0.013405\n",
      "                     worse     0.016886\n",
      "Name: is_arrested, dtype: float64\n"
     ]
    }
   ],
   "source": [
    "# Save the output of the groupby operation from the last exercise\n",
    "arrest_rate = ri_weather.groupby(['violation', 'rating']).is_arrested.mean()\n",
    "\n",
    "# Print the 'arrest_rate' Series\n",
    "print(arrest_rate)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "b0a9194c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.05804964058049641\n"
     ]
    }
   ],
   "source": [
    "# Print the arrest rate for moving violations in bad weather\n",
    "print(arrest_rate.loc['Moving violation', 'bad'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "215b41fc",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "rating\n",
      "bad      0.013314\n",
      "good     0.013405\n",
      "worse    0.016886\n",
      "Name: is_arrested, dtype: float64\n"
     ]
    }
   ],
   "source": [
    "# Print the arrest rates for speeding violations in all three weather conditions\n",
    "print(arrest_rate.loc['Speeding'] ) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "7ec6a928",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "rating                    bad      good     worse\n",
      "violation                                        \n",
      "Equipment            0.066311  0.059007  0.097357\n",
      "Moving violation     0.058050  0.056227  0.065860\n",
      "Other                0.087443  0.076966  0.062893\n",
      "Registration/plates  0.098160  0.081574  0.115625\n",
      "Seat belt            0.022493  0.028587  0.000000\n",
      "Speeding             0.013314  0.013405  0.016886\n"
     ]
    }
   ],
   "source": [
    "# Unstack the 'arrest_rate' Series into a DataFrame\n",
    "print(arrest_rate.unstack())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "26fb1d68",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "rating                    bad      good     worse\n",
      "violation                                        \n",
      "Equipment            0.066311  0.059007  0.097357\n",
      "Moving violation     0.058050  0.056227  0.065860\n",
      "Other                0.087443  0.076966  0.062893\n",
      "Registration/plates  0.098160  0.081574  0.115625\n",
      "Seat belt            0.022493  0.028587  0.000000\n",
      "Speeding             0.013314  0.013405  0.016886\n"
     ]
    }
   ],
   "source": [
    "# Create the same DataFrame using a pivot table\n",
    "print(ri_weather.pivot_table(index='violation', columns='rating', values='is_arrested'))"
   ]
  }
 ],
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