PoliceCalls_PreProcessing.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "e17c87ce",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "from datetime import datetime\n",
    "from datetime import time\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "e78f68db",
   "metadata": {},
   "outputs": [],
   "source": [
    "# reading all call records\n",
    "calls_22 = pd.read_csv(\"data/policecalls2022.csv\")\n",
    "calls_23 = pd.read_csv(\"data/policecalls2023.csv\")\n",
    "calls_24 = pd.read_csv(\"data/policecalls2024.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "09442e5c",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "calls = pd.concat([calls_22, calls_23, calls_24], ignore_index=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "0807e9e1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "CDTS                object\n",
       "EID                  int64\n",
       "START_DATE          object\n",
       "CALL_NUMBER         object\n",
       "PRIORITY             int64\n",
       "REPORT_DATE         object\n",
       "OFFENSE_DATE        object\n",
       "OFFENSE_TIME        object\n",
       "CALLTYPE_CODE       object\n",
       "CALL_TYPE           object\n",
       "FINAL_DISPO_CODE    object\n",
       "FINAL_DISPO         object\n",
       "ADDRESS             object\n",
       "CITY                object\n",
       "STATE               object\n",
       "dtype: object"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "calls.dtypes"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8a83f5b1",
   "metadata": {},
   "source": [
    "### Columns\n",
    "1. CDTS: Call Date Time Stamp - Represents the timestamp indicating the time of the call or when the system recorded the data.\n",
    "2. EID: Event ID - Appears to be a unique identifier for an event or call.\n",
    "3. START_DATE: Indicates the date and time when the call started.\n",
    "4. CALL_NUMBER: May represent a unique number or identifier for the call.\n",
    "5. PRIORITY: Indicates the priority of the call or event (e.g., urgent, important, normal).\n",
    "6. REPORT_DATE: Indicates the date when the event or call was reported.\n",
    "7. OFFENSE_DATE: Represents the date when the offense or event occurred.\n",
    "8. OFFENSE_TIME: Represents the time when the offense or event occurred.\n",
    "9. CALLTYPE_CODE: Represents the type of call with a code.\n",
    "10. CALL_TYPE: Describes the actual type or content of the call.\n",
    "11. FINAL_DISPO_CODE: Represents a code for the final disposition or outcome of the call or event.\n",
    "12. FINAL_DISPO: Describes the final disposition or outcome of the call or event.\n",
    "13. ADDRESS: Represents the address where the event or call occurred.\n",
    "14. CITY: Represents the city where the event or call occurred.\n",
    "15. STATE: Represents the state where the event or call occurred (applicable for the United States)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "c8aec6d7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Index(['CDTS', 'EID', 'START_DATE', 'CALL_NUMBER', 'PRIORITY', 'REPORT_DATE',\n",
      "       'OFFENSE_DATE', 'OFFENSE_TIME', 'CALLTYPE_CODE', 'CALL_TYPE',\n",
      "       'FINAL_DISPO_CODE', 'FINAL_DISPO', 'ADDRESS', 'CITY', 'STATE'],\n",
      "      dtype='object')\n"
     ]
    }
   ],
   "source": [
    "print(calls.columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "a8704ab7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "CDTS                     0\n",
       "EID                      0\n",
       "START_DATE               0\n",
       "CALL_NUMBER              0\n",
       "PRIORITY                 0\n",
       "REPORT_DATE              0\n",
       "OFFENSE_DATE             0\n",
       "OFFENSE_TIME             0\n",
       "CALLTYPE_CODE            0\n",
       "CALL_TYPE                0\n",
       "FINAL_DISPO_CODE         0\n",
       "FINAL_DISPO              0\n",
       "ADDRESS             113995\n",
       "CITY                     0\n",
       "STATE                    0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# checking for missingness\n",
    "calls.isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "c2c9f07b",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['SUSPICIOUS PERSON', 'DISTURBANCE', 'UNK TYPE 911 CALL',\n",
       "       'SUSPICIOUS CIRCUMSTANCES', 'NARCOTICS',\n",
       "       'DISTURBANCE, FIRECRACKERS', 'FIREARMS DISCHARGED',\n",
       "       'PEDESTRIAN STOP', 'WELFARE CHECK (COMBINED EVENT)',\n",
       "       'PUBLIC SAFETY ASSISTANCE', 'ALARM, AUDIBLE', 'DISTURBANCE, MUSIC',\n",
       "       'PROWLER', 'WELFARE CHECK', 'ALARM, SILENT', 'RECKLESS DRIVING',\n",
       "       'SUSPICIOUS PERSON W/ WEAPON', 'MENTALLY DISTURBED PERSON',\n",
       "       'BATTERY', 'BRANDISHING A WEAPON', 'PARKING VIOLATION',\n",
       "       'VEHICLE STOP', 'SUSPICIOUS VEHICLE', 'DISTURBANCE, FAMILY',\n",
       "       'MISDEMEANOR HIT AND RUN', 'THEFT', 'DISTURBANCE, FIGHT',\n",
       "       'STOLEN VEHICLE', 'VEHICLE ACCIDENT, PROPERTY DAMAGE',\n",
       "       'RECOVERED STOLEN VEHICLE', 'CARRYING A CONCEALED WEAPON',\n",
       "       'FOUND PROPERTY', 'TRESPASSING', 'VEHICLE STOP ON FEMALE',\n",
       "       'TRAFFIC HAZARD', 'MEET THE CITIZEN', 'DISTURBANCE, UNKNOWN',\n",
       "       'ARMED ROBBERY', 'MISDEMEANOR DUI', 'GRAND THEFT',\n",
       "       'MISSING FEMALE JUVENILE', 'CORONERS CASE', 'MALICIOUS MISCHIEF',\n",
       "       'FELONY WANT', 'VEHICLE ACCIDENT, MINOR INJURIES',\n",
       "       'VEHICLE BURGLARY', 'DISTURBANCE, NEIGHBOR', 'INDECENT EXPOSURE',\n",
       "       'VEHICLE ACCIDENT, UNKNOWN INJURIES', 'BURGLARY (460)',\n",
       "       'FIRE DEPARTMENT REQUEST FOR PD', 'MENTALLY DISTURBED FEMALE',\n",
       "       'MISDEMEANOR WANT', 'CRIMINAL THREATS', 'DISTURBANCE, WEAPON',\n",
       "       'STRONG ARM ROBBERY', 'ILLEGAL WEAPONS',\n",
       "       'ASSAULT WITH DEADLY WEAPON', 'VIOLATION OF PROTECTIVE ORDER',\n",
       "       'ABANDONED VEHICLE', 'MISSING PERSON',\n",
       "       'VEHICLE ACCIDENT, MAJOR INJURIES', 'OPEN DOOR',\n",
       "       'ANIMAL COMPLAINT', 'BURGLARY  REPORT  (460)', 'ALARM',\n",
       "       '1091AB          - VICIOUS ANIMAL (COMBINED EVENT)',\n",
       "       'USE OF CONTROLLED SUBSTANCE', 'DISTURBANCE, MOTORCYCLE',\n",
       "       'VEHICLE ACCIDENT, AMB DISPATCHED', 'SUSPICIOUS PACKAGE',\n",
       "       'ASSAULT WITH DEADLY WEAPON (COMBINED EVENT)',\n",
       "       'DRIVING W/SUSPENDED LICENSE',\n",
       "       'DISTURBANCE, FIGHT (COMBINED EVENT)',\n",
       "       'SHOOTING INTO OCCP VEH OR DWELLING', 'BATTERY (COMBINED EVENT)',\n",
       "       'MISSING PERSON, MENTAL HANDICAP', 'DISTURBANCE (COMBINED EVENT)',\n",
       "       'MALICIOUS MISCHIEF, GANG RELATED', 'DEAD ANIMAL', 'ROBBERY',\n",
       "       'VICIOUS ANIMAL', 'MISSING JUVENILE', 'FOUND, MISSING PERSON',\n",
       "       'CIVIL MATTER', 'EXPLOSION', 'ARSON (447A)', 'FELONY HIT AND RUN',\n",
       "       'RESISTING ARREST, GANG RELATED', 'EXPIRED REGISTRATION',\n",
       "       'TAMPERING WITH A VEHICLE', 'DISTURBANCE, FAMILY (COMBINED EVENT)',\n",
       "       'FIRE (SPECIFY TYPE)', 'PETTY THEFT (BROADCAST ONLY)',\n",
       "       'RESISTING ARREST', 'ELDER/DEPENDENT ADULT ABUSE',\n",
       "       'DISTURBANCE, UNKNOWN (COMBINED EVENT)', 'BATTERY ON AN OFFICER',\n",
       "       'DRUNK IN PUBLIC', 'ASSAULT', 'HANDICAPPED PARKING VIOLATION',\n",
       "       'PETTY THEFT', 'POSSESSION OF NARCOTICS, GANG RELATED', 'VAGRANT',\n",
       "       'EMBEZZLEMENT', 'SICK PERSON', 'PERSON CALLING FOR HELP',\n",
       "       'SUSPICIOUS FEMALE', 'ARMED ROBBERY (COMBINED EVENT)',\n",
       "       'RECEIVE/POSSESS STOLEN PROP', 'ASSAULT WITH DEADLY WEAPON, GANG',\n",
       "       'CRUELTY TO ANIMALS', 'SUSPICIOUS CIRCUMSTANCES (COMBINED EVENT)',\n",
       "       'REFUSAL TO LEAVE PROPERTY', 'FEMALE CALLING FOR HELP',\n",
       "       'TRAFFIC CONTROL', 'GARBAGE COMPLAINT', 'DISTURBANCE, JUVENILE',\n",
       "       'THROWING SUBSTANCES AT VEHICLE', 'CHILD ABUSE', 'CHILD BEATING',\n",
       "       'MISSING FEMALE', 'BAD CHECKS', 'MOLEST/ANNOY UNDER 18YRS',\n",
       "       'REGISTRATION OF SEX OFFENDER', 'SOLICITING FOR PROSTITUTION',\n",
       "       'UNLICENSED DRIVER', 'POSSESSION OF NARCOTICS', 'SPEED CONTEST',\n",
       "       'INJURED ANIMAL', 'PEDESTRIAN STOP ON FEMALE',\n",
       "       'COMMUNITY POLICING MEETING', 'THEFT OF RECYCLABLES',\n",
       "       'ASSAULT ON AN OFFICER', 'OBSTRUCT STREETS OR SIDEWALK',\n",
       "       'STALKING', 'OBSCENE OR HARASSING PH CALLS',\n",
       "       'PURSE SNATCH ROBBERY', 'PAROLE VIOLATION',\n",
       "       'ILLEGAL WEAPONS, GANG RELATED', 'FORGERY',\n",
       "       'BATTERY, SERIOUS INJURY', 'BOMB THREAT', 'DEFRAUDING AN INKEEPER',\n",
       "       'DISTURBANCE, WEAPON (COMBINED EVENT)', 'COMMUNIT POLICING BIKE',\n",
       "       'PERSON STABBED', 'POSSESSION OF CONTROLLED SUBSTANCE',\n",
       "       'ALARM, SVRN', 'FALSE IMPRISONMENT', 'NARCOTICS, GANG RELATED',\n",
       "       'PERSON SHOT', 'STOLEN VEHICLE, GANG RELATED', 'FELONY DUI',\n",
       "       'COMMUNITY POLICING SCHOOL', 'INJURED PERSON',\n",
       "       'BATTERY, GANG RELATED', 'EXTORTION', 'SPEEDING', 'HATE CRIMES',\n",
       "       'DOMESTIC VIOLENCE  (COMBINED EVENT)', 'HI-TECH CRIMES',\n",
       "       'STRONG ARM ROBBERY (COMBINED EVENT)', 'POSSESSION OF MARIJUANA',\n",
       "       'DISTURBANCE, GANG', 'SUSPICIOUS PERSON (GANG)',\n",
       "       'MINOR IN POSSESSION OF ALCOHOL', 'KIDNAPPING',\n",
       "       'OBSTRUCT MEDICAL CLINIC-PERSON', 'MURDER',\n",
       "       'POSSESSION OF CONTROLLED SUBSTANCE, GANG RELATED', 'OPEN WINDOW',\n",
       "       'DISTURBANCE, FIGHT GANG', 'BREACH OF AOA', 'DROWNING',\n",
       "       'ROBBERY, GANG RELATED', 'TAKE A REPORT', 'INTOXICATED PERSON',\n",
       "       'VEHICLE STOP, SEND FILL', 'COMMUNITY POLICING -CITZ ASSIST',\n",
       "       'BATTERY ON A PEACE OFFICER', 'PEDESTRIAN STOP, SEND FILL',\n",
       "       'TRUANT (TABS)', 'INJURE, OBSTRUCT A BUSINESS',\n",
       "       'BOMB REMOVAL, STANDBY', 'BRANDISHING A WEAPON, GANG',\n",
       "       'SOLICITING FOR LEWD CONDUCT', 'ASSAULT AND BATTERY',\n",
       "       'SELL OR FURN ALCOHOL TO MINOR', 'CARJACKING (COMBINED EVENT)',\n",
       "       'ARMED ROBBERY, GANG RELATED', 'THEFT, GANG RELATED',\n",
       "       'COMMUNITY POLICING OFFICE', 'STRAY HORSE',\n",
       "       'W&I-UNDER JURIS OF JUV COURT',\n",
       "       'SUSPICIOUS PERSON ON RUNWAY - SJIA',\n",
       "       'VEHICLE STOP ON X, SEND FILL', 'W&I UNCONTROLLABLE JUVENILE',\n",
       "       'PETTY THEFT PRIOR CONVICTION', 'NOISY ANIMAL',\n",
       "       'DISTURBANCE, JUVENILE GANG', 'ALTERED VIN NUMBER',\n",
       "       'SALE OF TOBACCO TO MINOR', 'BAR CHECK',\n",
       "       'CURFEW TRANS/CURF CTR TO RESD'], dtype=object)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "calls['CALL_TYPE'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "4ec0bcbb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "CALL_TYPE\n",
       "VEHICLE STOP                    247413\n",
       "DISTURBANCE                     181151\n",
       "WELFARE CHECK                   154367\n",
       "ALARM, AUDIBLE                  129428\n",
       "PARKING VIOLATION                94802\n",
       "                                 ...  \n",
       "W&I UNCONTROLLABLE JUVENILE          7\n",
       "PETTY THEFT PRIOR CONVICTION         7\n",
       "SUSPICIOUS PERSON (GANG)             5\n",
       "THEFT, GANG RELATED                  5\n",
       "NOISY ANIMAL                         4\n",
       "Name: count, Length: 200, dtype: int64"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "calls_22['CALL_TYPE'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "cd5b6a1b",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['1066', '415', '911UNK', 'SUSCIR', '11300', '415FC', '1057',\n",
       "       '1095', 'WELCKEMS', 'AID', '1033A', '415M', '1070', 'WELCK',\n",
       "       '1033S', '23103', '1066W', '5150', '242', '417', '22500', '1195',\n",
       "       '1154', '415F', '20002', '484', '415A', '10851', '1182', '10851R',\n",
       "       '12025', 'FNDPRP', '602PC', '1195X', '1125', '1062', '415UNK',\n",
       "       '211A', '23152', '487', '1065JX', '1055', '594', '6F', '1181',\n",
       "       '459VEH', '415N', '314', '1183', '459', 'FDAID', '5150X', '6M',\n",
       "       '422', '415W', '211SA', '12020', '245', 'DVRO', '1124', '1065',\n",
       "       '1180', '1034', '1091', '459R', '1033', '1091AEMS', '11550',\n",
       "       '415MC', '1179', '1066P', '245EMS', '14601', '415AEMS', '246',\n",
       "       '242EMS', '1065MH', '415EMS', '594G', '1091D', '211', '1091A',\n",
       "       '1065J', '1065F', 'CIVIL', '1080', '451', '20001', '148G', '4000A',\n",
       "       '10852', 'THEFT', '415FEMS', '904', '484BOL', '148', '368',\n",
       "       '415UNKEMS', '243', '647F', '240', '22507', '488', '11377G', '647',\n",
       "       '503', '1046', '1067', '1066X', '211AEMS', '496', '245G', '597',\n",
       "       'SUSCIREMS', '602N', '1067X', '1184', '1058', '415J', '23110',\n",
       "       '273A', '273D', '1065X', '476A', '647.6', '290', '647B', '12500',\n",
       "       '11377', '23109', '1091C', '1095X', 'CPM', '484R', '241', '647C',\n",
       "       '646.9', 'CODE6F', '653M', 'CODE6M', '211PS', '3056', '12020G',\n",
       "       '470', '243D', 'CODE10', '537', '415WEMS', 'CPB', '1072', '11350',\n",
       "       '1033L', '236', '11300G', '1071', '10851G', '23153', 'CPS', '1045',\n",
       "       '242G', '518', '22350', '422.6', '273.5EMS', '502', '211SAEMS',\n",
       "       '11357', '415G', '1066G', '25662', '207', 'BURG', '602.11', '187',\n",
       "       '11350G', '1035', 'CCW', '415AG', '1033B', '1032', 'ADW', '211G',\n",
       "       '1050', '1051', '1196', 'CPC', '243.2', '1096', '601T', '602J',\n",
       "       'BOMB', '417G', '647A', '240/242', '25658.A', '215EMS', '211AG',\n",
       "       '484G', 'CPO', '1091H', '300', '1066R', '1196X', '601', '666',\n",
       "       '1091B', '415JG', '10751', '240242', '308', 'BARCK', 'CURF'],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "calls['CALLTYPE_CODE'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "63cdb9f6",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "CALLTYPE_CODE\n",
       "1195      439816\n",
       "415       370679\n",
       "WELCK     324931\n",
       "1033A     279998\n",
       "22500     199219\n",
       "           ...  \n",
       "602.11         7\n",
       "10751          7\n",
       "308            7\n",
       "BARCK          7\n",
       "CURF           7\n",
       "Name: count, Length: 212, dtype: int64"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "calls['CALLTYPE_CODE'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "ec3563bf",
   "metadata": {},
   "outputs": [],
   "source": [
    "# let us first make column names lowercase\n",
    "calls.columns = calls.columns.str.lower()\n",
    "# calls_22.columns = calls_22.columns.str.lower()\n",
    "# calls_23.columns = calls_22.columns.str.lower()\n",
    "# calls_24.columns = calls_22.columns.str.lower()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "e4050426",
   "metadata": {},
   "outputs": [],
   "source": [
    "# EID is a identification so let us make it an object\n",
    "calls['eid'] = calls['eid'].astype(str)\n",
    "# calls_22['eid'] = calls_22['eid'].astype(str)\n",
    "# calls_23['eid'] = calls_23['eid'].astype(str)\n",
    "# calls_24['eid'] = calls_24['eid'].astype(str)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "191f5ae0",
   "metadata": {},
   "outputs": [],
   "source": [
    "calls = calls.drop(columns=['city','state'])\n",
    "# calls_22 = calls_22.drop(columns=['city','state'])\n",
    "# calls_23 = calls_23.drop(columns=['city','state'])\n",
    "# calls_24 = calls_24.drop(columns=['city','state'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "c3967f51",
   "metadata": {},
   "outputs": [],
   "source": [
    "calls[\"Timestamp\"] = pd.to_datetime(calls[\"cdts\"].str[:14], format='%Y%m%d%H%M%S', errors='coerce')\n",
    "# calls_22[\"Timestamp\"] = pd.to_datetime(calls_22[\"cdts\"].str[:14], format='%Y%m%d%H%M%S', errors='coerce')\n",
    "# calls_23[\"Timestamp\"] = pd.to_datetime(calls_23[\"cdts\"].str[:14], format='%Y%m%d%H%M%S', errors='coerce')\n",
    "# calls_24[\"Timestamp\"] = pd.to_datetime(calls_24[\"cdts\"].str[:14], format='%Y%m%d%H%M%S', errors='coerce')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "67bc413c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Empty DataFrame\n",
      "Columns: [cdts, eid, start_date, call_number, priority, report_date, offense_date, offense_time, calltype_code, call_type, final_dispo_code, final_dispo, address, Timestamp]\n",
      "Index: []\n"
     ]
    }
   ],
   "source": [
    "different_dates = calls[calls[\"report_date\"] != calls[\"offense_date\"]]\n",
    "\n",
    "print(different_dates)\n",
    "\n",
    "# different_dates_22 = calls_22[calls_22[\"report_date\"] != calls_22[\"offense_date\"]]\n",
    "# different_dates_23 = calls_23[calls_23[\"report_date\"] != calls_23[\"offense_date\"]]\n",
    "# different_dates_24 = calls_24[calls_24[\"report_date\"] != calls_24[\"offense_date\"]]\n",
    "\n",
    "# print(different_dates_22)\n",
    "# print(different_dates_23)\n",
    "# print(different_dates_24)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "ddeb3f1e",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                     cdts      eid            start_date call_number  \\\n",
      "0        20220101042916PS  8913184  1/1/2022 12:00:00 AM  P220010180   \n",
      "1        20220101043036PS  8913196  1/1/2022 12:00:00 AM  P220010191   \n",
      "2        20220101043827PS  8913252  1/1/2022 12:00:00 AM  P220010236   \n",
      "3        20220101044026PS  8913216  1/1/2022 12:00:00 AM  P220010206   \n",
      "4        20220102052501PS  8914182  1/1/2022 12:00:00 AM  P220010965   \n",
      "...                   ...      ...                   ...         ...   \n",
      "3998458  20240228143421PS  9917241  3/2/2024 12:00:00 AM  P240590531   \n",
      "3998459  20240228143423PS  9917234  3/2/2024 12:00:00 AM  P240590526   \n",
      "3998460  20240228143755PS  9917176  3/2/2024 12:00:00 AM  P240590481   \n",
      "3998461  20240228144042PS  9917220  3/2/2024 12:00:00 AM  P240590514   \n",
      "3998462  20240228144148PS  9917267  3/2/2024 12:00:00 AM  P240590551   \n",
      "\n",
      "         priority            report_date           offense_date offense_time  \\\n",
      "0               2   1/1/2022 12:00:00 AM   1/1/2022 12:00:00 AM     02:49:02   \n",
      "1               2   1/1/2022 12:00:00 AM   1/1/2022 12:00:00 AM     03:05:51   \n",
      "2               2   1/1/2022 12:00:00 AM   1/1/2022 12:00:00 AM     04:35:25   \n",
      "3               2   1/1/2022 12:00:00 AM   1/1/2022 12:00:00 AM     03:38:14   \n",
      "4               4   1/1/2022 12:00:00 AM   1/1/2022 12:00:00 AM     23:25:49   \n",
      "...           ...                    ...                    ...          ...   \n",
      "3998458         6  2/28/2024 12:00:00 AM  2/28/2024 12:00:00 AM     14:08:34   \n",
      "3998459         5  2/28/2024 12:00:00 AM  2/28/2024 12:00:00 AM     14:06:06   \n",
      "3998460         2  2/28/2024 12:00:00 AM  2/28/2024 12:00:00 AM     13:14:37   \n",
      "3998461         6  2/28/2024 12:00:00 AM  2/28/2024 12:00:00 AM     13:53:41   \n",
      "3998462         6  2/28/2024 12:00:00 AM  2/28/2024 12:00:00 AM     14:32:08   \n",
      "\n",
      "        calltype_code                 call_type final_dispo_code  \\\n",
      "0                1066         SUSPICIOUS PERSON                N   \n",
      "1                 415               DISTURBANCE                N   \n",
      "2              911UNK         UNK TYPE 911 CALL              CAN   \n",
      "3              SUSCIR  SUSPICIOUS CIRCUMSTANCES                N   \n",
      "4               11300                 NARCOTICS                A   \n",
      "...               ...                       ...              ...   \n",
      "3998458          1195              VEHICLE STOP                N   \n",
      "3998459         22500         PARKING VIOLATION                N   \n",
      "3998460           242                   BATTERY                R   \n",
      "3998461          1195              VEHICLE STOP                N   \n",
      "3998462          1195              VEHICLE STOP                D   \n",
      "\n",
      "                                          final_dispo  \\\n",
      "0            No report required; dispatch record only   \n",
      "1            No report required; dispatch record only   \n",
      "2                                            Canceled   \n",
      "3            No report required; dispatch record only   \n",
      "4                                         Arrest Made   \n",
      "...                                               ...   \n",
      "3998458      No report required; dispatch record only   \n",
      "3998459      No report required; dispatch record only   \n",
      "3998460                                  Report taken   \n",
      "3998461      No report required; dispatch record only   \n",
      "3998462  Traffic Citation Issued, Hazardous Violation   \n",
      "\n",
      "                             address           Timestamp  \n",
      "0             [800]-[900] JEANNE AV  2022-01-01 04:29:16  \n",
      "1        [1500]-[1600] SOUTHWEST EX  2022-01-01 04:30:36  \n",
      "2         [4800]-[4900] WESTMONT AV  2022-01-01 04:38:27  \n",
      "3         [3600]-[3700] GREENLEE DR  2022-01-01 04:40:26  \n",
      "4        E CAPITOL EX & EVERGREEN PL 2022-01-02 05:25:01  \n",
      "...                              ...                 ...  \n",
      "3998458    [5700]-[5800] ALMADEN RD  2024-02-28 14:34:21  \n",
      "3998459     [100]-[200] MCCREERY AV  2024-02-28 14:34:23  \n",
      "3998460       [0]-[100] N MARKET ST  2024-02-28 14:37:55  \n",
      "3998461        STORY RD & HOPKINS DR 2024-02-28 14:40:42  \n",
      "3998462                          NaN 2024-02-28 14:41:48  \n",
      "\n",
      "[3998463 rows x 14 columns]\n"
     ]
    }
   ],
   "source": [
    "different_dispo = calls[calls[\"final_dispo_code\"] != calls[\"final_dispo\"]]\n",
    "print(different_dispo)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "03037011",
   "metadata": {},
   "source": [
    "Every value for report_date and offense_date is same.\n",
    "Remove unnecessary columns: report_date, offense_date, start_date, cdts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "1fb47801",
   "metadata": {},
   "outputs": [],
   "source": [
    "pp_calls = calls.drop(columns=['eid',\n",
    "                                     'call_number',\n",
    "                                     'report_date', \n",
    "                                     'offense_date', \n",
    "                                     'start_date', \n",
    "                                     'cdts'])\n",
    "# pp_calls_22 = calls_22.drop(columns=['eid',\n",
    "#                                      'call_number',\n",
    "#                                      'report_date', \n",
    "#                                      'offense_date', \n",
    "#                                      'start_date', \n",
    "#                                      'cdts'])\n",
    "\n",
    "# pp_calls_23 = calls_23.drop(columns=['eid',\n",
    "#                                      'call_number',\n",
    "#                                      'report_date', \n",
    "#                                      'offense_date', \n",
    "#                                      'start_date', \n",
    "#                                      'cdts'])\n",
    "\n",
    "# pp_calls_24 = calls_24.drop(columns=['eid',\n",
    "#                                      'call_number',\n",
    "#                                      'report_date', \n",
    "#                                      'offense_date', \n",
    "#                                      'start_date', \n",
    "#                                      'cdts'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "5084dbd6",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "         priority offense_time calltype_code                 call_type  \\\n",
      "0               2     02:49:02          1066         SUSPICIOUS PERSON   \n",
      "1               2     03:05:51           415               DISTURBANCE   \n",
      "2               2     04:35:25        911UNK         UNK TYPE 911 CALL   \n",
      "3               2     03:38:14        SUSCIR  SUSPICIOUS CIRCUMSTANCES   \n",
      "4               4     23:25:49         11300                 NARCOTICS   \n",
      "...           ...          ...           ...                       ...   \n",
      "3998458         6     14:08:34          1195              VEHICLE STOP   \n",
      "3998459         5     14:06:06         22500         PARKING VIOLATION   \n",
      "3998460         2     13:14:37           242                   BATTERY   \n",
      "3998461         6     13:53:41          1195              VEHICLE STOP   \n",
      "3998462         6     14:32:08          1195              VEHICLE STOP   \n",
      "\n",
      "        final_dispo_code                                   final_dispo  \\\n",
      "0                      N      No report required; dispatch record only   \n",
      "1                      N      No report required; dispatch record only   \n",
      "2                    CAN                                      Canceled   \n",
      "3                      N      No report required; dispatch record only   \n",
      "4                      A                                   Arrest Made   \n",
      "...                  ...                                           ...   \n",
      "3998458                N      No report required; dispatch record only   \n",
      "3998459                N      No report required; dispatch record only   \n",
      "3998460                R                                  Report taken   \n",
      "3998461                N      No report required; dispatch record only   \n",
      "3998462                D  Traffic Citation Issued, Hazardous Violation   \n",
      "\n",
      "                             address           Timestamp  \n",
      "0             [800]-[900] JEANNE AV  2022-01-01 04:29:16  \n",
      "1        [1500]-[1600] SOUTHWEST EX  2022-01-01 04:30:36  \n",
      "2         [4800]-[4900] WESTMONT AV  2022-01-01 04:38:27  \n",
      "3         [3600]-[3700] GREENLEE DR  2022-01-01 04:40:26  \n",
      "4        E CAPITOL EX & EVERGREEN PL 2022-01-02 05:25:01  \n",
      "...                              ...                 ...  \n",
      "3998458    [5700]-[5800] ALMADEN RD  2024-02-28 14:34:21  \n",
      "3998459     [100]-[200] MCCREERY AV  2024-02-28 14:34:23  \n",
      "3998460       [0]-[100] N MARKET ST  2024-02-28 14:37:55  \n",
      "3998461        STORY RD & HOPKINS DR 2024-02-28 14:40:42  \n",
      "3998462                          NaN 2024-02-28 14:41:48  \n",
      "\n",
      "[3998463 rows x 8 columns]\n"
     ]
    }
   ],
   "source": [
    "print(pp_calls)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "3f086ed9",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'N': 'No report required; dispatch record only', 'CAN': 'Canceled', 'A': 'Arrest Made', 'GD': 'No Disposition', 'T': 'Turned over To (TOT)', 'NR': 'No Response', 'G': 'Gone on Arrival/unable to locate', 'R': 'Report taken', 'DUPNCAN': 'No Disposition', 'D': 'Traffic Citation Issued, Hazardous Violation', 'U': 'Unfounded event', 'H': 'Courtesy Service/Citizen or agency assist', 'E': 'Traffic Citation Issued, Non-Hazardous Violation', 'O': 'Supplemental report taken', 'B': 'Arrest by Warrant', 'ADV': 'No Disposition', 'F': 'Field Interview (F.I.) Completed', 'C': 'Criminal Citation', 'P': 'Prior case, follow-up activity only', 'M': 'Stranded motorist assist', 'Z': 'No Disposition', 'TOT': 'No Disposition'}\n"
     ]
    }
   ],
   "source": [
    "dispo_mapping = {}\n",
    "\n",
    "for index, row in pp_calls.iterrows():\n",
    "    dispo_code = row['final_dispo_code']\n",
    "    dispo = row['final_dispo']\n",
    "    dispo_mapping[dispo_code] = dispo\n",
    "\n",
    "print(dispo_mapping)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f3865abe",
   "metadata": {},
   "source": [
    "'N': 'No report required; dispatch record only'\n",
    "\n",
    "'CAN': 'Canceled'\n",
    "\n",
    "'A': 'Arrest Made'\n",
    "\n",
    "'GD': 'No Disposition'\n",
    "\n",
    "'T': 'Turned over To (TOT)'\n",
    "\n",
    "'NR': 'No Response'\n",
    "\n",
    "'G': 'Gone on Arrival/unable to locate'\n",
    "\n",
    "'R': 'Report taken'\n",
    "\n",
    "'DUPNCAN': 'No Disposition'\n",
    "\n",
    "'D': 'Traffic Citation Issued, Hazardous Violation'\n",
    "\n",
    "'U': 'Unfounded event'\n",
    "\n",
    "'H': 'Courtesy Service/Citizen or agency assist'\n",
    "\n",
    "'E': 'Traffic Citation Issued, Non-Hazardous Violation'\n",
    "\n",
    "'O': 'Supplemental report taken'\n",
    "\n",
    "'B': 'Arrest by Warrant'\n",
    "\n",
    "'ADV': 'No Disposition'\n",
    "\n",
    "'F': 'Field Interview (F.I.) Completed'\n",
    "\n",
    "'C': 'Criminal Citation'\n",
    "\n",
    "'P': 'Prior case, follow-up activity only'\n",
    "\n",
    "'M': 'Stranded motorist assist'\n",
    "\n",
    "'Z': 'No Disposition'\n",
    "\n",
    "'TOT': 'No Disposition'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "09555c56",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "pp_calls = pp_calls.drop(columns=['final_dispo'])\n",
    "# pp_calls_22 = pp_calls_22.drop(columns=['final_dispo'])\n",
    "# pp_calls_23 = pp_calls_23.drop(columns=['final_dispo'])\n",
    "# pp_calls_24 = pp_calls_24.drop(columns=['final_dispo'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "f1900594",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "212\n",
      "{'1066': 'SUSPICIOUS PERSON', '415': 'DISTURBANCE', '911UNK': 'UNK TYPE 911 CALL', 'SUSCIR': 'SUSPICIOUS CIRCUMSTANCES', '11300': 'NARCOTICS', '415FC': 'DISTURBANCE, FIRECRACKERS', '1057': 'FIREARMS DISCHARGED', '1095': 'PEDESTRIAN STOP', 'WELCKEMS': 'WELFARE CHECK (COMBINED EVENT)', 'AID': 'PUBLIC SAFETY ASSISTANCE', '1033A': 'ALARM, AUDIBLE', '415M': 'DISTURBANCE, MUSIC', '1070': 'PROWLER', 'WELCK': 'WELFARE CHECK', '1033S': 'ALARM, SILENT', '23103': 'RECKLESS DRIVING', '1066W': 'SUSPICIOUS PERSON W/ WEAPON', '5150': 'MENTALLY DISTURBED PERSON', '242': 'BATTERY', '417': 'BRANDISHING A WEAPON', '22500': 'PARKING VIOLATION', '1195': 'VEHICLE STOP', '1154': 'SUSPICIOUS VEHICLE', '415F': 'DISTURBANCE, FAMILY', '20002': 'MISDEMEANOR HIT AND RUN', '484': 'THEFT', '415A': 'DISTURBANCE, FIGHT', '10851': 'STOLEN VEHICLE', '1182': 'VEHICLE ACCIDENT, PROPERTY DAMAGE', '10851R': 'RECOVERED STOLEN VEHICLE', '12025': 'CARRYING A CONCEALED WEAPON', 'FNDPRP': 'FOUND PROPERTY', '602PC': 'TRESPASSING', '1195X': 'VEHICLE STOP ON FEMALE', '1125': 'TRAFFIC HAZARD', '1062': 'MEET THE CITIZEN', '415UNK': 'DISTURBANCE, UNKNOWN', '211A': 'ARMED ROBBERY', '23152': 'MISDEMEANOR DUI', '487': 'GRAND THEFT', '1065JX': 'MISSING FEMALE JUVENILE', '1055': 'CORONERS CASE', '594': 'MALICIOUS MISCHIEF', '6F': 'FELONY WANT', '1181': 'VEHICLE ACCIDENT, MINOR INJURIES', '459VEH': 'VEHICLE BURGLARY', '415N': 'DISTURBANCE, NEIGHBOR', '314': 'INDECENT EXPOSURE', '1183': 'VEHICLE ACCIDENT, UNKNOWN INJURIES', '459': 'BURGLARY (460)', 'FDAID': 'FIRE DEPARTMENT REQUEST FOR PD', '5150X': 'MENTALLY DISTURBED FEMALE', '6M': 'MISDEMEANOR WANT', '422': 'CRIMINAL THREATS', '415W': 'DISTURBANCE, WEAPON', '211SA': 'STRONG ARM ROBBERY', '12020': 'ILLEGAL WEAPONS', '245': 'ASSAULT WITH DEADLY WEAPON', 'DVRO': 'VIOLATION OF PROTECTIVE ORDER', '1124': 'ABANDONED VEHICLE', '1065': 'MISSING PERSON', '1180': 'VEHICLE ACCIDENT, MAJOR INJURIES', '1034': 'OPEN DOOR', '1091': 'ANIMAL COMPLAINT', '459R': 'BURGLARY  REPORT  (460)', '1033': 'ALARM', '1091AEMS': '1091AB          - VICIOUS ANIMAL (COMBINED EVENT)', '11550': 'USE OF CONTROLLED SUBSTANCE', '415MC': 'DISTURBANCE, MOTORCYCLE', '1179': 'VEHICLE ACCIDENT, AMB DISPATCHED', '1066P': 'SUSPICIOUS PACKAGE', '245EMS': 'ASSAULT WITH DEADLY WEAPON (COMBINED EVENT)', '14601': 'DRIVING W/SUSPENDED LICENSE', '415AEMS': 'DISTURBANCE, FIGHT (COMBINED EVENT)', '246': 'SHOOTING INTO OCCP VEH OR DWELLING', '242EMS': 'BATTERY (COMBINED EVENT)', '1065MH': 'MISSING PERSON, MENTAL HANDICAP', '415EMS': 'DISTURBANCE (COMBINED EVENT)', '594G': 'MALICIOUS MISCHIEF, GANG RELATED', '1091D': 'DEAD ANIMAL', '211': 'ROBBERY', '1091A': 'VICIOUS ANIMAL', '1065J': 'MISSING JUVENILE', '1065F': 'FOUND, MISSING PERSON', 'CIVIL': 'CIVIL MATTER', '1080': 'EXPLOSION', '451': 'ARSON (447A)', '20001': 'FELONY HIT AND RUN', '148G': 'RESISTING ARREST, GANG RELATED', '4000A': 'EXPIRED REGISTRATION', '10852': 'TAMPERING WITH A VEHICLE', 'THEFT': 'THEFT', '415FEMS': 'DISTURBANCE, FAMILY (COMBINED EVENT)', '904': 'FIRE (SPECIFY TYPE)', '484BOL': 'PETTY THEFT (BROADCAST ONLY)', '148': 'RESISTING ARREST', '368': 'ELDER/DEPENDENT ADULT ABUSE', '415UNKEMS': 'DISTURBANCE, UNKNOWN (COMBINED EVENT)', '243': 'BATTERY ON AN OFFICER', '647F': 'DRUNK IN PUBLIC', '240': 'ASSAULT', '22507': 'HANDICAPPED PARKING VIOLATION', '488': 'PETTY THEFT', '11377G': 'POSSESSION OF NARCOTICS, GANG RELATED', '647': 'VAGRANT', '503': 'EMBEZZLEMENT', '1046': 'SICK PERSON', '1067': 'PERSON CALLING FOR HELP', '1066X': 'SUSPICIOUS FEMALE', '211AEMS': 'ARMED ROBBERY (COMBINED EVENT)', '496': 'RECEIVE/POSSESS STOLEN PROP', '245G': 'ASSAULT WITH DEADLY WEAPON, GANG', '597': 'CRUELTY TO ANIMALS', 'SUSCIREMS': 'SUSPICIOUS CIRCUMSTANCES (COMBINED EVENT)', '602N': 'REFUSAL TO LEAVE PROPERTY', '1067X': 'FEMALE CALLING FOR HELP', '1184': 'TRAFFIC CONTROL', '1058': 'GARBAGE COMPLAINT', '415J': 'DISTURBANCE, JUVENILE', '23110': 'THROWING SUBSTANCES AT VEHICLE', '273A': 'CHILD ABUSE', '273D': 'CHILD BEATING', '1065X': 'MISSING FEMALE', '476A': 'BAD CHECKS', '647.6': 'MOLEST/ANNOY UNDER 18YRS', '290': 'REGISTRATION OF SEX OFFENDER', '647B': 'SOLICITING FOR PROSTITUTION', '12500': 'UNLICENSED DRIVER', '11377': 'POSSESSION OF NARCOTICS', '23109': 'SPEED CONTEST', '1091C': 'INJURED ANIMAL', '1095X': 'PEDESTRIAN STOP ON FEMALE', 'CPM': 'COMMUNITY POLICING MEETING', '484R': 'THEFT OF RECYCLABLES', '241': 'ASSAULT ON AN OFFICER', '647C': 'OBSTRUCT STREETS OR SIDEWALK', '646.9': 'STALKING', 'CODE6F': 'FELONY WANT', '653M': 'OBSCENE OR HARASSING PH CALLS', 'CODE6M': 'MISDEMEANOR WANT', '211PS': 'PURSE SNATCH ROBBERY', '3056': 'PAROLE VIOLATION', '12020G': 'ILLEGAL WEAPONS, GANG RELATED', '470': 'FORGERY', '243D': 'BATTERY, SERIOUS INJURY', 'CODE10': 'BOMB THREAT', '537': 'DEFRAUDING AN INKEEPER', '415WEMS': 'DISTURBANCE, WEAPON (COMBINED EVENT)', 'CPB': 'COMMUNIT POLICING BIKE', '1072': 'PERSON STABBED', '11350': 'POSSESSION OF CONTROLLED SUBSTANCE', '1033L': 'ALARM, SVRN', '236': 'FALSE IMPRISONMENT', '11300G': 'NARCOTICS, GANG RELATED', '1071': 'PERSON SHOT', '10851G': 'STOLEN VEHICLE, GANG RELATED', '23153': 'FELONY DUI', 'CPS': 'COMMUNITY POLICING SCHOOL', '1045': 'INJURED PERSON', '242G': 'BATTERY, GANG RELATED', '518': 'EXTORTION', '22350': 'SPEEDING', '422.6': 'HATE CRIMES', '273.5EMS': 'DOMESTIC VIOLENCE  (COMBINED EVENT)', '502': 'HI-TECH CRIMES', '211SAEMS': 'STRONG ARM ROBBERY (COMBINED EVENT)', '11357': 'POSSESSION OF MARIJUANA', '415G': 'DISTURBANCE, GANG', '1066G': 'SUSPICIOUS PERSON (GANG)', '25662': 'MINOR IN POSSESSION OF ALCOHOL', '207': 'KIDNAPPING', 'BURG': 'BURGLARY (460)', '602.11': 'OBSTRUCT MEDICAL CLINIC-PERSON', '187': 'MURDER', '11350G': 'POSSESSION OF CONTROLLED SUBSTANCE, GANG RELATED', '1035': 'OPEN WINDOW', 'CCW': 'CARRYING A CONCEALED WEAPON', '415AG': 'DISTURBANCE, FIGHT GANG', '1033B': 'BREACH OF AOA', '1032': 'DROWNING', 'ADW': 'ASSAULT WITH DEADLY WEAPON', '211G': 'ROBBERY, GANG RELATED', '1050': 'TAKE A REPORT', '1051': 'INTOXICATED PERSON', '1196': 'VEHICLE STOP, SEND FILL', 'CPC': 'COMMUNITY POLICING -CITZ ASSIST', '243.2': 'BATTERY ON A PEACE OFFICER', '1096': 'PEDESTRIAN STOP, SEND FILL', '601T': 'TRUANT (TABS)', '602J': 'INJURE, OBSTRUCT A BUSINESS', 'BOMB': 'BOMB REMOVAL, STANDBY', '417G': 'BRANDISHING A WEAPON, GANG', '647A': 'SOLICITING FOR LEWD CONDUCT', '240/242': 'ASSAULT AND BATTERY', '25658.A': 'SELL OR FURN ALCOHOL TO MINOR', '215EMS': 'CARJACKING (COMBINED EVENT)', '211AG': 'ARMED ROBBERY, GANG RELATED', '484G': 'THEFT, GANG RELATED', 'CPO': 'COMMUNITY POLICING OFFICE', '1091H': 'STRAY HORSE', '300': 'W&I-UNDER JURIS OF JUV COURT', '1066R': 'SUSPICIOUS PERSON ON RUNWAY - SJIA', '1196X': 'VEHICLE STOP ON X, SEND FILL', '601': 'W&I UNCONTROLLABLE JUVENILE', '666': 'PETTY THEFT PRIOR CONVICTION', '1091B': 'NOISY ANIMAL', '415JG': 'DISTURBANCE, JUVENILE GANG', '10751': 'ALTERED VIN NUMBER', '240242': 'ASSAULT AND BATTERY', '308': 'SALE OF TOBACCO TO MINOR', 'BARCK': 'BAR CHECK', 'CURF': 'CURFEW TRANS/CURF CTR TO RESD'}\n"
     ]
    }
   ],
   "source": [
    "calltype_mapping = {}\n",
    "\n",
    "for index, row in pp_calls.iterrows():\n",
    "    calltype_code = row['calltype_code']\n",
    "    calltype = row['call_type']\n",
    "    calltype_mapping[calltype_code] = calltype\n",
    "\n",
    "print(len(calltype_mapping))\n",
    "print(calltype_mapping)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "3ce7d9b8",
   "metadata": {},
   "outputs": [],
   "source": [
    "pp_calls = pp_calls.drop(columns=['call_type'])\n",
    "# pp_calls_22 = pp_calls_22.drop(columns=['call_type'])\n",
    "# pp_calls_23 = pp_calls_23.drop(columns=['call_type'])\n",
    "# pp_calls_24 = pp_calls_24.drop(columns=['call_type'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "4e9f03fa",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([2, 4, 6, 3, 1, 5])"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pp_calls['priority'].unique()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "46cb54a9",
   "metadata": {},
   "source": [
    "# Analysis of Police Call Data"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7f9ec1fa",
   "metadata": {},
   "source": [
    "### number of calls per day"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "616bfefe",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "date_column = pp_calls['Timestamp'].dt.date\n",
    "\n",
    "daily_count = pp_calls.groupby(pd.Grouper(key='Timestamp', freq='D')).size()\n",
    "monthly_count = daily_count.groupby(daily_count.index.to_period('M')).sum()\n",
    "\n",
    "monthly_count.plot(kind='bar', figsize=(10, 6))\n",
    "\n",
    "\n",
    "plt.title('Daily Data Count')\n",
    "plt.xlabel('Month')\n",
    "plt.ylabel('Count')\n",
    "plt.grid(True)\n",
    "\n",
    "plt.xticks(range(len(monthly_count.index)), monthly_count.index.strftime('%Y-%m'))\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "5ea1d6bf",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# 'CALL_TYPE' 열의 빈도수 계산\n",
    "call_type_counts = pp_calls['calltype_code'].value_counts()\n",
    "\n",
    "# 히스토그램 그리기\n",
    "plt.figure(figsize=(10, 6))  # 그래프 크기 설정\n",
    "call_type_counts.plot(kind='bar', color='skyblue')  # 막대 그래프 생성\n",
    "plt.title('Call Type Histogram')  # 그래프 제목 설정\n",
    "plt.xlabel('Call Type')  # x축 레이블 설정\n",
    "plt.ylabel('Frequency')  # y축 레이블 설정\n",
    "plt.xticks(rotation=45)  # x축 레이블 회전\n",
    "plt.grid(axis='y', linestyle='--', alpha=0.7)  # 그리드 설정\n",
    "plt.tight_layout()  # 그래프 레이아웃 조정\n",
    "plt.xticks([]) \n",
    "plt.show()  # 그래프 출력"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "6a804cf7",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# 상위 N개의 'CALL_TYPE' 빈도수 계산\n",
    "top_n_call_types = calls_22['CALL_TYPE'].value_counts().nlargest(10)\n",
    "\n",
    "# 히스토그램 그리기\n",
    "plt.figure(figsize=(10, 6))\n",
    "top_n_call_types.plot(kind='bar', color='skyblue')\n",
    "plt.title('Top 10 Call Types Histogram')\n",
    "plt.xlabel('Call Type')\n",
    "plt.ylabel('Frequency')\n",
    "plt.xticks(rotation=45)\n",
    "plt.grid(axis='y', linestyle='--', alpha=0.7)\n",
    "plt.tight_layout()\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "870f8707",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python (py3_8_10)",
   "language": "python",
   "name": "py3_8_10"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.10"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}