{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 100,
   "id": "78e785f1",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: kaggle in ./jupyter_env/lib/python3.10/site-packages (1.5.13)\n",
      "Requirement already satisfied: requests in ./jupyter_env/lib/python3.10/site-packages (from kaggle) (2.28.2)\n",
      "Requirement already satisfied: six>=1.10 in ./jupyter_env/lib/python3.10/site-packages (from kaggle) (1.16.0)\n",
      "Requirement already satisfied: tqdm in ./jupyter_env/lib/python3.10/site-packages (from kaggle) (4.65.0)\n",
      "Requirement already satisfied: urllib3 in ./jupyter_env/lib/python3.10/site-packages (from kaggle) (1.26.15)\n",
      "Requirement already satisfied: certifi in ./jupyter_env/lib/python3.10/site-packages (from kaggle) (2022.12.7)\n",
      "Requirement already satisfied: python-slugify in ./jupyter_env/lib/python3.10/site-packages (from kaggle) (8.0.1)\n",
      "Requirement already satisfied: python-dateutil in ./jupyter_env/lib/python3.10/site-packages (from kaggle) (2.8.2)\n",
      "Requirement already satisfied: text-unidecode>=1.3 in ./jupyter_env/lib/python3.10/site-packages (from python-slugify->kaggle) (1.3)\n",
      "Requirement already satisfied: idna<4,>=2.5 in ./jupyter_env/lib/python3.10/site-packages (from requests->kaggle) (3.4)\n",
      "Requirement already satisfied: charset-normalizer<4,>=2 in ./jupyter_env/lib/python3.10/site-packages (from requests->kaggle) (3.1.0)\n",
      "Requirement already satisfied: pandas in ./jupyter_env/lib/python3.10/site-packages (1.5.3)\n",
      "Requirement already satisfied: numpy>=1.21.0 in ./jupyter_env/lib/python3.10/site-packages (from pandas) (1.24.2)\n",
      "Requirement already satisfied: python-dateutil>=2.8.1 in ./jupyter_env/lib/python3.10/site-packages (from pandas) (2.8.2)\n",
      "Requirement already satisfied: pytz>=2020.1 in ./jupyter_env/lib/python3.10/site-packages (from pandas) (2022.7.1)\n",
      "Requirement already satisfied: six>=1.5 in ./jupyter_env/lib/python3.10/site-packages (from python-dateutil>=2.8.1->pandas) (1.16.0)\n",
      "Requirement already satisfied: unzip in ./jupyter_env/lib/python3.10/site-packages (1.0.0)\n",
      "Requirement already satisfied: scikit-learn in ./jupyter_env/lib/python3.10/site-packages (1.2.2)\n",
      "Requirement already satisfied: threadpoolctl>=2.0.0 in ./jupyter_env/lib/python3.10/site-packages (from scikit-learn) (3.1.0)\n",
      "Requirement already satisfied: numpy>=1.17.3 in ./jupyter_env/lib/python3.10/site-packages (from scikit-learn) (1.24.2)\n",
      "Requirement already satisfied: joblib>=1.1.1 in ./jupyter_env/lib/python3.10/site-packages (from scikit-learn) (1.2.0)\n",
      "Requirement already satisfied: scipy>=1.3.2 in ./jupyter_env/lib/python3.10/site-packages (from scikit-learn) (1.10.1)\n",
      "Requirement already satisfied: seaborn in ./jupyter_env/lib/python3.10/site-packages (0.12.2)\n",
      "Requirement already satisfied: numpy!=1.24.0,>=1.17 in ./jupyter_env/lib/python3.10/site-packages (from seaborn) (1.24.2)\n",
      "Requirement already satisfied: pandas>=0.25 in ./jupyter_env/lib/python3.10/site-packages (from seaborn) (1.5.3)\n",
      "Requirement already satisfied: matplotlib!=3.6.1,>=3.1 in ./jupyter_env/lib/python3.10/site-packages (from seaborn) (3.7.1)\n",
      "Requirement already satisfied: pillow>=6.2.0 in ./jupyter_env/lib/python3.10/site-packages (from matplotlib!=3.6.1,>=3.1->seaborn) (9.4.0)\n",
      "Requirement already satisfied: fonttools>=4.22.0 in ./jupyter_env/lib/python3.10/site-packages (from matplotlib!=3.6.1,>=3.1->seaborn) (4.39.2)\n",
      "Requirement already satisfied: pyparsing>=2.3.1 in ./jupyter_env/lib/python3.10/site-packages (from matplotlib!=3.6.1,>=3.1->seaborn) (3.0.9)\n",
      "Requirement already satisfied: contourpy>=1.0.1 in ./jupyter_env/lib/python3.10/site-packages (from matplotlib!=3.6.1,>=3.1->seaborn) (1.0.7)\n",
      "Requirement already satisfied: cycler>=0.10 in ./jupyter_env/lib/python3.10/site-packages (from matplotlib!=3.6.1,>=3.1->seaborn) (0.11.0)\n",
      "Requirement already satisfied: kiwisolver>=1.0.1 in ./jupyter_env/lib/python3.10/site-packages (from matplotlib!=3.6.1,>=3.1->seaborn) (1.4.4)\n",
      "Requirement already satisfied: packaging>=20.0 in ./jupyter_env/lib/python3.10/site-packages (from matplotlib!=3.6.1,>=3.1->seaborn) (23.0)\n",
      "Requirement already satisfied: python-dateutil>=2.7 in ./jupyter_env/lib/python3.10/site-packages (from matplotlib!=3.6.1,>=3.1->seaborn) (2.8.2)\n",
      "Requirement already satisfied: pytz>=2020.1 in ./jupyter_env/lib/python3.10/site-packages (from pandas>=0.25->seaborn) (2022.7.1)\n",
      "Requirement already satisfied: six>=1.5 in ./jupyter_env/lib/python3.10/site-packages (from python-dateutil>=2.7->matplotlib!=3.6.1,>=3.1->seaborn) (1.16.0)\n"
     ]
    }
   ],
   "source": [
    "#instalacja pakietow\n",
    "!pip install kaggle\n",
    "!pip install pandas\n",
    "!pip install unzip\n",
    "!pip install scikit-learn\n",
    "!pip install seaborn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "id": "d8fffef2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Warning: Your Kaggle API key is readable by other users on this system! To fix this, you can run 'chmod 600 /home/user/.kaggle/kaggle.json'\n",
      "crime-in-baltimore.zip: Skipping, found more recently modified local copy (use --force to force download)\n"
     ]
    }
   ],
   "source": [
    "#Pobranie zbioru\n",
    "!kaggle datasets download -d sohier/crime-in-baltimore"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "id": "febfcbd4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Archive:  crime-in-baltimore.zip\n",
      "  inflating: BPD_Part_1_Victim_Based_Crime_Data.csv  \n"
     ]
    }
   ],
   "source": [
    "!unzip -o crime-in-baltimore.zip"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "id": "11bc16fe",
   "metadata": {},
   "outputs": [],
   "source": [
    "! grep -P \"^$\" -n BPD_Part_1_Victim_Based_Crime_Data.csv"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "id": "cb85e933",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "id": "20e6099e",
   "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>CrimeDate</th>\n",
       "      <th>CrimeTime</th>\n",
       "      <th>CrimeCode</th>\n",
       "      <th>Location</th>\n",
       "      <th>Description</th>\n",
       "      <th>Inside/Outside</th>\n",
       "      <th>Weapon</th>\n",
       "      <th>Post</th>\n",
       "      <th>District</th>\n",
       "      <th>Neighborhood</th>\n",
       "      <th>Longitude</th>\n",
       "      <th>Latitude</th>\n",
       "      <th>Location 1</th>\n",
       "      <th>Premise</th>\n",
       "      <th>Total Incidents</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>09/02/2017</td>\n",
       "      <td>23:30:00</td>\n",
       "      <td>3JK</td>\n",
       "      <td>4200 AUDREY AVE</td>\n",
       "      <td>ROBBERY - RESIDENCE</td>\n",
       "      <td>I</td>\n",
       "      <td>KNIFE</td>\n",
       "      <td>913.0</td>\n",
       "      <td>SOUTHERN</td>\n",
       "      <td>Brooklyn</td>\n",
       "      <td>-76.60541</td>\n",
       "      <td>39.22951</td>\n",
       "      <td>(39.2295100000, -76.6054100000)</td>\n",
       "      <td>ROW/TOWNHO</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>09/02/2017</td>\n",
       "      <td>23:00:00</td>\n",
       "      <td>7A</td>\n",
       "      <td>800 NEWINGTON AVE</td>\n",
       "      <td>AUTO THEFT</td>\n",
       "      <td>O</td>\n",
       "      <td>NaN</td>\n",
       "      <td>133.0</td>\n",
       "      <td>CENTRAL</td>\n",
       "      <td>Reservoir Hill</td>\n",
       "      <td>-76.63217</td>\n",
       "      <td>39.31360</td>\n",
       "      <td>(39.3136000000, -76.6321700000)</td>\n",
       "      <td>STREET</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>09/02/2017</td>\n",
       "      <td>22:53:00</td>\n",
       "      <td>9S</td>\n",
       "      <td>600 RADNOR AV</td>\n",
       "      <td>SHOOTING</td>\n",
       "      <td>Outside</td>\n",
       "      <td>FIREARM</td>\n",
       "      <td>524.0</td>\n",
       "      <td>NORTHERN</td>\n",
       "      <td>Winston-Govans</td>\n",
       "      <td>-76.60697</td>\n",
       "      <td>39.34768</td>\n",
       "      <td>(39.3476800000, -76.6069700000)</td>\n",
       "      <td>Street</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>09/02/2017</td>\n",
       "      <td>22:50:00</td>\n",
       "      <td>4C</td>\n",
       "      <td>1800 RAMSAY ST</td>\n",
       "      <td>AGG. ASSAULT</td>\n",
       "      <td>I</td>\n",
       "      <td>OTHER</td>\n",
       "      <td>934.0</td>\n",
       "      <td>SOUTHERN</td>\n",
       "      <td>Carrollton Ridge</td>\n",
       "      <td>-76.64526</td>\n",
       "      <td>39.28315</td>\n",
       "      <td>(39.2831500000, -76.6452600000)</td>\n",
       "      <td>ROW/TOWNHO</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>09/02/2017</td>\n",
       "      <td>22:31:00</td>\n",
       "      <td>4E</td>\n",
       "      <td>100 LIGHT ST</td>\n",
       "      <td>COMMON ASSAULT</td>\n",
       "      <td>O</td>\n",
       "      <td>HANDS</td>\n",
       "      <td>113.0</td>\n",
       "      <td>CENTRAL</td>\n",
       "      <td>Downtown West</td>\n",
       "      <td>-76.61365</td>\n",
       "      <td>39.28756</td>\n",
       "      <td>(39.2875600000, -76.6136500000)</td>\n",
       "      <td>STREET</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>276524</th>\n",
       "      <td>01/01/2012</td>\n",
       "      <td>00:00:00</td>\n",
       "      <td>6J</td>\n",
       "      <td>1400 JOH AVE</td>\n",
       "      <td>LARCENY</td>\n",
       "      <td>I</td>\n",
       "      <td>NaN</td>\n",
       "      <td>832.0</td>\n",
       "      <td>SOUTHWESTERN</td>\n",
       "      <td>Violetville</td>\n",
       "      <td>-76.67195</td>\n",
       "      <td>39.26132</td>\n",
       "      <td>(39.2613200000, -76.6719500000)</td>\n",
       "      <td>OTHER - IN</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>276525</th>\n",
       "      <td>01/01/2012</td>\n",
       "      <td>00:00:00</td>\n",
       "      <td>6J</td>\n",
       "      <td>5500 SINCLAIR LN</td>\n",
       "      <td>LARCENY</td>\n",
       "      <td>O</td>\n",
       "      <td>NaN</td>\n",
       "      <td>444.0</td>\n",
       "      <td>NORTHEASTERN</td>\n",
       "      <td>Frankford</td>\n",
       "      <td>-76.53829</td>\n",
       "      <td>39.32493</td>\n",
       "      <td>(39.3249300000, -76.5382900000)</td>\n",
       "      <td>OTHER - OU</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>276526</th>\n",
       "      <td>01/01/2012</td>\n",
       "      <td>00:00:00</td>\n",
       "      <td>6E</td>\n",
       "      <td>400 N PATTERSON PK AV</td>\n",
       "      <td>LARCENY</td>\n",
       "      <td>O</td>\n",
       "      <td>NaN</td>\n",
       "      <td>321.0</td>\n",
       "      <td>EASTERN</td>\n",
       "      <td>CARE</td>\n",
       "      <td>-76.58497</td>\n",
       "      <td>39.29573</td>\n",
       "      <td>(39.2957300000, -76.5849700000)</td>\n",
       "      <td>STREET</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>276527</th>\n",
       "      <td>01/01/2012</td>\n",
       "      <td>00:00:00</td>\n",
       "      <td>5A</td>\n",
       "      <td>5800 LILLYAN AV</td>\n",
       "      <td>BURGLARY</td>\n",
       "      <td>I</td>\n",
       "      <td>NaN</td>\n",
       "      <td>425.0</td>\n",
       "      <td>NORTHEASTERN</td>\n",
       "      <td>Glenham-Belhar</td>\n",
       "      <td>-76.54578</td>\n",
       "      <td>39.34701</td>\n",
       "      <td>(39.3470100000, -76.5457800000)</td>\n",
       "      <td>APT. LOCKE</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>276528</th>\n",
       "      <td>01/01/2012</td>\n",
       "      <td>00:00:00</td>\n",
       "      <td>5A</td>\n",
       "      <td>1900 GRINNALDS AV</td>\n",
       "      <td>BURGLARY</td>\n",
       "      <td>I</td>\n",
       "      <td>NaN</td>\n",
       "      <td>831.0</td>\n",
       "      <td>SOUTHWESTERN</td>\n",
       "      <td>Morrell Park</td>\n",
       "      <td>-76.65094</td>\n",
       "      <td>39.26698</td>\n",
       "      <td>(39.2669800000, -76.6509400000)</td>\n",
       "      <td>ROW/TOWNHO</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>276529 rows × 15 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         CrimeDate CrimeTime CrimeCode               Location  \\\n",
       "0       09/02/2017  23:30:00       3JK        4200 AUDREY AVE   \n",
       "1       09/02/2017  23:00:00        7A      800 NEWINGTON AVE   \n",
       "2       09/02/2017  22:53:00        9S          600 RADNOR AV   \n",
       "3       09/02/2017  22:50:00        4C         1800 RAMSAY ST   \n",
       "4       09/02/2017  22:31:00        4E           100 LIGHT ST   \n",
       "...            ...       ...       ...                    ...   \n",
       "276524  01/01/2012  00:00:00        6J           1400 JOH AVE   \n",
       "276525  01/01/2012  00:00:00        6J       5500 SINCLAIR LN   \n",
       "276526  01/01/2012  00:00:00        6E  400 N PATTERSON PK AV   \n",
       "276527  01/01/2012  00:00:00        5A        5800 LILLYAN AV   \n",
       "276528  01/01/2012  00:00:00        5A      1900 GRINNALDS AV   \n",
       "\n",
       "                Description Inside/Outside   Weapon   Post      District  \\\n",
       "0       ROBBERY - RESIDENCE              I    KNIFE  913.0      SOUTHERN   \n",
       "1                AUTO THEFT              O      NaN  133.0       CENTRAL   \n",
       "2                  SHOOTING        Outside  FIREARM  524.0      NORTHERN   \n",
       "3              AGG. ASSAULT              I    OTHER  934.0      SOUTHERN   \n",
       "4            COMMON ASSAULT              O    HANDS  113.0       CENTRAL   \n",
       "...                     ...            ...      ...    ...           ...   \n",
       "276524              LARCENY              I      NaN  832.0  SOUTHWESTERN   \n",
       "276525              LARCENY              O      NaN  444.0  NORTHEASTERN   \n",
       "276526              LARCENY              O      NaN  321.0       EASTERN   \n",
       "276527             BURGLARY              I      NaN  425.0  NORTHEASTERN   \n",
       "276528             BURGLARY              I      NaN  831.0  SOUTHWESTERN   \n",
       "\n",
       "            Neighborhood  Longitude  Latitude  \\\n",
       "0               Brooklyn  -76.60541  39.22951   \n",
       "1         Reservoir Hill  -76.63217  39.31360   \n",
       "2         Winston-Govans  -76.60697  39.34768   \n",
       "3       Carrollton Ridge  -76.64526  39.28315   \n",
       "4          Downtown West  -76.61365  39.28756   \n",
       "...                  ...        ...       ...   \n",
       "276524       Violetville  -76.67195  39.26132   \n",
       "276525         Frankford  -76.53829  39.32493   \n",
       "276526              CARE  -76.58497  39.29573   \n",
       "276527    Glenham-Belhar  -76.54578  39.34701   \n",
       "276528      Morrell Park  -76.65094  39.26698   \n",
       "\n",
       "                             Location 1     Premise  Total Incidents  \n",
       "0       (39.2295100000, -76.6054100000)  ROW/TOWNHO                1  \n",
       "1       (39.3136000000, -76.6321700000)      STREET                1  \n",
       "2       (39.3476800000, -76.6069700000)      Street                1  \n",
       "3       (39.2831500000, -76.6452600000)  ROW/TOWNHO                1  \n",
       "4       (39.2875600000, -76.6136500000)      STREET                1  \n",
       "...                                 ...         ...              ...  \n",
       "276524  (39.2613200000, -76.6719500000)  OTHER - IN                1  \n",
       "276525  (39.3249300000, -76.5382900000)  OTHER - OU                1  \n",
       "276526  (39.2957300000, -76.5849700000)      STREET                1  \n",
       "276527  (39.3470100000, -76.5457800000)  APT. LOCKE                1  \n",
       "276528  (39.2669800000, -76.6509400000)  ROW/TOWNHO                1  \n",
       "\n",
       "[276529 rows x 15 columns]"
      ]
     },
     "execution_count": 105,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "baltimore=pd.read_csv('BPD_Part_1_Victim_Based_Crime_Data.csv')\n",
    "baltimore"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "id": "89b1028c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "CrimeDate               0\n",
       "CrimeTime               0\n",
       "CrimeCode               0\n",
       "Location             2207\n",
       "Description             0\n",
       "Inside/Outside      10279\n",
       "Weapon             180952\n",
       "Post                  224\n",
       "District               80\n",
       "Neighborhood         2740\n",
       "Longitude            2204\n",
       "Latitude             2204\n",
       "Location 1           2204\n",
       "Premise             10757\n",
       "Total Incidents         0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 106,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "baltimore.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "id": "7109d8f7",
   "metadata": {},
   "outputs": [],
   "source": [
    "# W wiekszosci przestepstw nie uzywa sie broni, zastepujemy\n",
    "# puste pola przez None\n",
    "baltimore[\"Weapon\"].fillna(\"None\", inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "id": "1c67e681",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "CrimeDate              0\n",
       "CrimeTime              0\n",
       "CrimeCode              0\n",
       "Location            2207\n",
       "Description            0\n",
       "Inside/Outside     10279\n",
       "Weapon                 0\n",
       "Post                 224\n",
       "District              80\n",
       "Neighborhood        2740\n",
       "Longitude           2204\n",
       "Latitude            2204\n",
       "Location 1          2204\n",
       "Premise            10757\n",
       "Total Incidents        0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 108,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "baltimore.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "id": "31966b62",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Wyczyszczenie zbioru z artefaktow\n",
    "baltimore.dropna(inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "id": "75f39653",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "CrimeDate          0\n",
       "CrimeTime          0\n",
       "CrimeCode          0\n",
       "Location           0\n",
       "Description        0\n",
       "Inside/Outside     0\n",
       "Weapon             0\n",
       "Post               0\n",
       "District           0\n",
       "Neighborhood       0\n",
       "Longitude          0\n",
       "Latitude           0\n",
       "Location 1         0\n",
       "Premise            0\n",
       "Total Incidents    0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 110,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "baltimore.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "id": "6cd411df",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "id": "8b8b4732",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Normalizacja\n",
    "baltimore['Post'] = baltimore['Post'] /baltimore['Post'].abs().max()\n",
    "baltimore['Location']=baltimore['Location'].str.lower()\n",
    "baltimore['Description']=baltimore['Description'].str.lower()\n",
    "baltimore['Weapon']=baltimore['Weapon'].str.lower()\n",
    "baltimore['Premise']=baltimore['Premise'].str.lower()\n",
    "baltimore['District']=baltimore['District'].str.lower()\n",
    "baltimore['CrimeCode']=baltimore['CrimeCode'].str.lower()\n",
    "baltimore['Neighborhood']=baltimore['Neighborhood'].str.lower()\n",
    "baltimore['Inside/Outside']=baltimore['Inside/Outside'].str.lower()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "id": "d9adbe06",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: >"
      ]
     },
     "execution_count": 113,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "baltimore['District'].value_counts().plot(kind=\"bar\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "id": "24b7582f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x7f9756fab6a0>"
      ]
     },
     "execution_count": 114,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 610.5x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "sns.set_theme()\n",
    "sns.relplot(data=baltimore[:20], x='Longitude', y='Latitude', hue='Weapon')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "id": "c9cf1067",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Podzial na zbiory\n",
    "baltimore_train, baltimore_test = train_test_split(baltimore, test_size=0.1, random_state=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "id": "350e7098",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead 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>CrimeDate</th>\n",
       "      <th>CrimeTime</th>\n",
       "      <th>CrimeCode</th>\n",
       "      <th>Location</th>\n",
       "      <th>Description</th>\n",
       "      <th>Inside/Outside</th>\n",
       "      <th>Weapon</th>\n",
       "      <th>Post</th>\n",
       "      <th>District</th>\n",
       "      <th>Neighborhood</th>\n",
       "      <th>Longitude</th>\n",
       "      <th>Latitude</th>\n",
       "      <th>Location 1</th>\n",
       "      <th>Premise</th>\n",
       "      <th>Total Incidents</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>20700</th>\n",
       "      <td>04/10/2017</td>\n",
       "      <td>22:26:00</td>\n",
       "      <td>4e</td>\n",
       "      <td>4900 eastern av</td>\n",
       "      <td>common assault</td>\n",
       "      <td>o</td>\n",
       "      <td>hands</td>\n",
       "      <td>0.256628</td>\n",
       "      <td>southeastern</td>\n",
       "      <td>greektown</td>\n",
       "      <td>-76.55422</td>\n",
       "      <td>39.28706</td>\n",
       "      <td>(39.2870600000, -76.5542200000)</td>\n",
       "      <td>alley</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>63746</th>\n",
       "      <td>06/05/2016</td>\n",
       "      <td>20:44:00</td>\n",
       "      <td>4e</td>\n",
       "      <td>3000 s hanover st</td>\n",
       "      <td>common assault</td>\n",
       "      <td>o</td>\n",
       "      <td>hands</td>\n",
       "      <td>0.977731</td>\n",
       "      <td>southern</td>\n",
       "      <td>middle branch/reedbird pa</td>\n",
       "      <td>-76.61504</td>\n",
       "      <td>39.25134</td>\n",
       "      <td>(39.2513400000, -76.6150400000)</td>\n",
       "      <td>street</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>169854</th>\n",
       "      <td>03/10/2014</td>\n",
       "      <td>20:00:00</td>\n",
       "      <td>4e</td>\n",
       "      <td>4100 parkside dr</td>\n",
       "      <td>common assault</td>\n",
       "      <td>o</td>\n",
       "      <td>hands</td>\n",
       "      <td>0.447508</td>\n",
       "      <td>northeastern</td>\n",
       "      <td>belair-parkside</td>\n",
       "      <td>-76.56605</td>\n",
       "      <td>39.32783</td>\n",
       "      <td>(39.3278300000, -76.5660500000)</td>\n",
       "      <td>street</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42473</th>\n",
       "      <td>10/31/2016</td>\n",
       "      <td>09:30:00</td>\n",
       "      <td>4e</td>\n",
       "      <td>5600 loch raven blvd</td>\n",
       "      <td>common assault</td>\n",
       "      <td>i</td>\n",
       "      <td>hands</td>\n",
       "      <td>0.440085</td>\n",
       "      <td>northeastern</td>\n",
       "      <td>loch raven</td>\n",
       "      <td>-76.58856</td>\n",
       "      <td>39.35952</td>\n",
       "      <td>(39.3595200000, -76.5885600000)</td>\n",
       "      <td>hotel/mote</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>86103</th>\n",
       "      <td>12/05/2015</td>\n",
       "      <td>08:15:00</td>\n",
       "      <td>4e</td>\n",
       "      <td>1100 guilford ave</td>\n",
       "      <td>common assault</td>\n",
       "      <td>i</td>\n",
       "      <td>hands</td>\n",
       "      <td>0.149523</td>\n",
       "      <td>central</td>\n",
       "      <td>mid-town belvedere</td>\n",
       "      <td>-76.61194</td>\n",
       "      <td>39.30319</td>\n",
       "      <td>(39.3031900000, -76.6119400000)</td>\n",
       "      <td>apt/condo</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>182763</th>\n",
       "      <td>11/20/2013</td>\n",
       "      <td>20:00:00</td>\n",
       "      <td>6d</td>\n",
       "      <td>3800 dolfield av</td>\n",
       "      <td>larceny from auto</td>\n",
       "      <td>o</td>\n",
       "      <td>none</td>\n",
       "      <td>0.681866</td>\n",
       "      <td>northwestern</td>\n",
       "      <td>dolfield</td>\n",
       "      <td>-76.68090</td>\n",
       "      <td>39.33938</td>\n",
       "      <td>(39.3393800000, -76.6809000000)</td>\n",
       "      <td>street</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14972</th>\n",
       "      <td>05/22/2017</td>\n",
       "      <td>03:30:00</td>\n",
       "      <td>4c</td>\n",
       "      <td>3000 w garrison ave</td>\n",
       "      <td>agg. assault</td>\n",
       "      <td>i</td>\n",
       "      <td>other</td>\n",
       "      <td>0.651113</td>\n",
       "      <td>northwestern</td>\n",
       "      <td>central park heights</td>\n",
       "      <td>-76.67146</td>\n",
       "      <td>39.34863</td>\n",
       "      <td>(39.3486300000, -76.6714600000)</td>\n",
       "      <td>row/townho</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44956</th>\n",
       "      <td>10/15/2016</td>\n",
       "      <td>23:30:00</td>\n",
       "      <td>7a</td>\n",
       "      <td>500 jack st</td>\n",
       "      <td>auto theft</td>\n",
       "      <td>o</td>\n",
       "      <td>none</td>\n",
       "      <td>0.968187</td>\n",
       "      <td>southern</td>\n",
       "      <td>brooklyn</td>\n",
       "      <td>-76.60582</td>\n",
       "      <td>39.23265</td>\n",
       "      <td>(39.2326500000, -76.6058200000)</td>\n",
       "      <td>street</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36873</th>\n",
       "      <td>12/08/2016</td>\n",
       "      <td>18:30:00</td>\n",
       "      <td>4e</td>\n",
       "      <td>3800 cedarhurst rd</td>\n",
       "      <td>common assault</td>\n",
       "      <td>o</td>\n",
       "      <td>hands</td>\n",
       "      <td>0.451750</td>\n",
       "      <td>northeastern</td>\n",
       "      <td>waltherson</td>\n",
       "      <td>-76.56315</td>\n",
       "      <td>39.33720</td>\n",
       "      <td>(39.3372000000, -76.5631500000)</td>\n",
       "      <td>street</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>230084</th>\n",
       "      <td>12/06/2012</td>\n",
       "      <td>14:00:00</td>\n",
       "      <td>4e</td>\n",
       "      <td>800 s highland av</td>\n",
       "      <td>common assault</td>\n",
       "      <td>i</td>\n",
       "      <td>hands</td>\n",
       "      <td>0.246023</td>\n",
       "      <td>southeastern</td>\n",
       "      <td>canton</td>\n",
       "      <td>-76.56878</td>\n",
       "      <td>39.28342</td>\n",
       "      <td>(39.2834200000, -76.5687800000)</td>\n",
       "      <td>school</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>26312 rows × 15 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         CrimeDate CrimeTime CrimeCode              Location  \\\n",
       "20700   04/10/2017  22:26:00        4e       4900 eastern av   \n",
       "63746   06/05/2016  20:44:00        4e     3000 s hanover st   \n",
       "169854  03/10/2014  20:00:00        4e      4100 parkside dr   \n",
       "42473   10/31/2016  09:30:00        4e  5600 loch raven blvd   \n",
       "86103   12/05/2015  08:15:00        4e     1100 guilford ave   \n",
       "...            ...       ...       ...                   ...   \n",
       "182763  11/20/2013  20:00:00        6d      3800 dolfield av   \n",
       "14972   05/22/2017  03:30:00        4c   3000 w garrison ave   \n",
       "44956   10/15/2016  23:30:00        7a           500 jack st   \n",
       "36873   12/08/2016  18:30:00        4e    3800 cedarhurst rd   \n",
       "230084  12/06/2012  14:00:00        4e     800 s highland av   \n",
       "\n",
       "              Description Inside/Outside Weapon      Post      District  \\\n",
       "20700      common assault              o  hands  0.256628  southeastern   \n",
       "63746      common assault              o  hands  0.977731      southern   \n",
       "169854     common assault              o  hands  0.447508  northeastern   \n",
       "42473      common assault              i  hands  0.440085  northeastern   \n",
       "86103      common assault              i  hands  0.149523       central   \n",
       "...                   ...            ...    ...       ...           ...   \n",
       "182763  larceny from auto              o   none  0.681866  northwestern   \n",
       "14972        agg. assault              i  other  0.651113  northwestern   \n",
       "44956          auto theft              o   none  0.968187      southern   \n",
       "36873      common assault              o  hands  0.451750  northeastern   \n",
       "230084     common assault              i  hands  0.246023  southeastern   \n",
       "\n",
       "                     Neighborhood  Longitude  Latitude  \\\n",
       "20700                   greektown  -76.55422  39.28706   \n",
       "63746   middle branch/reedbird pa  -76.61504  39.25134   \n",
       "169854            belair-parkside  -76.56605  39.32783   \n",
       "42473                  loch raven  -76.58856  39.35952   \n",
       "86103          mid-town belvedere  -76.61194  39.30319   \n",
       "...                           ...        ...       ...   \n",
       "182763                   dolfield  -76.68090  39.33938   \n",
       "14972        central park heights  -76.67146  39.34863   \n",
       "44956                    brooklyn  -76.60582  39.23265   \n",
       "36873                  waltherson  -76.56315  39.33720   \n",
       "230084                     canton  -76.56878  39.28342   \n",
       "\n",
       "                             Location 1     Premise  Total Incidents  \n",
       "20700   (39.2870600000, -76.5542200000)       alley                1  \n",
       "63746   (39.2513400000, -76.6150400000)      street                1  \n",
       "169854  (39.3278300000, -76.5660500000)      street                1  \n",
       "42473   (39.3595200000, -76.5885600000)  hotel/mote                1  \n",
       "86103   (39.3031900000, -76.6119400000)   apt/condo                1  \n",
       "...                                 ...         ...              ...  \n",
       "182763  (39.3393800000, -76.6809000000)      street                1  \n",
       "14972   (39.3486300000, -76.6714600000)  row/townho                1  \n",
       "44956   (39.2326500000, -76.6058200000)      street                1  \n",
       "36873   (39.3372000000, -76.5631500000)      street                1  \n",
       "230084  (39.2834200000, -76.5687800000)      school                1  \n",
       "\n",
       "[26312 rows x 15 columns]"
      ]
     },
     "execution_count": 116,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "baltimore_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "id": "ed66b750",
   "metadata": {},
   "outputs": [],
   "source": [
    "baltimore_train, baltimore_val= train_test_split(baltimore_train, test_size=0.25, random_state=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "id": "3840c547",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>CrimeDate</th>\n",
       "      <th>CrimeTime</th>\n",
       "      <th>CrimeCode</th>\n",
       "      <th>Location</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>263118</td>\n",
       "      <td>263118</td>\n",
       "      <td>263118</td>\n",
       "      <td>263118</td>\n",
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       "      <td>263118</td>\n",
       "      <td>263118</td>\n",
       "      <td>263118.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>unique</th>\n",
       "      <td>2072</td>\n",
       "      <td>2935</td>\n",
       "      <td>80</td>\n",
       "      <td>25276</td>\n",
       "      <td>15</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>9</td>\n",
       "      <td>278</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>93543</td>\n",
       "      <td>118</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>top</th>\n",
       "      <td>04/27/2015</td>\n",
       "      <td>18:00:00</td>\n",
       "      <td>4e</td>\n",
       "      <td>200 e pratt st</td>\n",
       "      <td>larceny</td>\n",
       "      <td>i</td>\n",
       "      <td>none</td>\n",
       "      <td>NaN</td>\n",
       "      <td>northeastern</td>\n",
       "      <td>downtown</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>(39.3180000000, -76.6582100000)</td>\n",
       "      <td>street</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>freq</th>\n",
       "      <td>407</td>\n",
       "      <td>6483</td>\n",
       "      <td>43093</td>\n",
       "      <td>632</td>\n",
       "      <td>58246</td>\n",
       "      <td>131015</td>\n",
       "      <td>173175</td>\n",
       "      <td>NaN</td>\n",
       "      <td>40842</td>\n",
       "      <td>8701</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>503</td>\n",
       "      <td>102544</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.536416</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-76.617469</td>\n",
       "      <td>39.307456</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.276554</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.042220</td>\n",
       "      <td>0.029537</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.117709</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-76.711280</td>\n",
       "      <td>39.200410</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.256628</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-76.648420</td>\n",
       "      <td>39.288340</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.541888</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-76.614010</td>\n",
       "      <td>39.303680</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
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       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.775186</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-76.587490</td>\n",
       "      <td>39.327890</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
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       "    <tr>\n",
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       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-76.529770</td>\n",
       "      <td>39.371980</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
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      ],
      "text/plain": [
       "         CrimeDate CrimeTime CrimeCode        Location Description  \\\n",
       "count       263118    263118    263118          263118      263118   \n",
       "unique        2072      2935        80           25276          15   \n",
       "top     04/27/2015  18:00:00        4e  200 e pratt st     larceny   \n",
       "freq           407      6483     43093             632       58246   \n",
       "mean           NaN       NaN       NaN             NaN         NaN   \n",
       "std            NaN       NaN       NaN             NaN         NaN   \n",
       "min            NaN       NaN       NaN             NaN         NaN   \n",
       "25%            NaN       NaN       NaN             NaN         NaN   \n",
       "50%            NaN       NaN       NaN             NaN         NaN   \n",
       "75%            NaN       NaN       NaN             NaN         NaN   \n",
       "max            NaN       NaN       NaN             NaN         NaN   \n",
       "\n",
       "       Inside/Outside  Weapon           Post      District Neighborhood  \\\n",
       "count          263118  263118  263118.000000        263118       263118   \n",
       "unique              4       5            NaN             9          278   \n",
       "top                 i    none            NaN  northeastern     downtown   \n",
       "freq           131015  173175            NaN         40842         8701   \n",
       "mean              NaN     NaN       0.536416           NaN          NaN   \n",
       "std               NaN     NaN       0.276554           NaN          NaN   \n",
       "min               NaN     NaN       0.117709           NaN          NaN   \n",
       "25%               NaN     NaN       0.256628           NaN          NaN   \n",
       "50%               NaN     NaN       0.541888           NaN          NaN   \n",
       "75%               NaN     NaN       0.775186           NaN          NaN   \n",
       "max               NaN     NaN       1.000000           NaN          NaN   \n",
       "\n",
       "            Longitude       Latitude                       Location 1 Premise  \\\n",
       "count   263118.000000  263118.000000                           263118  263118   \n",
       "unique            NaN            NaN                            93543     118   \n",
       "top               NaN            NaN  (39.3180000000, -76.6582100000)  street   \n",
       "freq              NaN            NaN                              503  102544   \n",
       "mean       -76.617469      39.307456                              NaN     NaN   \n",
       "std          0.042220       0.029537                              NaN     NaN   \n",
       "min        -76.711280      39.200410                              NaN     NaN   \n",
       "25%        -76.648420      39.288340                              NaN     NaN   \n",
       "50%        -76.614010      39.303680                              NaN     NaN   \n",
       "75%        -76.587490      39.327890                              NaN     NaN   \n",
       "max        -76.529770      39.371980                              NaN     NaN   \n",
       "\n",
       "        Total Incidents  \n",
       "count          263118.0  \n",
       "unique              NaN  \n",
       "top                 NaN  \n",
       "freq                NaN  \n",
       "mean                1.0  \n",
       "std                 0.0  \n",
       "min                 1.0  \n",
       "25%                 1.0  \n",
       "50%                 1.0  \n",
       "75%                 1.0  \n",
       "max                 1.0  "
      ]
     },
     "execution_count": 118,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "baltimore.describe(include='all')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "id": "06e5c943",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
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       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>9</td>\n",
       "      <td>276</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>18843</td>\n",
       "      <td>104</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>top</th>\n",
       "      <td>04/27/2015</td>\n",
       "      <td>18:00:00</td>\n",
       "      <td>4e</td>\n",
       "      <td>1500 russell st</td>\n",
       "      <td>larceny</td>\n",
       "      <td>i</td>\n",
       "      <td>none</td>\n",
       "      <td>NaN</td>\n",
       "      <td>northeastern</td>\n",
       "      <td>downtown</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>(39.3180000000, -76.6582100000)</td>\n",
       "      <td>street</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>freq</th>\n",
       "      <td>28</td>\n",
       "      <td>650</td>\n",
       "      <td>4357</td>\n",
       "      <td>56</td>\n",
       "      <td>5740</td>\n",
       "      <td>13248</td>\n",
       "      <td>17358</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4137</td>\n",
       "      <td>853</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>49</td>\n",
       "      <td>10075</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.535663</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-76.617518</td>\n",
       "      <td>39.307771</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.275572</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.042479</td>\n",
       "      <td>0.029477</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.117709</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-76.711220</td>\n",
       "      <td>39.200470</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.257688</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-76.648905</td>\n",
       "      <td>39.288490</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.541888</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-76.614170</td>\n",
       "      <td>39.303850</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.766702</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-76.587170</td>\n",
       "      <td>39.328290</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-76.529770</td>\n",
       "      <td>39.371970</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         CrimeDate CrimeTime CrimeCode         Location Description  \\\n",
       "count        26312     26312     26312            26312       26312   \n",
       "unique        2071      1513        71            11180          15   \n",
       "top     04/27/2015  18:00:00        4e  1500 russell st     larceny   \n",
       "freq            28       650      4357               56        5740   \n",
       "mean           NaN       NaN       NaN              NaN         NaN   \n",
       "std            NaN       NaN       NaN              NaN         NaN   \n",
       "min            NaN       NaN       NaN              NaN         NaN   \n",
       "25%            NaN       NaN       NaN              NaN         NaN   \n",
       "50%            NaN       NaN       NaN              NaN         NaN   \n",
       "75%            NaN       NaN       NaN              NaN         NaN   \n",
       "max            NaN       NaN       NaN              NaN         NaN   \n",
       "\n",
       "       Inside/Outside Weapon          Post      District Neighborhood  \\\n",
       "count           26312  26312  26312.000000         26312        26312   \n",
       "unique              4      5           NaN             9          276   \n",
       "top                 i   none           NaN  northeastern     downtown   \n",
       "freq            13248  17358           NaN          4137          853   \n",
       "mean              NaN    NaN      0.535663           NaN          NaN   \n",
       "std               NaN    NaN      0.275572           NaN          NaN   \n",
       "min               NaN    NaN      0.117709           NaN          NaN   \n",
       "25%               NaN    NaN      0.257688           NaN          NaN   \n",
       "50%               NaN    NaN      0.541888           NaN          NaN   \n",
       "75%               NaN    NaN      0.766702           NaN          NaN   \n",
       "max               NaN    NaN      1.000000           NaN          NaN   \n",
       "\n",
       "           Longitude      Latitude                       Location 1 Premise  \\\n",
       "count   26312.000000  26312.000000                            26312   26312   \n",
       "unique           NaN           NaN                            18843     104   \n",
       "top              NaN           NaN  (39.3180000000, -76.6582100000)  street   \n",
       "freq             NaN           NaN                               49   10075   \n",
       "mean      -76.617518     39.307771                              NaN     NaN   \n",
       "std         0.042479      0.029477                              NaN     NaN   \n",
       "min       -76.711220     39.200470                              NaN     NaN   \n",
       "25%       -76.648905     39.288490                              NaN     NaN   \n",
       "50%       -76.614170     39.303850                              NaN     NaN   \n",
       "75%       -76.587170     39.328290                              NaN     NaN   \n",
       "max       -76.529770     39.371970                              NaN     NaN   \n",
       "\n",
       "        Total Incidents  \n",
       "count           26312.0  \n",
       "unique              NaN  \n",
       "top                 NaN  \n",
       "freq                NaN  \n",
       "mean                1.0  \n",
       "std                 0.0  \n",
       "min                 1.0  \n",
       "25%                 1.0  \n",
       "50%                 1.0  \n",
       "75%                 1.0  \n",
       "max                 1.0  "
      ]
     },
     "execution_count": 119,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "baltimore_test.describe(include='all')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "id": "1566d1b1",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>CrimeDate</th>\n",
       "      <th>CrimeTime</th>\n",
       "      <th>CrimeCode</th>\n",
       "      <th>Location</th>\n",
       "      <th>Description</th>\n",
       "      <th>Inside/Outside</th>\n",
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       "      <th>Longitude</th>\n",
       "      <th>Latitude</th>\n",
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       "      <th>Total Incidents</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>177604</td>\n",
       "      <td>177604</td>\n",
       "      <td>177604</td>\n",
       "      <td>177604</td>\n",
       "      <td>177604</td>\n",
       "      <td>177604</td>\n",
       "      <td>177604</td>\n",
       "      <td>177604.000000</td>\n",
       "      <td>177604</td>\n",
       "      <td>177604</td>\n",
       "      <td>177604.000000</td>\n",
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       "      <td>177604</td>\n",
       "      <td>177604</td>\n",
       "      <td>177604.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>unique</th>\n",
       "      <td>2072</td>\n",
       "      <td>2435</td>\n",
       "      <td>79</td>\n",
       "      <td>22781</td>\n",
       "      <td>15</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>9</td>\n",
       "      <td>278</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>74417</td>\n",
       "      <td>116</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>top</th>\n",
       "      <td>04/27/2015</td>\n",
       "      <td>18:00:00</td>\n",
       "      <td>4e</td>\n",
       "      <td>200 e pratt st</td>\n",
       "      <td>larceny</td>\n",
       "      <td>i</td>\n",
       "      <td>none</td>\n",
       "      <td>NaN</td>\n",
       "      <td>northeastern</td>\n",
       "      <td>downtown</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>(39.3180000000, -76.6582100000)</td>\n",
       "      <td>street</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>freq</th>\n",
       "      <td>298</td>\n",
       "      <td>4340</td>\n",
       "      <td>29065</td>\n",
       "      <td>440</td>\n",
       "      <td>39287</td>\n",
       "      <td>88319</td>\n",
       "      <td>116884</td>\n",
       "      <td>NaN</td>\n",
       "      <td>27451</td>\n",
       "      <td>5877</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>337</td>\n",
       "      <td>69325</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.536132</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-76.617452</td>\n",
       "      <td>39.307395</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.276695</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.042192</td>\n",
       "      <td>0.029526</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.117709</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-76.711280</td>\n",
       "      <td>39.200410</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.256628</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-76.648290</td>\n",
       "      <td>39.288330</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
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       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.541888</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-76.613990</td>\n",
       "      <td>39.303580</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.775186</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-76.587500</td>\n",
       "      <td>39.327742</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-76.529770</td>\n",
       "      <td>39.371970</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         CrimeDate CrimeTime CrimeCode        Location Description  \\\n",
       "count       177604    177604    177604          177604      177604   \n",
       "unique        2072      2435        79           22781          15   \n",
       "top     04/27/2015  18:00:00        4e  200 e pratt st     larceny   \n",
       "freq           298      4340     29065             440       39287   \n",
       "mean           NaN       NaN       NaN             NaN         NaN   \n",
       "std            NaN       NaN       NaN             NaN         NaN   \n",
       "min            NaN       NaN       NaN             NaN         NaN   \n",
       "25%            NaN       NaN       NaN             NaN         NaN   \n",
       "50%            NaN       NaN       NaN             NaN         NaN   \n",
       "75%            NaN       NaN       NaN             NaN         NaN   \n",
       "max            NaN       NaN       NaN             NaN         NaN   \n",
       "\n",
       "       Inside/Outside  Weapon           Post      District Neighborhood  \\\n",
       "count          177604  177604  177604.000000        177604       177604   \n",
       "unique              4       5            NaN             9          278   \n",
       "top                 i    none            NaN  northeastern     downtown   \n",
       "freq            88319  116884            NaN         27451         5877   \n",
       "mean              NaN     NaN       0.536132           NaN          NaN   \n",
       "std               NaN     NaN       0.276695           NaN          NaN   \n",
       "min               NaN     NaN       0.117709           NaN          NaN   \n",
       "25%               NaN     NaN       0.256628           NaN          NaN   \n",
       "50%               NaN     NaN       0.541888           NaN          NaN   \n",
       "75%               NaN     NaN       0.775186           NaN          NaN   \n",
       "max               NaN     NaN       1.000000           NaN          NaN   \n",
       "\n",
       "            Longitude       Latitude                       Location 1 Premise  \\\n",
       "count   177604.000000  177604.000000                           177604  177604   \n",
       "unique            NaN            NaN                            74417     116   \n",
       "top               NaN            NaN  (39.3180000000, -76.6582100000)  street   \n",
       "freq              NaN            NaN                              337   69325   \n",
       "mean       -76.617452      39.307395                              NaN     NaN   \n",
       "std          0.042192       0.029526                              NaN     NaN   \n",
       "min        -76.711280      39.200410                              NaN     NaN   \n",
       "25%        -76.648290      39.288330                              NaN     NaN   \n",
       "50%        -76.613990      39.303580                              NaN     NaN   \n",
       "75%        -76.587500      39.327742                              NaN     NaN   \n",
       "max        -76.529770      39.371970                              NaN     NaN   \n",
       "\n",
       "        Total Incidents  \n",
       "count          177604.0  \n",
       "unique              NaN  \n",
       "top                 NaN  \n",
       "freq                NaN  \n",
       "mean                1.0  \n",
       "std                 0.0  \n",
       "min                 1.0  \n",
       "25%                 1.0  \n",
       "50%                 1.0  \n",
       "75%                 1.0  \n",
       "max                 1.0  "
      ]
     },
     "execution_count": 120,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "baltimore_train.describe(include='all')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "id": "02e5bf0c",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<div>\n",
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       "    .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>CrimeDate</th>\n",
       "      <th>CrimeTime</th>\n",
       "      <th>CrimeCode</th>\n",
       "      <th>Location</th>\n",
       "      <th>Description</th>\n",
       "      <th>Inside/Outside</th>\n",
       "      <th>Weapon</th>\n",
       "      <th>Post</th>\n",
       "      <th>District</th>\n",
       "      <th>Neighborhood</th>\n",
       "      <th>Longitude</th>\n",
       "      <th>Latitude</th>\n",
       "      <th>Location 1</th>\n",
       "      <th>Premise</th>\n",
       "      <th>Total Incidents</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>59202</td>\n",
       "      <td>59202</td>\n",
       "      <td>59202</td>\n",
       "      <td>59202</td>\n",
       "      <td>59202</td>\n",
       "      <td>59202</td>\n",
       "      <td>59202</td>\n",
       "      <td>59202.000000</td>\n",
       "      <td>59202</td>\n",
       "      <td>59202</td>\n",
       "      <td>59202.000000</td>\n",
       "      <td>59202.000000</td>\n",
       "      <td>59202</td>\n",
       "      <td>59202</td>\n",
       "      <td>59202.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>unique</th>\n",
       "      <td>2070</td>\n",
       "      <td>1804</td>\n",
       "      <td>77</td>\n",
       "      <td>16050</td>\n",
       "      <td>15</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>9</td>\n",
       "      <td>276</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>35435</td>\n",
       "      <td>112</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>top</th>\n",
       "      <td>04/27/2015</td>\n",
       "      <td>18:00:00</td>\n",
       "      <td>4e</td>\n",
       "      <td>200 e pratt st</td>\n",
       "      <td>larceny</td>\n",
       "      <td>i</td>\n",
       "      <td>none</td>\n",
       "      <td>NaN</td>\n",
       "      <td>northeastern</td>\n",
       "      <td>downtown</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>(39.3180000000, -76.6582100000)</td>\n",
       "      <td>street</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>freq</th>\n",
       "      <td>81</td>\n",
       "      <td>1493</td>\n",
       "      <td>9671</td>\n",
       "      <td>140</td>\n",
       "      <td>13219</td>\n",
       "      <td>29448</td>\n",
       "      <td>38933</td>\n",
       "      <td>NaN</td>\n",
       "      <td>9254</td>\n",
       "      <td>1971</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>117</td>\n",
       "      <td>23144</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.537601</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-76.617499</td>\n",
       "      <td>39.307502</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.276567</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.042191</td>\n",
       "      <td>0.029595</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.117709</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-76.711270</td>\n",
       "      <td>39.202540</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.257688</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-76.648500</td>\n",
       "      <td>39.288340</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.541888</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-76.614020</td>\n",
       "      <td>39.303930</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.775186</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-76.587592</td>\n",
       "      <td>39.328030</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-76.529770</td>\n",
       "      <td>39.371980</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         CrimeDate CrimeTime CrimeCode        Location Description  \\\n",
       "count        59202     59202     59202           59202       59202   \n",
       "unique        2070      1804        77           16050          15   \n",
       "top     04/27/2015  18:00:00        4e  200 e pratt st     larceny   \n",
       "freq            81      1493      9671             140       13219   \n",
       "mean           NaN       NaN       NaN             NaN         NaN   \n",
       "std            NaN       NaN       NaN             NaN         NaN   \n",
       "min            NaN       NaN       NaN             NaN         NaN   \n",
       "25%            NaN       NaN       NaN             NaN         NaN   \n",
       "50%            NaN       NaN       NaN             NaN         NaN   \n",
       "75%            NaN       NaN       NaN             NaN         NaN   \n",
       "max            NaN       NaN       NaN             NaN         NaN   \n",
       "\n",
       "       Inside/Outside Weapon          Post      District Neighborhood  \\\n",
       "count           59202  59202  59202.000000         59202        59202   \n",
       "unique              4      5           NaN             9          276   \n",
       "top                 i   none           NaN  northeastern     downtown   \n",
       "freq            29448  38933           NaN          9254         1971   \n",
       "mean              NaN    NaN      0.537601           NaN          NaN   \n",
       "std               NaN    NaN      0.276567           NaN          NaN   \n",
       "min               NaN    NaN      0.117709           NaN          NaN   \n",
       "25%               NaN    NaN      0.257688           NaN          NaN   \n",
       "50%               NaN    NaN      0.541888           NaN          NaN   \n",
       "75%               NaN    NaN      0.775186           NaN          NaN   \n",
       "max               NaN    NaN      1.000000           NaN          NaN   \n",
       "\n",
       "           Longitude      Latitude                       Location 1 Premise  \\\n",
       "count   59202.000000  59202.000000                            59202   59202   \n",
       "unique           NaN           NaN                            35435     112   \n",
       "top              NaN           NaN  (39.3180000000, -76.6582100000)  street   \n",
       "freq             NaN           NaN                              117   23144   \n",
       "mean      -76.617499     39.307502                              NaN     NaN   \n",
       "std         0.042191      0.029595                              NaN     NaN   \n",
       "min       -76.711270     39.202540                              NaN     NaN   \n",
       "25%       -76.648500     39.288340                              NaN     NaN   \n",
       "50%       -76.614020     39.303930                              NaN     NaN   \n",
       "75%       -76.587592     39.328030                              NaN     NaN   \n",
       "max       -76.529770     39.371980                              NaN     NaN   \n",
       "\n",
       "        Total Incidents  \n",
       "count           59202.0  \n",
       "unique              NaN  \n",
       "top                 NaN  \n",
       "freq                NaN  \n",
       "mean                1.0  \n",
       "std                 0.0  \n",
       "min                 1.0  \n",
       "25%                 1.0  \n",
       "50%                 1.0  \n",
       "75%                 1.0  \n",
       "max                 1.0  "
      ]
     },
     "execution_count": 121,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "baltimore_val.describe(include='all')"
   ]
  }
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