peruse.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# %pip install arcgis"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Link for this data: \n",
    "Interesting link: https://www.city-data.com/so/so-Baltimore-Maryland.html\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import sys, os\n",
    "from arcgis.features import GeoAccessor\n",
    "import datetime\n",
    "from collections import Counter\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# opening_date = pd.to_datetime(opening_date).date()\n",
    "# print(opening_date) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "pd.set_option('display.max_columns', None)\n",
    "pd.set_option('display.expand_frame_repr', False)\n",
    "pd.set_option('max_colwidth', None)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# pd.set_option('display.max_columns', None)\n",
    "# pd.set_option('display.expand_frame_repr', False)\n",
    "# pd.set_option('max_colwidth', -1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "type_dict = {\n",
    "    'X': 'float32',\n",
    "    'Y': 'float32',\n",
    "    'RowID': 'int',\n",
    "    'City': 'str',\n",
    "    'CaseNumber':'str',\n",
    "    'Post': 'str',\n",
    "    'ZipCode': 'str'\n",
    "    }"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "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>X</th>\n",
       "      <th>Y</th>\n",
       "      <th>RowID</th>\n",
       "      <th>CaseNumber</th>\n",
       "      <th>CreatedDate</th>\n",
       "      <th>ModifiedDate</th>\n",
       "      <th>LastName</th>\n",
       "      <th>FirstName</th>\n",
       "      <th>MiddleName</th>\n",
       "      <th>DateOfBirth</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Race</th>\n",
       "      <th>FullAddress</th>\n",
       "      <th>City</th>\n",
       "      <th>State</th>\n",
       "      <th>ZipCode</th>\n",
       "      <th>District</th>\n",
       "      <th>Post</th>\n",
       "      <th>Neighborhood</th>\n",
       "      <th>Latitude</th>\n",
       "      <th>Longitude</th>\n",
       "      <th>GeoLocation</th>\n",
       "      <th>Shape</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1763</th>\n",
       "      <td>1413470.625</td>\n",
       "      <td>589326.0</td>\n",
       "      <td>1764</td>\n",
       "      <td>18-0270</td>\n",
       "      <td>2018/05/15 00:00:00+00</td>\n",
       "      <td>2020/12/29 00:00:00+00</td>\n",
       "      <td>Bradds</td>\n",
       "      <td>William</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1989/02/01 00:00:00+00</td>\n",
       "      <td>Male</td>\n",
       "      <td>White</td>\n",
       "      <td>1611 Mchenry St</td>\n",
       "      <td>Baltimore</td>\n",
       "      <td>Md</td>\n",
       "      <td>21223</td>\n",
       "      <td>Southern</td>\n",
       "      <td>935</td>\n",
       "      <td>New Southwest/Mount Clare</td>\n",
       "      <td>39.2843</td>\n",
       "      <td>-76.6427</td>\n",
       "      <td>(39.2843,-76.6427)</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                X         Y  RowID CaseNumber             CreatedDate            ModifiedDate LastName FirstName MiddleName             DateOfBirth Gender   Race      FullAddress       City State ZipCode  District Post               Neighborhood  Latitude  Longitude         GeoLocation  Shape\n",
       "1763  1413470.625  589326.0   1764    18-0270  2018/05/15 00:00:00+00  2020/12/29 00:00:00+00   Bradds   William        NaN  1989/02/01 00:00:00+00   Male  White  1611 Mchenry St  Baltimore    Md   21223  Southern  935  New Southwest/Mount Clare   39.2843   -76.6427  (39.2843,-76.6427)    NaN"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df=pd.read_csv('folders/data/GunOffenders.csv', dtype=type_dict,)\n",
    "# df.sample(10)\n",
    "df.sample()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['DateOfBirth'] = df['DateOfBirth'].str.split(' ', expand=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['CreatedDate'] = df['CreatedDate'].str.split(' ', expand=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['ModifiedDate'] = df['ModifiedDate'].str.split(' ', expand=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['ModifiedDate'] = pd.to_datetime(df['ModifiedDate'], errors='coerce')\n",
    "df['CreatedDate'] = pd.to_datetime(df['CreatedDate'], errors='coerce')\n",
    "df['DateOfBirth'] = pd.to_datetime(df['DateOfBirth'], errors='coerce')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "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>X</th>\n",
       "      <th>Y</th>\n",
       "      <th>RowID</th>\n",
       "      <th>CaseNumber</th>\n",
       "      <th>CreatedDate</th>\n",
       "      <th>ModifiedDate</th>\n",
       "      <th>LastName</th>\n",
       "      <th>FirstName</th>\n",
       "      <th>MiddleName</th>\n",
       "      <th>DateOfBirth</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Race</th>\n",
       "      <th>FullAddress</th>\n",
       "      <th>City</th>\n",
       "      <th>State</th>\n",
       "      <th>ZipCode</th>\n",
       "      <th>District</th>\n",
       "      <th>Post</th>\n",
       "      <th>Neighborhood</th>\n",
       "      <th>Latitude</th>\n",
       "      <th>Longitude</th>\n",
       "      <th>GeoLocation</th>\n",
       "      <th>Shape</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1814</th>\n",
       "      <td>1430045.125</td>\n",
       "      <td>598392.8125</td>\n",
       "      <td>1815</td>\n",
       "      <td>18-0141</td>\n",
       "      <td>2018-03-13</td>\n",
       "      <td>2020-12-07</td>\n",
       "      <td>Davis</td>\n",
       "      <td>Dominez</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1991-04-22</td>\n",
       "      <td>Male</td>\n",
       "      <td>Black</td>\n",
       "      <td>2410 E Federal St</td>\n",
       "      <td>Baltimore</td>\n",
       "      <td>Md</td>\n",
       "      <td>21213</td>\n",
       "      <td>Eastern</td>\n",
       "      <td>331</td>\n",
       "      <td>Broadway East</td>\n",
       "      <td>39.309</td>\n",
       "      <td>-76.584</td>\n",
       "      <td>(39.309,-76.584)</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                X            Y  RowID CaseNumber CreatedDate ModifiedDate LastName FirstName MiddleName DateOfBirth Gender   Race        FullAddress       City State ZipCode District Post   Neighborhood  Latitude  Longitude       GeoLocation  Shape\n",
       "1814  1430045.125  598392.8125   1815    18-0141  2018-03-13   2020-12-07    Davis   Dominez        NaN  1991-04-22   Male  Black  2410 E Federal St  Baltimore    Md   21213  Eastern  331  Broadway East    39.309    -76.584  (39.309,-76.584)    NaN"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.sample()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 2748 entries, 0 to 2747\n",
      "Data columns (total 23 columns):\n",
      " #   Column        Non-Null Count  Dtype         \n",
      "---  ------        --------------  -----         \n",
      " 0   X             2530 non-null   float32       \n",
      " 1   Y             2530 non-null   float32       \n",
      " 2   RowID         2748 non-null   int64         \n",
      " 3   CaseNumber    2748 non-null   object        \n",
      " 4   CreatedDate   2748 non-null   datetime64[ns]\n",
      " 5   ModifiedDate  2748 non-null   datetime64[ns]\n",
      " 6   LastName      2748 non-null   object        \n",
      " 7   FirstName     2748 non-null   object        \n",
      " 8   MiddleName    399 non-null    object        \n",
      " 9   DateOfBirth   2747 non-null   datetime64[ns]\n",
      " 10  Gender        2748 non-null   object        \n",
      " 11  Race          2748 non-null   object        \n",
      " 12  FullAddress   2530 non-null   object        \n",
      " 13  City          2530 non-null   object        \n",
      " 14  State         2530 non-null   object        \n",
      " 15  ZipCode       2527 non-null   object        \n",
      " 16  District      2260 non-null   object        \n",
      " 17  Post          2260 non-null   object        \n",
      " 18  Neighborhood  2260 non-null   object        \n",
      " 19  Latitude      2530 non-null   float64       \n",
      " 20  Longitude     2530 non-null   float64       \n",
      " 21  GeoLocation   2748 non-null   object        \n",
      " 22  Shape         0 non-null      float64       \n",
      "dtypes: datetime64[ns](3), float32(2), float64(3), int64(1), object(14)\n",
      "memory usage: 472.4+ KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "from dateutil.relativedelta import relativedelta\n",
    "from datetime import date"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "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>X</th>\n",
       "      <th>Y</th>\n",
       "      <th>RowID</th>\n",
       "      <th>CaseNumber</th>\n",
       "      <th>CreatedDate</th>\n",
       "      <th>ModifiedDate</th>\n",
       "      <th>LastName</th>\n",
       "      <th>FirstName</th>\n",
       "      <th>MiddleName</th>\n",
       "      <th>DateOfBirth</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Race</th>\n",
       "      <th>FullAddress</th>\n",
       "      <th>City</th>\n",
       "      <th>State</th>\n",
       "      <th>ZipCode</th>\n",
       "      <th>District</th>\n",
       "      <th>Post</th>\n",
       "      <th>Neighborhood</th>\n",
       "      <th>Latitude</th>\n",
       "      <th>Longitude</th>\n",
       "      <th>GeoLocation</th>\n",
       "      <th>Shape</th>\n",
       "      <th>age</th>\n",
       "      <th>Age</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>716</th>\n",
       "      <td>1409381.5</td>\n",
       "      <td>607886.4375</td>\n",
       "      <td>717</td>\n",
       "      <td>20-0020</td>\n",
       "      <td>2020-01-23</td>\n",
       "      <td>2020-12-07</td>\n",
       "      <td>James</td>\n",
       "      <td>Eugene</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1993-07-09</td>\n",
       "      <td>Male</td>\n",
       "      <td>Black</td>\n",
       "      <td>2407 Keyworth Ave</td>\n",
       "      <td>Baltimore</td>\n",
       "      <td>Md</td>\n",
       "      <td>21215</td>\n",
       "      <td>Northern</td>\n",
       "      <td>533</td>\n",
       "      <td>Park Circle</td>\n",
       "      <td>39.3353</td>\n",
       "      <td>-76.6569</td>\n",
       "      <td>(39.3353,-76.6569)</td>\n",
       "      <td>NaN</td>\n",
       "      <td>9694 days</td>\n",
       "      <td>26.541271</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             X            Y  RowID CaseNumber CreatedDate ModifiedDate LastName FirstName MiddleName DateOfBirth Gender   Race        FullAddress       City State ZipCode  District Post Neighborhood  Latitude  Longitude         GeoLocation  Shape       age        Age\n",
       "716  1409381.5  607886.4375    717    20-0020  2020-01-23   2020-12-07    James    Eugene        NaN  1993-07-09   Male  Black  2407 Keyworth Ave  Baltimore    Md   21215  Northern  533  Park Circle   39.3353   -76.6569  (39.3353,-76.6569)    NaN 9694 days  26.541271"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['age']=df['CreatedDate']-df['DateOfBirth']\n",
    "df['Age'] = df['age']/np.timedelta64(1,'Y')\n",
    "df.sample()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "X               2401\n",
       "Y               2407\n",
       "RowID           2748\n",
       "CaseNumber      2745\n",
       "CreatedDate      967\n",
       "ModifiedDate     160\n",
       "LastName        1211\n",
       "FirstName       1129\n",
       "MiddleName       263\n",
       "DateOfBirth     2407\n",
       "Gender             2\n",
       "Race               5\n",
       "FullAddress     2450\n",
       "City              46\n",
       "State              1\n",
       "ZipCode           69\n",
       "District           9\n",
       "Post             122\n",
       "Neighborhood     213\n",
       "Latitude        1066\n",
       "Longitude       1382\n",
       "GeoLocation     2413\n",
       "Shape              0\n",
       "age             2320\n",
       "Age             2320\n",
       "dtype: int64"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# find unique value count\n",
    "df.nunique(axis = 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "FirstName  LastName \n",
       "Aamir      Benton       1\n",
       "Aaron      Alexander    1\n",
       "           Atkins       1\n",
       "           Ausby        1\n",
       "           Bryant       1\n",
       "                       ..\n",
       "Zebary     Pearson      1\n",
       "Zhamiel    Dixon        1\n",
       "Ziyon      Thompson     1\n",
       "Zuri       Kelly        1\n",
       "`Montaez   Allen        1\n",
       "Length: 2690, dtype: int64"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "count_series = df.groupby(['FirstName', 'LastName']).size()\n",
    "count_series"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "new_df = count_series.to_frame(name = 'size').reset_index()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "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>FirstName</th>\n",
       "      <th>LastName</th>\n",
       "      <th>size</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>110</th>\n",
       "      <td>Anthony</td>\n",
       "      <td>Brown</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>154</th>\n",
       "      <td>Antoine</td>\n",
       "      <td>Jones</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>177</th>\n",
       "      <td>Antonio</td>\n",
       "      <td>Moore</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1902</th>\n",
       "      <td>Michael</td>\n",
       "      <td>Smith</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1910</th>\n",
       "      <td>Michael</td>\n",
       "      <td>Williams</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     FirstName  LastName  size\n",
       "110    Anthony     Brown     3\n",
       "154    Antoine     Jones     3\n",
       "177    Antonio     Moore     3\n",
       "1902   Michael     Smith     3\n",
       "1910   Michael  Williams     3"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_df.nlargest(5, 'size')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "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>X</th>\n",
       "      <th>Y</th>\n",
       "      <th>RowID</th>\n",
       "      <th>CaseNumber</th>\n",
       "      <th>CreatedDate</th>\n",
       "      <th>ModifiedDate</th>\n",
       "      <th>LastName</th>\n",
       "      <th>FirstName</th>\n",
       "      <th>MiddleName</th>\n",
       "      <th>DateOfBirth</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Race</th>\n",
       "      <th>FullAddress</th>\n",
       "      <th>City</th>\n",
       "      <th>State</th>\n",
       "      <th>ZipCode</th>\n",
       "      <th>District</th>\n",
       "      <th>Post</th>\n",
       "      <th>Neighborhood</th>\n",
       "      <th>Latitude</th>\n",
       "      <th>Longitude</th>\n",
       "      <th>GeoLocation</th>\n",
       "      <th>Shape</th>\n",
       "      <th>age</th>\n",
       "      <th>Age</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>206</th>\n",
       "      <td>1411806.25</td>\n",
       "      <td>595220.125</td>\n",
       "      <td>207</td>\n",
       "      <td>21-0171</td>\n",
       "      <td>2021-06-04</td>\n",
       "      <td>2021-06-04</td>\n",
       "      <td>Chance</td>\n",
       "      <td>Brion</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2019-02-15</td>\n",
       "      <td>Male</td>\n",
       "      <td>Black</td>\n",
       "      <td>1022 Appleton St</td>\n",
       "      <td>Baltimore</td>\n",
       "      <td>Md</td>\n",
       "      <td>21217</td>\n",
       "      <td>Western</td>\n",
       "      <td>722</td>\n",
       "      <td>Midtown-Edmondson</td>\n",
       "      <td>39.3005</td>\n",
       "      <td>-76.6485</td>\n",
       "      <td>(39.3005,-76.6485)</td>\n",
       "      <td>NaN</td>\n",
       "      <td>840 days</td>\n",
       "      <td>2.299842</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              X           Y  RowID CaseNumber CreatedDate ModifiedDate LastName FirstName MiddleName DateOfBirth Gender   Race       FullAddress       City State ZipCode District Post       Neighborhood  Latitude  Longitude         GeoLocation  Shape      age       Age\n",
       "206  1411806.25  595220.125    207    21-0171  2021-06-04   2021-06-04   Chance     Brion        NaN  2019-02-15   Male  Black  1022 Appleton St  Baltimore    Md   21217  Western  722  Midtown-Edmondson   39.3005   -76.6485  (39.3005,-76.6485)    NaN 840 days  2.299842"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.sort_values('Age', ascending=True).head(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Age'].isna().sum()\n",
    "#only one null value"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "28.384672316497745"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Age'].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "26.240100754977856"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Age'].median()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Black      2653\n",
       "White        71\n",
       "Unknown      19\n",
       "Asian         4\n",
       "Indian        1\n",
       "Name: Race, dtype: int64"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Race'].value_counts()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Male      2688\n",
       "Female      60\n",
       "Name: Gender, dtype: int64"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Gender'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2.299841885870346"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Age'].min(axis = 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "80.28364716592401"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Age'].max(axis = 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "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>X</th>\n",
       "      <th>Y</th>\n",
       "      <th>RowID</th>\n",
       "      <th>CaseNumber</th>\n",
       "      <th>CreatedDate</th>\n",
       "      <th>ModifiedDate</th>\n",
       "      <th>LastName</th>\n",
       "      <th>FirstName</th>\n",
       "      <th>MiddleName</th>\n",
       "      <th>DateOfBirth</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Race</th>\n",
       "      <th>FullAddress</th>\n",
       "      <th>City</th>\n",
       "      <th>State</th>\n",
       "      <th>ZipCode</th>\n",
       "      <th>District</th>\n",
       "      <th>Post</th>\n",
       "      <th>Neighborhood</th>\n",
       "      <th>Latitude</th>\n",
       "      <th>Longitude</th>\n",
       "      <th>GeoLocation</th>\n",
       "      <th>Shape</th>\n",
       "      <th>age</th>\n",
       "      <th>Age</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>206</th>\n",
       "      <td>1411806.250</td>\n",
       "      <td>595220.1250</td>\n",
       "      <td>207</td>\n",
       "      <td>21-0171</td>\n",
       "      <td>2021-06-04</td>\n",
       "      <td>2021-06-04</td>\n",
       "      <td>Chance</td>\n",
       "      <td>Brion</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2019-02-15</td>\n",
       "      <td>Male</td>\n",
       "      <td>Black</td>\n",
       "      <td>1022 Appleton St</td>\n",
       "      <td>Baltimore</td>\n",
       "      <td>Md</td>\n",
       "      <td>21217</td>\n",
       "      <td>Western</td>\n",
       "      <td>722</td>\n",
       "      <td>Midtown-Edmondson</td>\n",
       "      <td>39.3005</td>\n",
       "      <td>-76.6485</td>\n",
       "      <td>(39.3005,-76.6485)</td>\n",
       "      <td>NaN</td>\n",
       "      <td>840 days</td>\n",
       "      <td>2.299842</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2710</th>\n",
       "      <td>1426328.375</td>\n",
       "      <td>568545.4375</td>\n",
       "      <td>2711</td>\n",
       "      <td>09-0260</td>\n",
       "      <td>2009-08-13</td>\n",
       "      <td>2021-08-23</td>\n",
       "      <td>Dorsey</td>\n",
       "      <td>Raheem</td>\n",
       "      <td>Jabar</td>\n",
       "      <td>1994-06-02</td>\n",
       "      <td>Male</td>\n",
       "      <td>Black</td>\n",
       "      <td>1107 Seaboard Ct</td>\n",
       "      <td>Baltimore</td>\n",
       "      <td>Md</td>\n",
       "      <td>21225</td>\n",
       "      <td>Southern</td>\n",
       "      <td>913</td>\n",
       "      <td>Brooklyn</td>\n",
       "      <td>39.2271</td>\n",
       "      <td>-76.5976</td>\n",
       "      <td>(39.2271,-76.5976)</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5551 days</td>\n",
       "      <td>15.198122</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2624</th>\n",
       "      <td>1409505.750</td>\n",
       "      <td>589857.1250</td>\n",
       "      <td>2625</td>\n",
       "      <td>11-0487</td>\n",
       "      <td>2011-11-21</td>\n",
       "      <td>2020-12-07</td>\n",
       "      <td>Holt</td>\n",
       "      <td>Arteesha</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1996-04-23</td>\n",
       "      <td>Female</td>\n",
       "      <td>Black</td>\n",
       "      <td>2526 W Lombard St</td>\n",
       "      <td>Baltimore</td>\n",
       "      <td>Md</td>\n",
       "      <td>21223</td>\n",
       "      <td>Southwest</td>\n",
       "      <td>835</td>\n",
       "      <td>Shipley Hill</td>\n",
       "      <td>39.2858</td>\n",
       "      <td>-76.6567</td>\n",
       "      <td>(39.2858,-76.6567)</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5690 days</td>\n",
       "      <td>15.578691</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2679</th>\n",
       "      <td>1426750.125</td>\n",
       "      <td>594844.9375</td>\n",
       "      <td>2680</td>\n",
       "      <td>10-0354</td>\n",
       "      <td>2010-08-31</td>\n",
       "      <td>2020-12-07</td>\n",
       "      <td>Hall</td>\n",
       "      <td>Anthony</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1994-07-07</td>\n",
       "      <td>Male</td>\n",
       "      <td>Black</td>\n",
       "      <td>1621 E Madison St</td>\n",
       "      <td>Baltimore</td>\n",
       "      <td>Md</td>\n",
       "      <td>21287</td>\n",
       "      <td>Eastern</td>\n",
       "      <td>323</td>\n",
       "      <td>Gay Street</td>\n",
       "      <td>39.2993</td>\n",
       "      <td>-76.5957</td>\n",
       "      <td>(39.2993,-76.5957)</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5899 days</td>\n",
       "      <td>16.150913</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2361</th>\n",
       "      <td>1426193.000</td>\n",
       "      <td>592839.1875</td>\n",
       "      <td>2362</td>\n",
       "      <td>15-0040</td>\n",
       "      <td>2015-01-22</td>\n",
       "      <td>2021-05-04</td>\n",
       "      <td>Moore</td>\n",
       "      <td>Torray</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1998-11-11</td>\n",
       "      <td>Male</td>\n",
       "      <td>Black</td>\n",
       "      <td>210 N Spring Ct</td>\n",
       "      <td>Baltimore</td>\n",
       "      <td>Md</td>\n",
       "      <td>21231</td>\n",
       "      <td>Southeast</td>\n",
       "      <td>212</td>\n",
       "      <td>Dunbar-Broadway</td>\n",
       "      <td>39.2938</td>\n",
       "      <td>-76.5977</td>\n",
       "      <td>(39.2938,-76.5977)</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5916 days</td>\n",
       "      <td>16.197458</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",
       "      <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>2349</th>\n",
       "      <td>1402956.375</td>\n",
       "      <td>592965.7500</td>\n",
       "      <td>2350</td>\n",
       "      <td>15-0119</td>\n",
       "      <td>2015-03-23</td>\n",
       "      <td>2021-08-24</td>\n",
       "      <td>Cobb</td>\n",
       "      <td>Jessie</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1945-05-02</td>\n",
       "      <td>Male</td>\n",
       "      <td>Black</td>\n",
       "      <td>607 Mount Holly St</td>\n",
       "      <td>Baltimore</td>\n",
       "      <td>Md</td>\n",
       "      <td>21229</td>\n",
       "      <td>Southwest</td>\n",
       "      <td>815</td>\n",
       "      <td>Edgewood</td>\n",
       "      <td>39.2944</td>\n",
       "      <td>-76.6798</td>\n",
       "      <td>(39.2944,-76.6798)</td>\n",
       "      <td>NaN</td>\n",
       "      <td>25527 days</td>\n",
       "      <td>69.890552</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>643</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>644</td>\n",
       "      <td>20-0096</td>\n",
       "      <td>2020-03-02</td>\n",
       "      <td>2020-12-07</td>\n",
       "      <td>Wynne</td>\n",
       "      <td>Hardaway</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1948-01-29</td>\n",
       "      <td>Male</td>\n",
       "      <td>Black</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>(,)</td>\n",
       "      <td>NaN</td>\n",
       "      <td>26331 days</td>\n",
       "      <td>72.091829</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1477</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1478</td>\n",
       "      <td>18-0667</td>\n",
       "      <td>2018-12-06</td>\n",
       "      <td>2020-12-07</td>\n",
       "      <td>Gomez</td>\n",
       "      <td>Irvin</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1944-08-31</td>\n",
       "      <td>Male</td>\n",
       "      <td>Black</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>(,)</td>\n",
       "      <td>NaN</td>\n",
       "      <td>27125 days</td>\n",
       "      <td>74.265728</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1574</th>\n",
       "      <td>1361726.875</td>\n",
       "      <td>619188.4375</td>\n",
       "      <td>1575</td>\n",
       "      <td>18-0563</td>\n",
       "      <td>2018-10-10</td>\n",
       "      <td>2021-03-30</td>\n",
       "      <td>Chavis</td>\n",
       "      <td>Gordon</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1938-06-29</td>\n",
       "      <td>Male</td>\n",
       "      <td>Black</td>\n",
       "      <td>9620 Mendoza Road, Randallstown, 21133</td>\n",
       "      <td>Randallstown</td>\n",
       "      <td>Md</td>\n",
       "      <td>21133</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>39.3667</td>\n",
       "      <td>-76.8253</td>\n",
       "      <td>(39.3667,-76.8253)</td>\n",
       "      <td>NaN</td>\n",
       "      <td>29323 days</td>\n",
       "      <td>80.283647</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1428</th>\n",
       "      <td>1401533.250</td>\n",
       "      <td>595291.9375</td>\n",
       "      <td>1429</td>\n",
       "      <td>18-0720</td>\n",
       "      <td>2018-12-26</td>\n",
       "      <td>2021-04-15</td>\n",
       "      <td>Jones</td>\n",
       "      <td>David</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaT</td>\n",
       "      <td>Male</td>\n",
       "      <td>Black</td>\n",
       "      <td>4119 Mountwood Rd</td>\n",
       "      <td>Baltimore</td>\n",
       "      <td>Md</td>\n",
       "      <td>21229</td>\n",
       "      <td>Southwest</td>\n",
       "      <td>815</td>\n",
       "      <td>Edmondson Village</td>\n",
       "      <td>39.3008</td>\n",
       "      <td>-76.6848</td>\n",
       "      <td>(39.3008,-76.6848)</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaT</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2748 rows × 25 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                X            Y  RowID CaseNumber CreatedDate ModifiedDate LastName FirstName MiddleName DateOfBirth  Gender   Race                             FullAddress          City State ZipCode   District Post       Neighborhood  Latitude  Longitude         GeoLocation  Shape        age        Age\n",
       "206   1411806.250  595220.1250    207    21-0171  2021-06-04   2021-06-04   Chance     Brion        NaN  2019-02-15    Male  Black                        1022 Appleton St     Baltimore    Md   21217    Western  722  Midtown-Edmondson   39.3005   -76.6485  (39.3005,-76.6485)    NaN   840 days   2.299842\n",
       "2710  1426328.375  568545.4375   2711    09-0260  2009-08-13   2021-08-23   Dorsey    Raheem      Jabar  1994-06-02    Male  Black                        1107 Seaboard Ct     Baltimore    Md   21225   Southern  913           Brooklyn   39.2271   -76.5976  (39.2271,-76.5976)    NaN  5551 days  15.198122\n",
       "2624  1409505.750  589857.1250   2625    11-0487  2011-11-21   2020-12-07     Holt  Arteesha        NaN  1996-04-23  Female  Black                       2526 W Lombard St     Baltimore    Md   21223  Southwest  835       Shipley Hill   39.2858   -76.6567  (39.2858,-76.6567)    NaN  5690 days  15.578691\n",
       "2679  1426750.125  594844.9375   2680    10-0354  2010-08-31   2020-12-07     Hall   Anthony        NaN  1994-07-07    Male  Black                       1621 E Madison St     Baltimore    Md   21287    Eastern  323         Gay Street   39.2993   -76.5957  (39.2993,-76.5957)    NaN  5899 days  16.150913\n",
       "2361  1426193.000  592839.1875   2362    15-0040  2015-01-22   2021-05-04    Moore    Torray        NaN  1998-11-11    Male  Black                         210 N Spring Ct     Baltimore    Md   21231  Southeast  212    Dunbar-Broadway   39.2938   -76.5977  (39.2938,-76.5977)    NaN  5916 days  16.197458\n",
       "...           ...          ...    ...        ...         ...          ...      ...       ...        ...         ...     ...    ...                                     ...           ...   ...     ...        ...  ...                ...       ...        ...                 ...    ...        ...        ...\n",
       "2349  1402956.375  592965.7500   2350    15-0119  2015-03-23   2021-08-24     Cobb    Jessie        NaN  1945-05-02    Male  Black                      607 Mount Holly St     Baltimore    Md   21229  Southwest  815           Edgewood   39.2944   -76.6798  (39.2944,-76.6798)    NaN 25527 days  69.890552\n",
       "643           NaN          NaN    644    20-0096  2020-03-02   2020-12-07    Wynne  Hardaway        NaN  1948-01-29    Male  Black                                     NaN           NaN   NaN     NaN        NaN  NaN                NaN       NaN        NaN                 (,)    NaN 26331 days  72.091829\n",
       "1477          NaN          NaN   1478    18-0667  2018-12-06   2020-12-07    Gomez     Irvin        NaN  1944-08-31    Male  Black                                     NaN           NaN   NaN     NaN        NaN  NaN                NaN       NaN        NaN                 (,)    NaN 27125 days  74.265728\n",
       "1574  1361726.875  619188.4375   1575    18-0563  2018-10-10   2021-03-30   Chavis    Gordon        NaN  1938-06-29    Male  Black  9620 Mendoza Road, Randallstown, 21133  Randallstown    Md   21133        NaN  NaN                NaN   39.3667   -76.8253  (39.3667,-76.8253)    NaN 29323 days  80.283647\n",
       "1428  1401533.250  595291.9375   1429    18-0720  2018-12-26   2021-04-15    Jones     David        NaN         NaT    Male  Black                       4119 Mountwood Rd     Baltimore    Md   21229  Southwest  815  Edmondson Village   39.3008   -76.6848  (39.3008,-76.6848)    NaN        NaT        NaN\n",
       "\n",
       "[2748 rows x 25 columns]"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.sort_values('Age', axis=0, ascending=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "218"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['FullAddress'].isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2668\n"
     ]
    }
   ],
   "source": [
    "print(2450 + 218)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "BEGINNING OF REVIEW:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "addressesdf=df.groupby(df['FullAddress'].tolist()).size().reset_index().\\\n",
    "    rename(columns={0:'records'})\n",
    "addressesdf.sort_values('records', ascending=False, inplace=True)\n",
    "# addressesdf\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "address_count = dict()\n",
    "for i in df['FullAddress']:\n",
    "  address_count[i] = address_count.get(i, 0) + 1\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "# address_count"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "addressDict={}\n",
    "for address in df['FullAddress']:\n",
    "    address_count=1\n",
    "    if not address in addressDict:\n",
    "        addressDict[address]=address_count\n",
    "\n",
    "    else:\n",
    "        addressDict[address]=address_count=+1\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "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>0</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1302 Glyndon Ave</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>132 Denison St</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2622 Loyola Northway</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>708 E 21st St</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1734 Appleton St</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                      0\n",
       "1302 Glyndon Ave      1\n",
       "132 Denison St        1\n",
       "2622 Loyola Northway  1\n",
       "708 E 21st St         1\n",
       "1734 Appleton St      1"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "address_df=pd.DataFrame.from_dict(addressDict,orient='index')\n",
    "address_df.head()\n",
    "address_df.sort_values(0,ascending=True)\n",
    "address_df.tail()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "END OF REVIEW ^"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['Age'] = df['Age'].astype(float)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "# df['CreatedDate'].astype()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['month'] = pd.DatetimeIndex(df['CreatedDate']).month"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['date_month'] = pd.DatetimeIndex(df['CreatedDate']).day"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['year'] = pd.DatetimeIndex(df['CreatedDate']).year"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "years_by_count=df[['RowID','year']].groupby('year').agg('count')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "days_by_count = df[['RowID','date_month']].groupby('date_month').agg('count')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='date_month'>"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "days_by_count.plot.bar()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "days_by_count.sort_values('RowID', ascending=False, inplace=True)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='date_month'>"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "days_by_count.plot.bar()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "months_by_count = df[['RowID','month']].groupby('month').agg('count')\n",
    "months_by_count.sort_values('RowID', ascending=False, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='month'>"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "months_by_count.plot.bar()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:ylabel='month'>"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "months_by_count.plot.barh()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "mon_day_series = df.groupby(['month', 'date_month']).size()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    353.000000\n",
       "mean       7.784703\n",
       "std        5.574301\n",
       "min        1.000000\n",
       "25%        4.000000\n",
       "50%        6.000000\n",
       "75%       11.000000\n",
       "max       40.000000\n",
       "dtype: float64"
      ]
     },
     "execution_count": 97,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mon_day_series.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "40"
      ]
     },
     "execution_count": 99,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mon_day_series[12,26]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 100,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "mon_day_series.hist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 102,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "np.log(mon_day_series).hist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    353.000000\n",
       "mean       1.798941\n",
       "std        0.745577\n",
       "min        0.000000\n",
       "25%        1.386294\n",
       "50%        1.791759\n",
       "75%        2.397895\n",
       "max        3.688879\n",
       "dtype: float64"
      ]
     },
     "execution_count": 103,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.log(mon_day_series).describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3.6888794541139363"
      ]
     },
     "execution_count": 104,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.log(40)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "39.964837299699624"
      ]
     },
     "execution_count": 105,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.exp(3.688)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "6.043244278833318"
      ]
     },
     "execution_count": 106,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.exp(1.798941)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 720x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "ax = mon_day_series.plot()\n",
    "ax = mon_day_series.reindex(mon_day_series.index).plot(marker='o',linestyle='none',color='g', ax=ax, figsize=(10, 4))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [],
   "source": [
    "ax.figure.savefig('plot.png')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='month,date_month'>"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "mon_day_series.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='month,date_month'>"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "#great way to plot a series!\n",
    "mon_day_series.plot(style='.')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [],
   "source": [
    "scatter_df=mon_day_series.to_frame(name = 'size').reset_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='date_month', ylabel='size'>"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "scatter_df.plot(kind='scatter',x='date_month',y='size')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='month', ylabel='size'>"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAX4AAAEGCAYAAABiq/5QAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjQuMiwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8rg+JYAAAACXBIWXMAAAsTAAALEwEAmpwYAAAjXElEQVR4nO3dfXRV5Z0v8O83LwQUWjCklCtUuMLo0IrRnmuxtI74fq0LmKWdmU6tzrq68C7trVY7ou1dcx3vTK86rU5n2eWVqvWlLl8KHXHZNyjisrZFPShEhVoYUQmXlxihQoWY5PzuH2cfSUICCdnPbyfP/n7WiuZscs7zPPvs88vO/u3n99DMICIi+VGVdQdERMSXAr+ISM4o8IuI5IwCv4hIzijwi4jkTE3WHeiP8ePH25QpU7LuhojIsLJ69ep3zKyh5/ZhEfinTJmCYrGYdTdERIYVkm/1tl2XekREckaBX0QkZxT4RURyRoFfRCRnFPhFRHImeOAnWU3yZZJPJY+nknye5EaSj5EcEboPIiLDUeueNqzdvAute9pSfV2PM/6rAazv8vhWAHeY2TQAOwFc5tAHEZFhZemaLZh969O4+J7nMfvWp/Hkmi2pvXbQwE9yEoAvALgneUwAZwBYnPzIAwDmh+yDiMhw07qnDQuXNGFfewm72zqwr72E65c0pXbmH/qM/18BXA+glDyuB7DLzDqSx80Aju7tiSQXkCySLLa0tATupojI0NG8cy9qq7qH59qqKjTv3JvK6wcL/CQvALDDzFYfzvPNbJGZFcys0NBwwIxjEZFoTRo3Cu2lUrdt7aUSJo0blcrrhzzjnw1gLsk3ATyK8iWe7wEYS7JSKmISgPQuXImIRKB+dB1uu3AmRtZWYUxdDUbWVuG2C2eifnRdKq8frFaPmd0I4EYAIHk6gG+Y2ZdJ/hjARSj/MrgUwNJQfRARGa7mNh6N2dPGo3nnXkwaNyq1oA9kcx//QgDXktyI8jX/ezPog4jIkFc/ug4nTh6batAHnKpzmtkzAJ5Jvn8DwCke7YqIyIE0c1dEJGcU+EVEckaBX0QkZxT4RURyRoFfRCRnFPhFRHJGgV9EJGcU+EVEckaBX0QkZxT4RURyRoFfRCRnFPhFRHJGgV9EJGcU+EVEckaBX0QkZxT4RURyJuRi6yNJvkByLcnXSP5jsv1+kptIrkm+GkP1QUREDhRyBa42AGeY2R6StQCeI/nz5N/+3swWB2xbRET6EHKxdQOwJ3lYm3xZqPZERKR/gl7jJ1lNcg2AHQCWm9nzyT/9M8kmkneQTHcVYREROaiggd/MOs2sEcAkAKeQ/BSAGwEcD+C/ADgKwMLenktyAckiyWJLS0vIboqI5IrLXT1mtgvASgDnmdlWK2sD8EMAp/TxnEVmVjCzQkNDg0c3RURyIeRdPQ0kxybfjwJwNoDfk5yYbCOA+QBeDdUHERE5UMi7eiYCeIBkNcq/YB43s6dIPk2yAQABrAHw3wP2QUREegh5V08TgJN62X5GqDZFROTQNHNXRCRnFPhFRHJGgV9EJGcU+EVEckaBX0QkZxT4RURyRoFfRCRnFPhFRHJGgV9EJGcU+EVEckaBX0QkZxT4RURyRoFfRCRnFPhFRHJGgV9EJGcU+EVEckaBX0QkZ0KuuTuS5Ask15J8jeQ/Jtunknye5EaSj5EcEaoPIiJyoJBn/G0AzjCzEwE0AjiP5CwAtwK4w8ymAdgJ4LKAfRARkR6CBX4r25M8rE2+DMAZABYn2x8AMD9UH0RE5EBBr/GTrCa5BsAOAMsB/AeAXWbWkfxIM4Cj+3juApJFksWWlpaQ3RQRyZWggd/MOs2sEcAkAKcAOH4Az11kZgUzKzQ0NITqoohI7rjc1WNmuwCsBHAqgLEka5J/mgRgi0cfRIaK1j1tWLt5F1r3tGXdFcmpmkP/yOEh2QCg3cx2kRwF4GyUE7srAVwE4FEAlwJYGqoPIkPN0jVbsHBJE2qrqtBeKuG2C2dibmOvVztFggl5xj8RwEqSTQBeBLDczJ4CsBDAtSQ3AqgHcG/APogMGa172rBwSRP2tZewu60D+9pLuH5Jk878xV2wM34zawJwUi/b30D5er9IrjTv3IvaqirsQ+nDbbVVVWjeuRf1o+sy7JnkjWbuijiZNG4U2kulbtvaSyVMGjcqox5JXinwizipH12H2y6ciZG1VRhTV4ORtVW47cKZOtsXd8Eu9YjIgeY2Ho3Z08ajeedeTBo3SkFfMqHAL+KsfnSdAr5kSpd6RERyRoFfRCRnFPhFRHJGgV9EJGcU+EVEckaBX0QkZxT4RURyRoFfRCRnFPhFRHJGgV9EJGcU+EVEckaBX0QkZ4IFfpKTSa4kuY7kaySvTrbfRHILyTXJ1/mh+iAiIgcKWZ2zA8B1ZvYSyTEAVpNcnvzbHWb2nYBti4hIH0IuvbgVwNbk+90k1wPQqtIiIhlzucZPcgrK6+8+n2z6KskmkveRHNfHcxaQLJIstrS0eHRTRCQXggd+kqMBLAFwjZm9B+AuAMcCaET5L4Lv9vY8M1tkZgUzKzQ0NITupohIbgQN/CRrUQ76D5vZTwDAzLabWaeZlQD8AMApIfsgIiLdhbyrhwDuBbDezG7vsn1ilx/7SwCvhuqDiIgcKORdPbMBfAXAKyTXJNu+CeBLJBsBGIA3AVwRsA8iItJDyLt6ngPAXv7pZ6HaFBGRQ9PMXRGRnFHgH8Za97Rh7eZdaN3TlnVXUuc9tpj3pTfPfRn7+xZqfCGv8UtAS9dswcIlTaitqkJ7qYTbLpyJuY1xzI/zHlvM+9Kb576M/X0LOT6d8Q9DrXvasHBJE/a1l7C7rQP72ku4fklTFGc93mOLeV9689yXsb9vocenwD8MNe/ci9qq7m9dbVUVmnfuzahH6fEeW8z70pvnvoz9fQs9PgX+YWjSuFFoL5W6bWsvlTBp3KiMepQe77HFvC+9ee7L2N+30OOLOvDHmvipH12H2y6ciZG1VRhTV4ORtVW47cKZqB9dF6xNr33pPbYs9mWsPPdlpa26miocMaIadTVxvW+h92W0yd3YEz9zG4/G7Gnj0bxzLyaNGxX0gPfel55jy6K9mHnuS6v81/jho5iE3Jc0G/o7rFAoWLFY7PfPt+5pw+xbn8a+9v1/Ko2srcJvFp6hD/UAaV/KUKTjsn9IrjazQs/tUV7qiT3x40n7UoYiHZeDE2Xgjz3x40n7UoYiHZeD06/AT3ICyXtJ/jx5PIPkZWG7dviUsEuP9qUMRTouB6df1/iTgP9DAN8ysxNJ1gB42cxOCN1BYODX+Cta97QpYZcS7UsZijZu3401m3ehcfJYTJswJuvupG6wn7u+rvH3966e8Wb2OMkbAcDMOkh2DrgXzupH1ylIpUT7Uoaa2O/cGwolG/5Esh7JPVMkZwH4Yyo9EBEZIJVsGJz+nvFfB+BJAMeS/A2ABgBfTKUHIiIDVLmrZx/2J3grd/XE8Jdp6PH1K/Cb2WqSfwHgOJQXV3ndzNoP9hySkwE8CGACyn8pLDKz75E8CsBjAKagvALXX5nZzsMegYjkTux39QyJkg0k/wPA5Wb2mpm9ambtJJ86xNM6AFxnZjMAzAJwFckZAG4AsMLMpgNYkTyWIS7W8hcVsY8vNrHf1RN6fP29q+f3ANYCeB/AFWb2AcmXzeykfjdELgVwZ/J1upltTRZef8bMjjvYcw/3rh5Jh5JoMlTFfrdZqLt6+pvcfd/M/hrAegC/JvkJDKA4BskpAE4C8DyACWa2NfmnbShfCpIhSkk0GcrqR9fhxMljowz6QLjx9TfwEwDM7DYA3wKwDMCkfj2RHA1gCYBrzOy9rv9m5T83ev0FQnIBySLJYktLSz+7KWmLfWp87OMT6U1/A/8/VL4xs18BOBflSzYHRbIW5aD/sJn9JNm8PbnEg+T/O3p7rpktMrOCmRUaGhr62U1Jm5JoIvE5aOAneXzy7RaSJ1e+ANQDOGhylyQB3AtgvZnd3uWfngRwafL9pQCWHlbPxcX+uufEEbXVqKthlEk0z7ruG7fvxuLiZmzcvjtYG1nyTJRrXx6eQ93OeS2ABQC+22Vb10szZxzkubMBfAXAKyTXJNu+CeAWAI8ntX7eAvBXA+mw+Cu/4Sxf8DNm25kAPOu6/8MTr+DBVW9/+PiSUz+Bm+e5VD5x4Zko1748fAc94zezBcm3dwGYZ2ZzAKxEedbuNw7x3OfMjGY208wak6+fmVmrmZ1pZtPN7CwzezeVkUgQleRnW0cJ73/QibaOuJKf+8dneL+9E20dFmx8G7fv7haoAODB370dzdmqZ6Jc+3Jw+nuN/3+a2XskP4fyWf49KP8ykMjFnvz0HN+azbsGtH240b5Mz1BZbL1SkO0LAH5gZj8FMCKVHsiQFnvy03N8jZPHDmj7cKN9mZ4hMXMX5eTu3QD+GsDPSNYN4Lm5EePsT82QTM+0CWNwyamf6LbtklM/EU05Ye3L9AyVmbtHADgPwCtmtiG5DfMEM1uWSi8OYTjM3I199qdmSKZHNeTTo315cH3N3I1ysXVvWvhZRIaiXC227i32BKiIxEWBPwWxJ0BFJC4K/CmIPQGahRgT5RUxj02Gh/6uwCWHMLfxaMyeNj7qBKiXmBPlMY9Nhg+d8aco9hKxHmIukxzz2GR4UeCXISXmRHnMY5PhRYFfhpSYE+Uxj02GFwX+FHkn7TxL0nqNLeZEeewlriuUvE5PcVMrbl/2OoqbWlN9XSV3U+KdtPMsSes9tpgT5bGXuFbyOj0X37MKz20sB/x/e3ojPj+tHg9dPiuV19YZfwq8k3aeJWmzSkjGmCjPS4lrJa8Hr7ip9cOgX/Hrja2pnfkr8KfAO2nnWZJWCcn0xL4vYx+fp2c3vDOg7QOlwJ8C76SdZ0laJSTTE/u+jH18nk6bPn5A2wcqWOAneR/JHSRf7bLtJpJbSK5Jvs4P1b4n73VbPUvSVsY2opqoq6nCiOo4E5IesljfF4g7MR9rIrkwtR6fn1bfbdvnp9WjMLW+j2cMTMjk7v0A7gTwYI/td5jZdwK2mwnPdVu7NOii+Oa7+KDTPmy0+Na7StgdJu/jJObEfOyJ5Icun4XiplY8u+EdnDZ9fGpBHwh4xm9mzwLIxXq6nuu2Ar7J3djXNvXkfZzEnJjPSyK5MLUe155zXKpBH8jmGv9XSTYll4LG9fVDJBeQLJIstrS0ePZvwGJO7sa+tqkn7+Mk5mRrzGPz4B347wJwLIBGAFsBfLevHzSzRWZWMLNCQ0ODU/cOT8zJ3djXNvXkfZzEnGyNeWweXAO/mW03s04zKwH4AYBTPNsPxXtGpmdyN/a1TSs8koTeyc+sZgrHuC+zEmpfus7cJTnRzLYmD/8SwKsH+/nhxH1GpmNy9+Z5J+CSWVOiXdvUM0noPSvZ+7iMeV96C7kvg625S/IRAKcDGA9gO4D/lTxuRPl4fBPAFV1+EfRJa+52t3H7bpx1x7MHbP/V10+LLiiHFvN6yd5ji3lfektrX/a15m6wM34z+1Ivm+8N1V6WKommfdj/JlUSTSEO+IMlXBX4B8b7vfPkPbaY96W30PtSM3dTEHNyN3YxJwmVTB6+Qu/LqAN/qJKmPcWc3M2KV8npLJKEsR6XXWcmV740c/fwhD4uoy3LHLKkaW+8k2gxJ1w9S04DvknC2I/L4pvvoq1j/5lqyFnesc/cDXlcRnnGH7qkaU9ZldudNmEMLipMjiroZzVT2GO2aezHZR7KhXsLdVxGGfhDlzTtSbMI0xPzTOHYj0uVCx8+ogz8oUua9qSkVnpiTlzHflyqXPjwEWXgD13StKesZhHGmNiKOXGd1XE5opqorWLwktpZlAuvIVBNoIZw+cx5JeZDiza5e1FhMp7ftL846BcLk4O25z2LMObE1qePOQqPvvA2iCoYSigcc1TWXUpNyFK7vblz5YakpHbZ91duCHqceN50cOfKDejoMv809Ni8E/MhRXnGX0n8fNBpH37FUo4WiDuxtf+9A9o6S/igE9GMrSJUqd2eVqzbhj9s/1O3ba9v/xNWrNsWtF2Pmw68x+admA8tysAfe+In5vHFPDZvy9ZtH9D24cR7bN6J+dCiDPyxJ35iHl/MY/N2zowJA9o+nHiPzTsxH1qUgX9/Uguoq67CiGqfxI/32qYxzpDM6r3zmins6cwZH8dxE47stu24CUfizBkfD9qux7HiPTbvxHxFFGWZPZXXiQWQFDkKvU6sd7I15hmS3u+d90xhT1fOmY7rHlsDI0ADrpozPWh7nsfKlXOm4+9/vBZmABl+bN43jITcl1Ge8XvP/vROtsY8Q9L7vYt5TeHKe9dhQGcJ6LCwiXLPY6XrDRztpfA3cHjfMBJ6X0YZ+L1nf2qGZHq837uYZwrHvMZvzGPzaC/KwO89+1MzJNPj/d7FPFM45rLMMY/No71ggZ/kfSR3kHy1y7ajSC4nuSH5/7gQbXvP/uyabD1iRHXwZGsWMyS9ZiV7v3dZzBT2LjntdVx6Hisxj82jvZDJ3fsB3AngwS7bbgCwwsxuIXlD8nhhiMa9Z39a5b9GuCyI67jmrvvapo5jA3yPFe9Esvdx6XmsxDy20O0FO+M3s2cBvNtj8zwADyTfPwBgfoi2vWd/7i9/a3i/vRNtHWETP1kkJL1mJWeVmPc4VrIam9dxWeFxrMQ8No/2vK/xT+iyuPo2AH3OtiC5gGSRZLGlpWVAjcSWiOkp5oRkzIn5mMfmLeaxecgsuWtmhoP8fWZmi8ysYGaFhoaGAb12bImYnmJOSMacmI95bN5iHpsH78C/neREAEj+vyNEI1klfrxm0sZcujirxLzHTOHsbjrwWXO3wmPmbl5Koa9Ytw0LF69Nvfic98zdJwFcCuCW5P9LQzXknfjxnEkLAG+0dK9MuKnH4+HMe2yuM4WdE9fea+56ztyNvRT6OXc882EF0seKzThuwpH45ddPT+W1Q97O+QiA3wE4jmQzyctQDvhnk9wA4KzkcepiT7bGViK2K++xeb532SV3fdbczaJceKyl0EOXnQ55V8+XzGyimdWa2SQzu9fMWs3sTDObbmZnmVnPu35SEXuyNbYSsV15j83zvYs9uRtzwtV7bKHLTkc5czf2ZGtsJWK78h6b53sXe3I35oSr99hCl52OMvB7J7W8k3ZZlIh94qXNuPyBF/HES5uDtQH4j83zvcsuce2z5q73TRWevMcWuux0lIEf6JHUQvikVjZJOx+zvr0c1zzehF+t34FrHm/Cqd9eHrS9qeO7H/BTG47s4yfT8eljjsKIaqKupgojqhl05m65rf13EIWeUV5Zc7dSwfL7KzcEbW//TRX7H8XCe2wNY0Z2e/yxHo8HI8rA753Uijm5+8RLm7HtvQ+6bdv63gfBzvyzm7lraOsoBS236z2j3Htd2qxm03rwHlvoz3iUgV/J3fQ89UrvQaKv7YMVcwI0tgRhT0rupif0ZzzKwK/kbnouOKH3a4p9bR+smBOgsSUIe1JyNz2hP+NRBv7YSwl7JkDnnzwZEz8yotu2iR8Zgfknh1l2LqsEqGcpYa/j0ntd2qxm03rwHlvozzjLJXOGtkKhYMViccDPa93T5ldKGOXrcs9ueAenTR8ffBFmoHz9/alXtuGCEz4eLBBn0RZQvta/ZvMuNE4e61KKwrM97+PkR7/dhKVNWzFv5kRc/NmpwdtbsW4blq3bjnNmTHBZ2N3zMz7cYgrJ1WZWOGB7zIHfk/d07pgXCPfm+d55v2/e7V18z6puScnPT6vHQ5fPCtKW92duOOor8Ed5qcdb7AuSx8zzvYt9IXnPu82yKA8REwX+FMR+F1HMYq7HH/PdZjHfQeRBgT8Fsd9FFLOY6/HHfLdZzHcQeYg68IeqZd1TJeNfmR9MhKvpDsS9QHiFV93zyntXk0zyrmE89fhjvtus8r7VVgHVVUBtVdjPXIVX6ZKKUJ8D73r8bkLWsu7NwsVrP5zEbcnjoIkmx5y8d4LQO2m3cPFadCT7s8PCvnfu6yhEXErkzpUb0J6c9HcC+P7KDUGPk1nfXv7hLPZfrd+BW3/xe/zum2cHay/k5yDKM37vqep3r9yAvR3dD/m9HYa7A9VFyUMNea+kned7F/NaA4Dv+Lw/496lS0J/DqIM/N5T1Z9o2jqg7YOlGvLp8XzvYl5rAPAdn/dn3Lt0SejPQSaBn+SbJF8huYZk6jfoe09Vnz9z4oC2D5ZqyKfH872Lea0BwHd83p9x79IloT8HWZ7xzzGzxt4mFwyW91T1K+ZMx6ia7qWfR9UQV8yZHqS9PNSQr60CaqsYPGnn+d7FvNYA4Ds+78+4d+mSbmspVKe/lkKUl3qAsLWse3PLRSd2u6vn1otODNqeZw157wTh48XNaC8B7SVDewn4cTHsHRST64/o9vgTPR6n6aLC5G6/1L5YCFv+Ytlr3S9FLH8t7B1uxTffPejjNHl/xo/9WPdfmNM+Fra0x+PFzeW1FDrLaymk+TnIKvAbgGUkV5NckPaLeyfRKomYrnf1eCxq7VFDPuYEIeCbJKy8b11/qYU8TrwTkjEnymNrL6vA/zkzOxnAfwVwFcnTev4AyQUkiySLLS0tA3px7yRazItax5wgBHyThN7HiXdCMuZEeWztZRL4zWxL8v8dAP4dwCm9/MwiMyuYWaGhoWFAr++dRIt5UeuYE4SAb5LQ+zjxTkjGnCiPrT33wE/ySJJjKt8DOAfAq2m24Z1E6zr7s7oq7OzPru15LKIdc4IQ8E0S7n/f9q+5G/I48U5Ixpwoj629LM74JwB4juRaAC8A+KmZ/SLtRrbv3tft8Y4ej9N258oN6DCgs1Se/Rl6UWvPRbQf6XGNv+fjtHkvtu6ZJCy++W63NXeLb4VLfgLADefPKJ+QsHxCcuP5M4K2t/6fzseN5/4Z/nziGNx47p9h/T+dH6wt78/4hh3d81obd4QtX/LQ5bOw+IpZ+NoZ07D4ilmplrd2D/xm9oaZnZh8fdLM/jntNrxn9cXc3h2/XI/2Hnf1tFt5ewgxJ5OzmgXdYUCnlU9IPEoXXzFnOn5+9WnBbmcG4p+5W1GYWo9rzzku9b8soryd03tWX8ztLW3q/YPU1/bBijmZHPssaE+xz9wNLcrA7z2rL+b25s3s/Vp3X9sHK+ZkcuyzoD3FPnM3tCgDv/esvpjb+/q5f47a7vk61LK8PYSYk8lZzYL2uumgoripFbcvez3YPe5A/DN3K0LtyygDPwB8pscH9zP/Oeyi1lfOmY7aKqAmqQ1+VcDrm4BvQvIzx3bfd7OODbsvV73R/SB//o1wAQTwTRIufXnLQR+nzfumg4vvWYWL7l6Ff3t6Iy66exW+cs+qYG15f8a9VycPuS+jDPxZJdHaS0BHCcFnZHomJGOeSevd3o9+uwl/3NfZbdsf93XiR7/dlHpbgP++jDlR7p3cjXXmblCxJ9E8E5Ixz6T1bm9pHzNY+9o+WN77MuZEuXdyN8qZu6HFnkTzTEjGPJPWu715fcxg7Wv7YHnvy5gT5d7J3ehm7nrIKokWqoRqT54JyZhn0nq3d/Fnp+KjI6u7bfvoyGpc/NmpqbcF+O/LmBPl3sndGGfuuvBe2zRkCdXeeJb39Z5Jm0VivuudLyET8zfPPwHVKH/wqgH87/nh1i4G/Pel67HinG09u8cvzLM/GfZWzpA3HUQZ+GMrodqTZ3lf7yRaprNbS2Fnt1ba6gRQQnmB8JA3AcT83sU8NiB8Yj7KwB9bCdWeYi7LHHNi3vsmgJjfu5jHBoRPzEcZ+GMrodpTzGWZY07Me98EEPN7F/PYgPCJ+SgDf2wlVHuqJJNH1lZhTF0NRtZWRVOWOavEvMe+rLRVV1OFI0ZUo64mXFtA3O9dzGMDwifma1J5lSHIu2TrQ5fPQnFTK57d8A5Omz4+WNCvmNt4NGZPG4/mnXsxadyooNPwP33MUXjsxWYQ5Xxa0PV90fs6sTfPC5cE9dyXVvmvER7ZyU8fcxQefeFtEFUwlIK/d+X2NoMkzCxoe95ju3neCbhk1hSs2bwLjZPHBgv6FVfOmY5vPL4WBgPBVG86iPKM33vGYkWoEqp9qR9dhxMnjw0aqCoJybaOEvZ1lNDWUYpqndgK331peL+9E20d4dZK7tpe1/r/sawF7T22imkTxuCiwuTgQX//DRyWVANId19GGfi9ZyzGLPZ1Yj3FvDazd3sxl5wGwo8vysDvPWMxZrGvE+sp5rWZvduLueQ0EH58mQR+kueRfJ3kRpI3pP363jMWY+aZ/ASyK3/rwXtfxtye99i8hR4fzXynv5GsBvAHAGcDaAbwIoAvmdm6vp5TKBSsWCwOuK0V67Zh2brtOGfGBAX9QWrd0+aS/Kx44qXNeOqVbbjghI9HEfS78t6XMbfnPTZvgx0fydVmVjhgewaB/1QAN5nZucnjGwHAzP5PX8853MAvIpJnfQX+LC71HA2g6y0azcm2bkguIFkkWWxpaXHrnIhI7IZsctfMFplZwcwKDQ0NWXdHRCQaWQT+LQC6XrSdlGwTEREHWQT+FwFMJzmV5AgAfwPgyQz6ISKSS+4lG8ysg+RXAfwS5ZLk95nZa979EBHJK/e7eg4HyRYAb2Xdj34aDyBMPebsxTw2IO7xaWzD12DGd4yZHZAkHRaBfzghWezt9qkYxDw2IO7xaWzDV4jxDdm7ekREJAwFfhGRnFHgT9+irDsQUMxjA+Ien8Y2fKU+Pl3jFxHJGZ3xi4jkjAK/iEjOKPCngORkkitJriP5Gsmrs+5T2khWk3yZ5FNZ9yVtJMeSXEzy9yTXJxVko0Dy68kx+SrJR0iOzLpPg0HyPpI7SL7aZdtRJJeT3JD8f1yWfTxcfYztX5Ljsonkv5Mcm0ZbCvzp6ABwnZnNADALwFUkZ2Tcp7RdDWB91p0I5HsAfmFmxwM4EZGMk+TRAL4GoGBmn0J5pvzfZNurQbsfwHk9tt0AYIWZTQewInk8HN2PA8e2HMCnzGwmyuuY3JhGQwr8KTCzrWb2UvL9bpQDxwGlpocrkpMAfAHAPVn3JW0kPwrgNAD3AoCZfWBmuzLtVLpqAIwiWQPgCAD/L+P+DIqZPQvg3R6b5wF4IPn+AQDzPfuUlt7GZmbLzKwjebgK5aKWg6bAnzKSUwCcBOD5jLuSpn8FcD2A0iF+bjiaCqAFwA+TS1n3kDzyUE8aDsxsC4DvAHgbwFYAfzSzZdn2KogJZrY1+X4bgFgX1/5vAH6exgsp8KeI5GgASwBcY2bvZd2fNJC8AMAOM1uddV8CqQFwMoC7zOwkAH/C8L1U0E1yrXseyr/c/hOAI0lenG2vwrLy/enR3aNO8lsoX1J+OI3XU+BPCclalIP+w2b2k6z7k6LZAOaSfBPAowDOIPmjbLuUqmYAzWZW+QttMcq/CGJwFoBNZtZiZu0AfgLgsxn3KYTtJCcCQPL/HRn3J1Uk/w7ABQC+bClNvFLgTwFJonyNeL2Z3Z51f9JkZjea2SQzm4JyYvBpM4vmrNHMtgHYTPK4ZNOZANZl2KU0vQ1gFskjkmP0TESSuO7hSQCXJt9fCmBphn1JFcnzUL7MOtfM3k/rdRX40zEbwFdQPhtek3ydn3WnpN/+B4CHSTYBaATw7Wy7k47kr5jFAF4C8ArKn/dhXd6A5CMAfgfgOJLNJC8DcAuAs0luQPmvnFuy7OPh6mNsdwIYA2B5Elf+byptqWSDiEi+6IxfRCRnFPhFRHJGgV9EJGcU+EVEckaBX0QkZxT4RQJIKn5e2eXx6TFWNpXhSYFfJIyxAK481A+JZEGBX3KP5JSk5vn9JP9A8mGSZ5H8TVLj/ZSk5vsTSV30VSRnJs+9Kamj/gzJN0h+LXnZWwAcm0y6+Zdk2+gudf8fTmbTiriryboDIkPENABfRLkC4osA/hbA5wDMBfBNAJsBvGxm80meAeBBlGf5AsDxAOagPMPydZJ3oVzo7VNm1giUL/WgXLX1kyiXRv4NyjO+nws+MpEedMYvUrbJzF4xsxKA11Be2MNQLnUwBeVfAg8BgJk9DaCe5EeS5/7UzNrM7B2UC4T1VRb4BTNrTtpYk7yuiDsFfpGyti7fl7o8LuHQfxl3fW7nQX6+vz8nEpQCv0j//BrAl4EPL9u8c4g1F3ajfOlHZMjRGYdI/9wE4L6kguf72F8GuFdm1pokh19FedWkn4bvokj/qDqniEjO6FKPiEjOKPCLiOSMAr+ISM4o8IuI5IwCv4hIzijwi4jkjAK/iEjO/H8t2ClbjmgGVgAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "scatter_df.plot(kind='scatter',x='month',y='size')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "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>X</th>\n",
       "      <th>Y</th>\n",
       "      <th>RowID</th>\n",
       "      <th>CaseNumber</th>\n",
       "      <th>CreatedDate</th>\n",
       "      <th>ModifiedDate</th>\n",
       "      <th>LastName</th>\n",
       "      <th>FirstName</th>\n",
       "      <th>MiddleName</th>\n",
       "      <th>DateOfBirth</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Race</th>\n",
       "      <th>FullAddress</th>\n",
       "      <th>City</th>\n",
       "      <th>State</th>\n",
       "      <th>ZipCode</th>\n",
       "      <th>District</th>\n",
       "      <th>Post</th>\n",
       "      <th>Neighborhood</th>\n",
       "      <th>Latitude</th>\n",
       "      <th>Longitude</th>\n",
       "      <th>GeoLocation</th>\n",
       "      <th>Shape</th>\n",
       "      <th>age</th>\n",
       "      <th>Age</th>\n",
       "      <th>month</th>\n",
       "      <th>date_month</th>\n",
       "      <th>year</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>758</th>\n",
       "      <td>1420365.750</td>\n",
       "      <td>598860.5000</td>\n",
       "      <td>759</td>\n",
       "      <td>19-0678</td>\n",
       "      <td>2019-12-26</td>\n",
       "      <td>2021-06-15</td>\n",
       "      <td>Sutoon</td>\n",
       "      <td>Juan</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1993-10-03</td>\n",
       "      <td>Male</td>\n",
       "      <td>Black</td>\n",
       "      <td>124 W Trenton St</td>\n",
       "      <td>Baltimore</td>\n",
       "      <td>Md</td>\n",
       "      <td>21201</td>\n",
       "      <td>Central</td>\n",
       "      <td>134</td>\n",
       "      <td>Charles North</td>\n",
       "      <td>39.3104</td>\n",
       "      <td>-76.6182</td>\n",
       "      <td>(39.3104,-76.6182)</td>\n",
       "      <td>NaN</td>\n",
       "      <td>9580 days</td>\n",
       "      <td>26.229149</td>\n",
       "      <td>12</td>\n",
       "      <td>26</td>\n",
       "      <td>2019</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>759</th>\n",
       "      <td>1435344.375</td>\n",
       "      <td>602679.1250</td>\n",
       "      <td>760</td>\n",
       "      <td>19-0677</td>\n",
       "      <td>2019-12-26</td>\n",
       "      <td>2021-01-14</td>\n",
       "      <td>Caldwell-Jackson</td>\n",
       "      <td>Nyheem</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2001-05-22</td>\n",
       "      <td>Male</td>\n",
       "      <td>Black</td>\n",
       "      <td>3522 Pelham Ave</td>\n",
       "      <td>Baltimore</td>\n",
       "      <td>Md</td>\n",
       "      <td>21213</td>\n",
       "      <td>Northeast</td>\n",
       "      <td>432</td>\n",
       "      <td>Belair-Edison</td>\n",
       "      <td>39.3207</td>\n",
       "      <td>-76.5652</td>\n",
       "      <td>(39.3207,-76.5652)</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6792 days</td>\n",
       "      <td>18.595864</td>\n",
       "      <td>12</td>\n",
       "      <td>26</td>\n",
       "      <td>2019</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>760</th>\n",
       "      <td>1410320.750</td>\n",
       "      <td>583959.6250</td>\n",
       "      <td>761</td>\n",
       "      <td>19-0676</td>\n",
       "      <td>2019-12-26</td>\n",
       "      <td>2020-12-07</td>\n",
       "      <td>Shrout</td>\n",
       "      <td>Dustin</td>\n",
       "      <td>Edward</td>\n",
       "      <td>1999-02-24</td>\n",
       "      <td>Male</td>\n",
       "      <td>White</td>\n",
       "      <td>1601 Sexton St</td>\n",
       "      <td>Baltimore</td>\n",
       "      <td>Md</td>\n",
       "      <td>21230</td>\n",
       "      <td>Southwest</td>\n",
       "      <td>831</td>\n",
       "      <td>Morrell Park</td>\n",
       "      <td>39.2696</td>\n",
       "      <td>-76.6539</td>\n",
       "      <td>(39.2696,-76.6539)</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7610 days</td>\n",
       "      <td>20.835472</td>\n",
       "      <td>12</td>\n",
       "      <td>26</td>\n",
       "      <td>2019</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>761</th>\n",
       "      <td>1409321.500</td>\n",
       "      <td>608796.8125</td>\n",
       "      <td>762</td>\n",
       "      <td>19-0675</td>\n",
       "      <td>2019-12-26</td>\n",
       "      <td>2020-12-07</td>\n",
       "      <td>Curbeam</td>\n",
       "      <td>Larry</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2000-12-08</td>\n",
       "      <td>Male</td>\n",
       "      <td>Black</td>\n",
       "      <td>2302 Oswego Ave</td>\n",
       "      <td>Baltimore</td>\n",
       "      <td>Md</td>\n",
       "      <td>21215</td>\n",
       "      <td>Northern</td>\n",
       "      <td>533</td>\n",
       "      <td>Greenspring</td>\n",
       "      <td>39.3378</td>\n",
       "      <td>-76.6571</td>\n",
       "      <td>(39.3378,-76.6571)</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6957 days</td>\n",
       "      <td>19.047619</td>\n",
       "      <td>12</td>\n",
       "      <td>26</td>\n",
       "      <td>2019</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>762</th>\n",
       "      <td>1440317.000</td>\n",
       "      <td>609733.1875</td>\n",
       "      <td>763</td>\n",
       "      <td>19-0674</td>\n",
       "      <td>2019-12-26</td>\n",
       "      <td>2021-08-26</td>\n",
       "      <td>Lingham</td>\n",
       "      <td>Xzavin</td>\n",
       "      <td>Vernon</td>\n",
       "      <td>2000-12-28</td>\n",
       "      <td>Male</td>\n",
       "      <td>Black</td>\n",
       "      <td>4219 Berger Ave</td>\n",
       "      <td>Baltimore</td>\n",
       "      <td>Md</td>\n",
       "      <td>21206</td>\n",
       "      <td>Northeast</td>\n",
       "      <td>444</td>\n",
       "      <td>Frankford</td>\n",
       "      <td>39.3400</td>\n",
       "      <td>-76.5475</td>\n",
       "      <td>(39.34,-76.5475)</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6937 days</td>\n",
       "      <td>18.992861</td>\n",
       "      <td>12</td>\n",
       "      <td>26</td>\n",
       "      <td>2019</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>763</th>\n",
       "      <td>1416776.000</td>\n",
       "      <td>576918.8125</td>\n",
       "      <td>764</td>\n",
       "      <td>19-0673</td>\n",
       "      <td>2019-12-26</td>\n",
       "      <td>2021-07-23</td>\n",
       "      <td>Johnson</td>\n",
       "      <td>Donyae</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1998-05-07</td>\n",
       "      <td>Male</td>\n",
       "      <td>Black</td>\n",
       "      <td>1216 Shellbanks Rd</td>\n",
       "      <td>Baltimore</td>\n",
       "      <td>Md</td>\n",
       "      <td>21225</td>\n",
       "      <td>Southern</td>\n",
       "      <td>922</td>\n",
       "      <td>Cherry Hill</td>\n",
       "      <td>39.2502</td>\n",
       "      <td>-76.6312</td>\n",
       "      <td>(39.2502,-76.6312)</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7903 days</td>\n",
       "      <td>21.637679</td>\n",
       "      <td>12</td>\n",
       "      <td>26</td>\n",
       "      <td>2019</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>764</th>\n",
       "      <td>1432793.875</td>\n",
       "      <td>603577.6875</td>\n",
       "      <td>765</td>\n",
       "      <td>19-0672</td>\n",
       "      <td>2019-12-26</td>\n",
       "      <td>2020-12-07</td>\n",
       "      <td>Thomas</td>\n",
       "      <td>Glen</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1987-01-02</td>\n",
       "      <td>Male</td>\n",
       "      <td>Black</td>\n",
       "      <td>2826 Mayfield Ave</td>\n",
       "      <td>Baltimore</td>\n",
       "      <td>Md</td>\n",
       "      <td>21213</td>\n",
       "      <td>Northeast</td>\n",
       "      <td>432</td>\n",
       "      <td>Belair-Edison</td>\n",
       "      <td>39.3232</td>\n",
       "      <td>-76.5742</td>\n",
       "      <td>(39.3232,-76.5742)</td>\n",
       "      <td>NaN</td>\n",
       "      <td>12046 days</td>\n",
       "      <td>32.980828</td>\n",
       "      <td>12</td>\n",
       "      <td>26</td>\n",
       "      <td>2019</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>765</th>\n",
       "      <td>1413712.625</td>\n",
       "      <td>534765.7500</td>\n",
       "      <td>766</td>\n",
       "      <td>19-0671</td>\n",
       "      <td>2019-12-26</td>\n",
       "      <td>2020-12-07</td>\n",
       "      <td>Smith</td>\n",
       "      <td>Corey</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1994-02-26</td>\n",
       "      <td>Male</td>\n",
       "      <td>Black</td>\n",
       "      <td>7930 E Park Dr, Glen Burnie, 21061</td>\n",
       "      <td>Glen Burnie</td>\n",
       "      <td>Md</td>\n",
       "      <td>21060</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>39.1345</td>\n",
       "      <td>-76.6426</td>\n",
       "      <td>(39.1345,-76.6426)</td>\n",
       "      <td>NaN</td>\n",
       "      <td>9434 days</td>\n",
       "      <td>25.829415</td>\n",
       "      <td>12</td>\n",
       "      <td>26</td>\n",
       "      <td>2019</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>766</th>\n",
       "      <td>1425119.500</td>\n",
       "      <td>598844.3750</td>\n",
       "      <td>767</td>\n",
       "      <td>19-0670</td>\n",
       "      <td>2019-12-26</td>\n",
       "      <td>2020-12-07</td>\n",
       "      <td>Curry</td>\n",
       "      <td>George</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1958-10-30</td>\n",
       "      <td>Male</td>\n",
       "      <td>Black</td>\n",
       "      <td>1233 E Lafayette Ave</td>\n",
       "      <td>Baltimore</td>\n",
       "      <td>Md</td>\n",
       "      <td>21202</td>\n",
       "      <td>Eastern</td>\n",
       "      <td>314</td>\n",
       "      <td>Oliver</td>\n",
       "      <td>39.3103</td>\n",
       "      <td>-76.6014</td>\n",
       "      <td>(39.3103,-76.6014)</td>\n",
       "      <td>NaN</td>\n",
       "      <td>22337 days</td>\n",
       "      <td>61.156629</td>\n",
       "      <td>12</td>\n",
       "      <td>26</td>\n",
       "      <td>2019</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>767</th>\n",
       "      <td>1406273.500</td>\n",
       "      <td>606927.9375</td>\n",
       "      <td>768</td>\n",
       "      <td>19-0669</td>\n",
       "      <td>2019-12-26</td>\n",
       "      <td>2021-08-11</td>\n",
       "      <td>Davis</td>\n",
       "      <td>Icham</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1979-11-14</td>\n",
       "      <td>Male</td>\n",
       "      <td>Black</td>\n",
       "      <td>4047 Edgewood Rd</td>\n",
       "      <td>Baltimore</td>\n",
       "      <td>Md</td>\n",
       "      <td>21215</td>\n",
       "      <td>Northwest</td>\n",
       "      <td>625</td>\n",
       "      <td>East Arlington</td>\n",
       "      <td>39.3327</td>\n",
       "      <td>-76.6679</td>\n",
       "      <td>(39.3327,-76.6679)</td>\n",
       "      <td>NaN</td>\n",
       "      <td>14652 days</td>\n",
       "      <td>40.115813</td>\n",
       "      <td>12</td>\n",
       "      <td>26</td>\n",
       "      <td>2019</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1423</th>\n",
       "      <td>1381960.500</td>\n",
       "      <td>612387.3750</td>\n",
       "      <td>1424</td>\n",
       "      <td>18-0725</td>\n",
       "      <td>2018-12-26</td>\n",
       "      <td>2020-12-07</td>\n",
       "      <td>Mcclain</td>\n",
       "      <td>Aaron</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1992-04-13</td>\n",
       "      <td>Male</td>\n",
       "      <td>Black</td>\n",
       "      <td>3423 Merle Dr, Windsor Mill, 21244</td>\n",
       "      <td>Windsor Mill</td>\n",
       "      <td>Md</td>\n",
       "      <td>21206</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>39.3479</td>\n",
       "      <td>-76.7538</td>\n",
       "      <td>(39.3479,-76.7538)</td>\n",
       "      <td>NaN</td>\n",
       "      <td>9753 days</td>\n",
       "      <td>26.702807</td>\n",
       "      <td>12</td>\n",
       "      <td>26</td>\n",
       "      <td>2018</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1424</th>\n",
       "      <td>1401335.500</td>\n",
       "      <td>595181.9375</td>\n",
       "      <td>1425</td>\n",
       "      <td>18-0724</td>\n",
       "      <td>2018-12-26</td>\n",
       "      <td>2021-07-14</td>\n",
       "      <td>Jones</td>\n",
       "      <td>Devin</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1982-09-03</td>\n",
       "      <td>Male</td>\n",
       "      <td>Black</td>\n",
       "      <td>1246 N Augusta Ave</td>\n",
       "      <td>Baltimore</td>\n",
       "      <td>Md</td>\n",
       "      <td>21229</td>\n",
       "      <td>Southwest</td>\n",
       "      <td>815</td>\n",
       "      <td>Edmondson Village</td>\n",
       "      <td>39.3005</td>\n",
       "      <td>-76.6855</td>\n",
       "      <td>(39.3005,-76.6855)</td>\n",
       "      <td>NaN</td>\n",
       "      <td>13263 days</td>\n",
       "      <td>36.312861</td>\n",
       "      <td>12</td>\n",
       "      <td>26</td>\n",
       "      <td>2018</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1425</th>\n",
       "      <td>1441535.000</td>\n",
       "      <td>586755.7500</td>\n",
       "      <td>1426</td>\n",
       "      <td>18-0723</td>\n",
       "      <td>2018-12-26</td>\n",
       "      <td>2020-12-07</td>\n",
       "      <td>Rhodes</td>\n",
       "      <td>Larry</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1982-10-27</td>\n",
       "      <td>Male</td>\n",
       "      <td>Black</td>\n",
       "      <td>1437 Broening Hwy</td>\n",
       "      <td>Baltimore</td>\n",
       "      <td>Md</td>\n",
       "      <td>21224</td>\n",
       "      <td>Southeast</td>\n",
       "      <td>231</td>\n",
       "      <td>Medford</td>\n",
       "      <td>39.2769</td>\n",
       "      <td>-76.5436</td>\n",
       "      <td>(39.2769,-76.5436)</td>\n",
       "      <td>NaN</td>\n",
       "      <td>13209 days</td>\n",
       "      <td>36.165014</td>\n",
       "      <td>12</td>\n",
       "      <td>26</td>\n",
       "      <td>2018</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1426</th>\n",
       "      <td>1406875.500</td>\n",
       "      <td>612466.5625</td>\n",
       "      <td>1427</td>\n",
       "      <td>18-0722</td>\n",
       "      <td>2018-12-26</td>\n",
       "      <td>2021-05-04</td>\n",
       "      <td>Davis</td>\n",
       "      <td>Kevin</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1988-08-21</td>\n",
       "      <td>Male</td>\n",
       "      <td>Black</td>\n",
       "      <td>4812 Laurel Ave</td>\n",
       "      <td>Baltimore</td>\n",
       "      <td>Md</td>\n",
       "      <td>21215</td>\n",
       "      <td>Northern</td>\n",
       "      <td>532</td>\n",
       "      <td>Cylburn</td>\n",
       "      <td>39.3479</td>\n",
       "      <td>-76.6657</td>\n",
       "      <td>(39.3479,-76.6657)</td>\n",
       "      <td>NaN</td>\n",
       "      <td>11084 days</td>\n",
       "      <td>30.346961</td>\n",
       "      <td>12</td>\n",
       "      <td>26</td>\n",
       "      <td>2018</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1427</th>\n",
       "      <td>1427260.625</td>\n",
       "      <td>613678.4375</td>\n",
       "      <td>1428</td>\n",
       "      <td>18-0721</td>\n",
       "      <td>2018-12-26</td>\n",
       "      <td>2020-12-07</td>\n",
       "      <td>Lee</td>\n",
       "      <td>Damon</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1983-09-13</td>\n",
       "      <td>Male</td>\n",
       "      <td>Black</td>\n",
       "      <td>1409 Kitmore Rd</td>\n",
       "      <td>Baltimore</td>\n",
       "      <td>Md</td>\n",
       "      <td>21239</td>\n",
       "      <td>Northeast</td>\n",
       "      <td>413</td>\n",
       "      <td>New Northwood</td>\n",
       "      <td>39.3510</td>\n",
       "      <td>-76.5936</td>\n",
       "      <td>(39.351,-76.5936)</td>\n",
       "      <td>NaN</td>\n",
       "      <td>12888 days</td>\n",
       "      <td>35.286146</td>\n",
       "      <td>12</td>\n",
       "      <td>26</td>\n",
       "      <td>2018</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1428</th>\n",
       "      <td>1401533.250</td>\n",
       "      <td>595291.9375</td>\n",
       "      <td>1429</td>\n",
       "      <td>18-0720</td>\n",
       "      <td>2018-12-26</td>\n",
       "      <td>2021-04-15</td>\n",
       "      <td>Jones</td>\n",
       "      <td>David</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaT</td>\n",
       "      <td>Male</td>\n",
       "      <td>Black</td>\n",
       "      <td>4119 Mountwood Rd</td>\n",
       "      <td>Baltimore</td>\n",
       "      <td>Md</td>\n",
       "      <td>21229</td>\n",
       "      <td>Southwest</td>\n",
       "      <td>815</td>\n",
       "      <td>Edmondson Village</td>\n",
       "      <td>39.3008</td>\n",
       "      <td>-76.6848</td>\n",
       "      <td>(39.3008,-76.6848)</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaT</td>\n",
       "      <td>NaN</td>\n",
       "      <td>12</td>\n",
       "      <td>26</td>\n",
       "      <td>2018</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1429</th>\n",
       "      <td>1412865.500</td>\n",
       "      <td>599340.1250</td>\n",
       "      <td>1430</td>\n",
       "      <td>18-0719</td>\n",
       "      <td>2018-12-26</td>\n",
       "      <td>2020-12-07</td>\n",
       "      <td>Stewart</td>\n",
       "      <td>Dashon</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1990-10-14</td>\n",
       "      <td>Male</td>\n",
       "      <td>Black</td>\n",
       "      <td>2646 Pennsylvania Ave</td>\n",
       "      <td>Baltimore</td>\n",
       "      <td>Md</td>\n",
       "      <td>21217</td>\n",
       "      <td>Western</td>\n",
       "      <td>733</td>\n",
       "      <td>Penn North</td>\n",
       "      <td>39.3118</td>\n",
       "      <td>-76.6447</td>\n",
       "      <td>(39.3118,-76.6447)</td>\n",
       "      <td>NaN</td>\n",
       "      <td>10300 days</td>\n",
       "      <td>28.200442</td>\n",
       "      <td>12</td>\n",
       "      <td>26</td>\n",
       "      <td>2018</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1430</th>\n",
       "      <td>1384286.625</td>\n",
       "      <td>609844.0625</td>\n",
       "      <td>1431</td>\n",
       "      <td>18-0718</td>\n",
       "      <td>2018-12-26</td>\n",
       "      <td>2020-12-07</td>\n",
       "      <td>Carpintieri</td>\n",
       "      <td>Mancino</td>\n",
       "      <td>Paul</td>\n",
       "      <td>1996-08-28</td>\n",
       "      <td>Male</td>\n",
       "      <td>Black</td>\n",
       "      <td>7013 Bexhill Road, Windsor Mill, 21244</td>\n",
       "      <td>Windsor Mill</td>\n",
       "      <td>Md</td>\n",
       "      <td>21244</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>39.3409</td>\n",
       "      <td>-76.7456</td>\n",
       "      <td>(39.3409,-76.7456)</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8155 days</td>\n",
       "      <td>22.327632</td>\n",
       "      <td>12</td>\n",
       "      <td>26</td>\n",
       "      <td>2018</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1431</th>\n",
       "      <td>1407713.500</td>\n",
       "      <td>592327.2500</td>\n",
       "      <td>1432</td>\n",
       "      <td>18-0717</td>\n",
       "      <td>2018-12-26</td>\n",
       "      <td>2021-04-02</td>\n",
       "      <td>Wilson</td>\n",
       "      <td>Ellsworth</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1978-09-14</td>\n",
       "      <td>Male</td>\n",
       "      <td>Black</td>\n",
       "      <td>2823 W Mulberry St</td>\n",
       "      <td>Baltimore</td>\n",
       "      <td>Md</td>\n",
       "      <td>21223</td>\n",
       "      <td>Southwest</td>\n",
       "      <td>835</td>\n",
       "      <td>Penrose/Fayette Street Outreach</td>\n",
       "      <td>39.2926</td>\n",
       "      <td>-76.6630</td>\n",
       "      <td>(39.2926,-76.663)</td>\n",
       "      <td>NaN</td>\n",
       "      <td>14713 days</td>\n",
       "      <td>40.282826</td>\n",
       "      <td>12</td>\n",
       "      <td>26</td>\n",
       "      <td>2018</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1432</th>\n",
       "      <td>1424391.000</td>\n",
       "      <td>603722.0000</td>\n",
       "      <td>1433</td>\n",
       "      <td>18-0716</td>\n",
       "      <td>2018-12-26</td>\n",
       "      <td>2021-05-04</td>\n",
       "      <td>Dunbar</td>\n",
       "      <td>Walter</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1988-05-19</td>\n",
       "      <td>Male</td>\n",
       "      <td>Black</td>\n",
       "      <td>2926 Independence St</td>\n",
       "      <td>Baltimore</td>\n",
       "      <td>Md</td>\n",
       "      <td>21218</td>\n",
       "      <td>Northern</td>\n",
       "      <td>513</td>\n",
       "      <td>Better Waverly</td>\n",
       "      <td>39.3237</td>\n",
       "      <td>-76.6039</td>\n",
       "      <td>(39.3237,-76.6039)</td>\n",
       "      <td>NaN</td>\n",
       "      <td>11178 days</td>\n",
       "      <td>30.604325</td>\n",
       "      <td>12</td>\n",
       "      <td>26</td>\n",
       "      <td>2018</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1433</th>\n",
       "      <td>1432902.250</td>\n",
       "      <td>598551.6875</td>\n",
       "      <td>1434</td>\n",
       "      <td>18-0715</td>\n",
       "      <td>2018-12-26</td>\n",
       "      <td>2020-12-07</td>\n",
       "      <td>Prout</td>\n",
       "      <td>Donta</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1995-11-20</td>\n",
       "      <td>Male</td>\n",
       "      <td>Black</td>\n",
       "      <td>3102 E Federal St</td>\n",
       "      <td>Baltimore</td>\n",
       "      <td>Md</td>\n",
       "      <td>21213</td>\n",
       "      <td>Eastern</td>\n",
       "      <td>332</td>\n",
       "      <td>Berea</td>\n",
       "      <td>39.3094</td>\n",
       "      <td>-76.5739</td>\n",
       "      <td>(39.3094,-76.5739)</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8437 days</td>\n",
       "      <td>23.099721</td>\n",
       "      <td>12</td>\n",
       "      <td>26</td>\n",
       "      <td>2018</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1434</th>\n",
       "      <td>1413831.000</td>\n",
       "      <td>591257.8750</td>\n",
       "      <td>1435</td>\n",
       "      <td>18-0714</td>\n",
       "      <td>2018-12-26</td>\n",
       "      <td>2021-07-20</td>\n",
       "      <td>Graham</td>\n",
       "      <td>Donald</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1970-11-22</td>\n",
       "      <td>Male</td>\n",
       "      <td>Black</td>\n",
       "      <td>1525 W Fayette St</td>\n",
       "      <td>Baltimore</td>\n",
       "      <td>Md</td>\n",
       "      <td>21223</td>\n",
       "      <td>Western</td>\n",
       "      <td>711</td>\n",
       "      <td>Franklin Square</td>\n",
       "      <td>39.2896</td>\n",
       "      <td>-76.6414</td>\n",
       "      <td>(39.2896,-76.6414)</td>\n",
       "      <td>NaN</td>\n",
       "      <td>17566 days</td>\n",
       "      <td>48.094074</td>\n",
       "      <td>12</td>\n",
       "      <td>26</td>\n",
       "      <td>2018</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1435</th>\n",
       "      <td>1409701.875</td>\n",
       "      <td>597907.5000</td>\n",
       "      <td>1436</td>\n",
       "      <td>18-0713</td>\n",
       "      <td>2018-12-26</td>\n",
       "      <td>2020-12-07</td>\n",
       "      <td>Johnson</td>\n",
       "      <td>Tyrone</td>\n",
       "      <td>Dominick</td>\n",
       "      <td>1992-03-21</td>\n",
       "      <td>Male</td>\n",
       "      <td>Black</td>\n",
       "      <td>1732 Moreland Ave</td>\n",
       "      <td>Baltimore</td>\n",
       "      <td>Md</td>\n",
       "      <td>21216</td>\n",
       "      <td>Western</td>\n",
       "      <td>723</td>\n",
       "      <td>Coppin Heights/Ash-Co-East</td>\n",
       "      <td>39.3079</td>\n",
       "      <td>-76.6559</td>\n",
       "      <td>(39.3079,-76.6559)</td>\n",
       "      <td>NaN</td>\n",
       "      <td>9776 days</td>\n",
       "      <td>26.765779</td>\n",
       "      <td>12</td>\n",
       "      <td>26</td>\n",
       "      <td>2018</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1436</th>\n",
       "      <td>1430533.125</td>\n",
       "      <td>615405.1250</td>\n",
       "      <td>1437</td>\n",
       "      <td>18-0712</td>\n",
       "      <td>2018-12-26</td>\n",
       "      <td>2021-06-02</td>\n",
       "      <td>Blackstone</td>\n",
       "      <td>Michael</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1977-07-03</td>\n",
       "      <td>Male</td>\n",
       "      <td>Black</td>\n",
       "      <td>1702 Woodbourne Ave</td>\n",
       "      <td>Baltimore</td>\n",
       "      <td>Md</td>\n",
       "      <td>21239</td>\n",
       "      <td>Northeast</td>\n",
       "      <td>415</td>\n",
       "      <td>Loch Raven</td>\n",
       "      <td>39.3557</td>\n",
       "      <td>-76.5820</td>\n",
       "      <td>(39.3557,-76.582)</td>\n",
       "      <td>NaN</td>\n",
       "      <td>15151 days</td>\n",
       "      <td>41.482029</td>\n",
       "      <td>12</td>\n",
       "      <td>26</td>\n",
       "      <td>2018</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1437</th>\n",
       "      <td>1401907.875</td>\n",
       "      <td>601485.2500</td>\n",
       "      <td>1438</td>\n",
       "      <td>18-0710</td>\n",
       "      <td>2018-12-26</td>\n",
       "      <td>2021-05-27</td>\n",
       "      <td>Thomas</td>\n",
       "      <td>Carlos</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1997-03-04</td>\n",
       "      <td>Male</td>\n",
       "      <td>Black</td>\n",
       "      <td>3915 Bateman Ave</td>\n",
       "      <td>Baltimore</td>\n",
       "      <td>Md</td>\n",
       "      <td>21216</td>\n",
       "      <td>Northwest</td>\n",
       "      <td>624</td>\n",
       "      <td>Windsor Hills</td>\n",
       "      <td>39.3178</td>\n",
       "      <td>-76.6834</td>\n",
       "      <td>(39.3178,-76.6834)</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7967 days</td>\n",
       "      <td>21.812905</td>\n",
       "      <td>12</td>\n",
       "      <td>26</td>\n",
       "      <td>2018</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1438</th>\n",
       "      <td>1412880.750</td>\n",
       "      <td>580873.5000</td>\n",
       "      <td>1439</td>\n",
       "      <td>18-0709</td>\n",
       "      <td>2018-12-26</td>\n",
       "      <td>2021-08-17</td>\n",
       "      <td>Taylor</td>\n",
       "      <td>Cavontae</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2000-05-26</td>\n",
       "      <td>Male</td>\n",
       "      <td>Black</td>\n",
       "      <td>2629 Huron St</td>\n",
       "      <td>Baltimore</td>\n",
       "      <td>Md</td>\n",
       "      <td>21230</td>\n",
       "      <td>Southern</td>\n",
       "      <td>921</td>\n",
       "      <td>Mount Winans</td>\n",
       "      <td>39.2611</td>\n",
       "      <td>-76.6449</td>\n",
       "      <td>(39.2611,-76.6449)</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6788 days</td>\n",
       "      <td>18.584913</td>\n",
       "      <td>12</td>\n",
       "      <td>26</td>\n",
       "      <td>2018</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1439</th>\n",
       "      <td>1406372.750</td>\n",
       "      <td>595199.8125</td>\n",
       "      <td>1440</td>\n",
       "      <td>18-0708</td>\n",
       "      <td>2018-12-26</td>\n",
       "      <td>2020-12-07</td>\n",
       "      <td>Stanley</td>\n",
       "      <td>Michael</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1988-12-23</td>\n",
       "      <td>Male</td>\n",
       "      <td>Black</td>\n",
       "      <td>3009 Lyttleton Rd</td>\n",
       "      <td>Baltimore</td>\n",
       "      <td>Md</td>\n",
       "      <td>21216</td>\n",
       "      <td>Southwest</td>\n",
       "      <td>814</td>\n",
       "      <td>Franklintown Road</td>\n",
       "      <td>39.3005</td>\n",
       "      <td>-76.6677</td>\n",
       "      <td>(39.3005,-76.6677)</td>\n",
       "      <td>NaN</td>\n",
       "      <td>10960 days</td>\n",
       "      <td>30.007461</td>\n",
       "      <td>12</td>\n",
       "      <td>26</td>\n",
       "      <td>2018</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1440</th>\n",
       "      <td>1409586.625</td>\n",
       "      <td>598453.4375</td>\n",
       "      <td>1441</td>\n",
       "      <td>18-0707</td>\n",
       "      <td>2018-12-26</td>\n",
       "      <td>2021-07-19</td>\n",
       "      <td>Washington</td>\n",
       "      <td>Jerard</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1974-09-03</td>\n",
       "      <td>Male</td>\n",
       "      <td>Black</td>\n",
       "      <td>2425 W North Ave</td>\n",
       "      <td>Baltimore</td>\n",
       "      <td>Md</td>\n",
       "      <td>21216</td>\n",
       "      <td>Western</td>\n",
       "      <td>731</td>\n",
       "      <td>Coppin Heights/Ash-Co-East</td>\n",
       "      <td>39.3094</td>\n",
       "      <td>-76.6563</td>\n",
       "      <td>(39.3094,-76.6563)</td>\n",
       "      <td>NaN</td>\n",
       "      <td>16185 days</td>\n",
       "      <td>44.313025</td>\n",
       "      <td>12</td>\n",
       "      <td>26</td>\n",
       "      <td>2018</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1441</th>\n",
       "      <td>1408229.375</td>\n",
       "      <td>598193.3750</td>\n",
       "      <td>1442</td>\n",
       "      <td>18-0706</td>\n",
       "      <td>2018-12-26</td>\n",
       "      <td>2020-12-07</td>\n",
       "      <td>Holloway</td>\n",
       "      <td>Jonathan</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1999-01-05</td>\n",
       "      <td>Male</td>\n",
       "      <td>Black</td>\n",
       "      <td>1816 Braddish Ave</td>\n",
       "      <td>Baltimore</td>\n",
       "      <td>Md</td>\n",
       "      <td>21216</td>\n",
       "      <td>Western</td>\n",
       "      <td>723</td>\n",
       "      <td>Coppin Heights/Ash-Co-East</td>\n",
       "      <td>39.3087</td>\n",
       "      <td>-76.6611</td>\n",
       "      <td>(39.3087,-76.6611)</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7295 days</td>\n",
       "      <td>19.973032</td>\n",
       "      <td>12</td>\n",
       "      <td>26</td>\n",
       "      <td>2018</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1442</th>\n",
       "      <td>1370979.875</td>\n",
       "      <td>628277.4375</td>\n",
       "      <td>1443</td>\n",
       "      <td>18-0705</td>\n",
       "      <td>2018-12-26</td>\n",
       "      <td>2021-06-02</td>\n",
       "      <td>Ward</td>\n",
       "      <td>Norman</td>\n",
       "      <td>Murace</td>\n",
       "      <td>1993-02-09</td>\n",
       "      <td>Male</td>\n",
       "      <td>Black</td>\n",
       "      <td>9347 Owings Choice Ct, Owings Mills, 21117</td>\n",
       "      <td>Owings Mills</td>\n",
       "      <td>Md</td>\n",
       "      <td>21117</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>39.3916</td>\n",
       "      <td>-76.7925</td>\n",
       "      <td>(39.3916,-76.7925)</td>\n",
       "      <td>NaN</td>\n",
       "      <td>9451 days</td>\n",
       "      <td>25.875959</td>\n",
       "      <td>12</td>\n",
       "      <td>26</td>\n",
       "      <td>2018</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1443</th>\n",
       "      <td>1424860.625</td>\n",
       "      <td>612793.6875</td>\n",
       "      <td>1444</td>\n",
       "      <td>18-0704</td>\n",
       "      <td>2018-12-26</td>\n",
       "      <td>2021-07-21</td>\n",
       "      <td>Watson</td>\n",
       "      <td>Lamar</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1992-11-08</td>\n",
       "      <td>Male</td>\n",
       "      <td>Black</td>\n",
       "      <td>5017 Saint Georges Ave</td>\n",
       "      <td>Baltimore</td>\n",
       "      <td>Md</td>\n",
       "      <td>21212</td>\n",
       "      <td>Northern</td>\n",
       "      <td>524</td>\n",
       "      <td>Kenilworth Park</td>\n",
       "      <td>39.3486</td>\n",
       "      <td>-76.6021</td>\n",
       "      <td>(39.3486,-76.6021)</td>\n",
       "      <td>NaN</td>\n",
       "      <td>9544 days</td>\n",
       "      <td>26.130584</td>\n",
       "      <td>12</td>\n",
       "      <td>26</td>\n",
       "      <td>2018</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1444</th>\n",
       "      <td>1411671.875</td>\n",
       "      <td>593362.0625</td>\n",
       "      <td>1445</td>\n",
       "      <td>18-0703</td>\n",
       "      <td>2018-12-26</td>\n",
       "      <td>2021-05-04</td>\n",
       "      <td>Wilkins</td>\n",
       "      <td>Jamal</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1985-08-23</td>\n",
       "      <td>Male</td>\n",
       "      <td>Black</td>\n",
       "      <td>607 N Payson St</td>\n",
       "      <td>Baltimore</td>\n",
       "      <td>Md</td>\n",
       "      <td>21223</td>\n",
       "      <td>Western</td>\n",
       "      <td>722</td>\n",
       "      <td>Midtown-Edmondson</td>\n",
       "      <td>39.2954</td>\n",
       "      <td>-76.6490</td>\n",
       "      <td>(39.2954,-76.649)</td>\n",
       "      <td>NaN</td>\n",
       "      <td>12178 days</td>\n",
       "      <td>33.342232</td>\n",
       "      <td>12</td>\n",
       "      <td>26</td>\n",
       "      <td>2018</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1445</th>\n",
       "      <td>1413700.875</td>\n",
       "      <td>595591.7500</td>\n",
       "      <td>1446</td>\n",
       "      <td>18-0702</td>\n",
       "      <td>2018-12-26</td>\n",
       "      <td>2020-12-07</td>\n",
       "      <td>Brown</td>\n",
       "      <td>Willie</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1991-12-31</td>\n",
       "      <td>Male</td>\n",
       "      <td>Black</td>\n",
       "      <td>1132 N Stricker St</td>\n",
       "      <td>Baltimore</td>\n",
       "      <td>Md</td>\n",
       "      <td>21217</td>\n",
       "      <td>Western</td>\n",
       "      <td>724</td>\n",
       "      <td>Sandtown-Winchester</td>\n",
       "      <td>39.3015</td>\n",
       "      <td>-76.6418</td>\n",
       "      <td>(39.3015,-76.6418)</td>\n",
       "      <td>NaN</td>\n",
       "      <td>9857 days</td>\n",
       "      <td>26.987549</td>\n",
       "      <td>12</td>\n",
       "      <td>26</td>\n",
       "      <td>2018</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1446</th>\n",
       "      <td>1371824.625</td>\n",
       "      <td>641865.8125</td>\n",
       "      <td>1447</td>\n",
       "      <td>18-0701</td>\n",
       "      <td>2018-12-26</td>\n",
       "      <td>2020-12-07</td>\n",
       "      <td>Ruiz-Cruz</td>\n",
       "      <td>Cristian</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1997-06-27</td>\n",
       "      <td>Male</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>14 Enchanted Hills Rd, Md, 21117</td>\n",
       "      <td>Owings Mills</td>\n",
       "      <td>Md</td>\n",
       "      <td>21117</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>39.4289</td>\n",
       "      <td>-76.7894</td>\n",
       "      <td>(39.4289,-76.7894)</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7852 days</td>\n",
       "      <td>21.498046</td>\n",
       "      <td>12</td>\n",
       "      <td>26</td>\n",
       "      <td>2018</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1447</th>\n",
       "      <td>1431220.500</td>\n",
       "      <td>595083.6875</td>\n",
       "      <td>1448</td>\n",
       "      <td>18-0700</td>\n",
       "      <td>2018-12-26</td>\n",
       "      <td>2020-12-07</td>\n",
       "      <td>Green</td>\n",
       "      <td>Marquis</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1995-07-18</td>\n",
       "      <td>Male</td>\n",
       "      <td>Black</td>\n",
       "      <td>2635 E Madison St</td>\n",
       "      <td>Baltimore</td>\n",
       "      <td>Md</td>\n",
       "      <td>21205</td>\n",
       "      <td>Eastern</td>\n",
       "      <td>333</td>\n",
       "      <td>Madison-Eastend</td>\n",
       "      <td>39.2999</td>\n",
       "      <td>-76.5799</td>\n",
       "      <td>(39.2999,-76.5799)</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8562 days</td>\n",
       "      <td>23.441960</td>\n",
       "      <td>12</td>\n",
       "      <td>26</td>\n",
       "      <td>2018</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1448</th>\n",
       "      <td>1407349.250</td>\n",
       "      <td>598991.4375</td>\n",
       "      <td>1449</td>\n",
       "      <td>18-0699</td>\n",
       "      <td>2018-12-26</td>\n",
       "      <td>2021-08-17</td>\n",
       "      <td>Whitehead</td>\n",
       "      <td>Tevin</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1991-04-25</td>\n",
       "      <td>Male</td>\n",
       "      <td>Black</td>\n",
       "      <td>2006 N Dukeland St</td>\n",
       "      <td>Baltimore</td>\n",
       "      <td>Md</td>\n",
       "      <td>21216</td>\n",
       "      <td>Southwest</td>\n",
       "      <td>811</td>\n",
       "      <td>Walbrook</td>\n",
       "      <td>39.3109</td>\n",
       "      <td>-76.6642</td>\n",
       "      <td>(39.3109,-76.6642)</td>\n",
       "      <td>NaN</td>\n",
       "      <td>10107 days</td>\n",
       "      <td>27.672026</td>\n",
       "      <td>12</td>\n",
       "      <td>26</td>\n",
       "      <td>2018</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1449</th>\n",
       "      <td>1410941.375</td>\n",
       "      <td>599369.1250</td>\n",
       "      <td>1450</td>\n",
       "      <td>18-0698</td>\n",
       "      <td>2018-12-26</td>\n",
       "      <td>2020-12-07</td>\n",
       "      <td>Murray</td>\n",
       "      <td>James</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1998-05-02</td>\n",
       "      <td>Male</td>\n",
       "      <td>Black</td>\n",
       "      <td>2100 Clifton Ave</td>\n",
       "      <td>Baltimore</td>\n",
       "      <td>Md</td>\n",
       "      <td>21217</td>\n",
       "      <td>Western</td>\n",
       "      <td>731</td>\n",
       "      <td>Mondawmin</td>\n",
       "      <td>39.3119</td>\n",
       "      <td>-76.6515</td>\n",
       "      <td>(39.3119,-76.6515)</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7543 days</td>\n",
       "      <td>20.652033</td>\n",
       "      <td>12</td>\n",
       "      <td>26</td>\n",
       "      <td>2018</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1450</th>\n",
       "      <td>1426930.500</td>\n",
       "      <td>630687.2500</td>\n",
       "      <td>1451</td>\n",
       "      <td>18-0697</td>\n",
       "      <td>2018-12-26</td>\n",
       "      <td>2021-05-24</td>\n",
       "      <td>Cozart</td>\n",
       "      <td>Darius</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1999-02-22</td>\n",
       "      <td>Male</td>\n",
       "      <td>Black</td>\n",
       "      <td>303 Hendrickson Ln, Md, 21286</td>\n",
       "      <td>Towson</td>\n",
       "      <td>Md</td>\n",
       "      <td>21286</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>39.3977</td>\n",
       "      <td>-76.5945</td>\n",
       "      <td>(39.3977,-76.5945)</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7247 days</td>\n",
       "      <td>19.841612</td>\n",
       "      <td>12</td>\n",
       "      <td>26</td>\n",
       "      <td>2018</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1451</th>\n",
       "      <td>1429495.000</td>\n",
       "      <td>619844.1875</td>\n",
       "      <td>1452</td>\n",
       "      <td>18-0696</td>\n",
       "      <td>2018-12-26</td>\n",
       "      <td>2021-08-13</td>\n",
       "      <td>Hagwood</td>\n",
       "      <td>Anthony</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1987-06-11</td>\n",
       "      <td>Male</td>\n",
       "      <td>Black</td>\n",
       "      <td>1443 Meridene Dr</td>\n",
       "      <td>Baltimore</td>\n",
       "      <td>Md</td>\n",
       "      <td>21239</td>\n",
       "      <td>Northeast</td>\n",
       "      <td>414</td>\n",
       "      <td>Idlewood</td>\n",
       "      <td>39.3679</td>\n",
       "      <td>-76.5856</td>\n",
       "      <td>(39.3679,-76.5856)</td>\n",
       "      <td>NaN</td>\n",
       "      <td>11521 days</td>\n",
       "      <td>31.543427</td>\n",
       "      <td>12</td>\n",
       "      <td>26</td>\n",
       "      <td>2018</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1877</th>\n",
       "      <td>1423910.250</td>\n",
       "      <td>603683.5000</td>\n",
       "      <td>1878</td>\n",
       "      <td>17-0567</td>\n",
       "      <td>2017-12-26</td>\n",
       "      <td>2020-12-07</td>\n",
       "      <td>Johnson</td>\n",
       "      <td>Omanuel</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1988-07-13</td>\n",
       "      <td>Male</td>\n",
       "      <td>Black</td>\n",
       "      <td>841 E 30th St</td>\n",
       "      <td>Baltimore</td>\n",
       "      <td>Md</td>\n",
       "      <td>21218</td>\n",
       "      <td>Northern</td>\n",
       "      <td>513</td>\n",
       "      <td>Better Waverly</td>\n",
       "      <td>39.3236</td>\n",
       "      <td>-76.6056</td>\n",
       "      <td>(39.3236,-76.6056)</td>\n",
       "      <td>NaN</td>\n",
       "      <td>10758 days</td>\n",
       "      <td>29.454404</td>\n",
       "      <td>12</td>\n",
       "      <td>26</td>\n",
       "      <td>2017</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                X            Y  RowID CaseNumber CreatedDate ModifiedDate          LastName  FirstName MiddleName DateOfBirth Gender     Race                                 FullAddress          City State ZipCode   District Post                     Neighborhood  Latitude  Longitude         GeoLocation  Shape        age        Age  month  date_month  year\n",
       "758   1420365.750  598860.5000    759    19-0678  2019-12-26   2021-06-15            Sutoon       Juan        NaN  1993-10-03   Male    Black                            124 W Trenton St     Baltimore    Md   21201    Central  134                    Charles North   39.3104   -76.6182  (39.3104,-76.6182)    NaN  9580 days  26.229149     12          26  2019\n",
       "759   1435344.375  602679.1250    760    19-0677  2019-12-26   2021-01-14  Caldwell-Jackson     Nyheem        NaN  2001-05-22   Male    Black                             3522 Pelham Ave     Baltimore    Md   21213  Northeast  432                    Belair-Edison   39.3207   -76.5652  (39.3207,-76.5652)    NaN  6792 days  18.595864     12          26  2019\n",
       "760   1410320.750  583959.6250    761    19-0676  2019-12-26   2020-12-07            Shrout     Dustin     Edward  1999-02-24   Male    White                              1601 Sexton St     Baltimore    Md   21230  Southwest  831                     Morrell Park   39.2696   -76.6539  (39.2696,-76.6539)    NaN  7610 days  20.835472     12          26  2019\n",
       "761   1409321.500  608796.8125    762    19-0675  2019-12-26   2020-12-07           Curbeam      Larry        NaN  2000-12-08   Male    Black                             2302 Oswego Ave     Baltimore    Md   21215   Northern  533                      Greenspring   39.3378   -76.6571  (39.3378,-76.6571)    NaN  6957 days  19.047619     12          26  2019\n",
       "762   1440317.000  609733.1875    763    19-0674  2019-12-26   2021-08-26           Lingham     Xzavin     Vernon  2000-12-28   Male    Black                             4219 Berger Ave     Baltimore    Md   21206  Northeast  444                        Frankford   39.3400   -76.5475    (39.34,-76.5475)    NaN  6937 days  18.992861     12          26  2019\n",
       "763   1416776.000  576918.8125    764    19-0673  2019-12-26   2021-07-23           Johnson     Donyae        NaN  1998-05-07   Male    Black                          1216 Shellbanks Rd     Baltimore    Md   21225   Southern  922                      Cherry Hill   39.2502   -76.6312  (39.2502,-76.6312)    NaN  7903 days  21.637679     12          26  2019\n",
       "764   1432793.875  603577.6875    765    19-0672  2019-12-26   2020-12-07            Thomas       Glen        NaN  1987-01-02   Male    Black                           2826 Mayfield Ave     Baltimore    Md   21213  Northeast  432                    Belair-Edison   39.3232   -76.5742  (39.3232,-76.5742)    NaN 12046 days  32.980828     12          26  2019\n",
       "765   1413712.625  534765.7500    766    19-0671  2019-12-26   2020-12-07             Smith      Corey        NaN  1994-02-26   Male    Black          7930 E Park Dr, Glen Burnie, 21061   Glen Burnie    Md   21060        NaN  NaN                              NaN   39.1345   -76.6426  (39.1345,-76.6426)    NaN  9434 days  25.829415     12          26  2019\n",
       "766   1425119.500  598844.3750    767    19-0670  2019-12-26   2020-12-07             Curry     George        NaN  1958-10-30   Male    Black                        1233 E Lafayette Ave     Baltimore    Md   21202    Eastern  314                           Oliver   39.3103   -76.6014  (39.3103,-76.6014)    NaN 22337 days  61.156629     12          26  2019\n",
       "767   1406273.500  606927.9375    768    19-0669  2019-12-26   2021-08-11             Davis      Icham        NaN  1979-11-14   Male    Black                            4047 Edgewood Rd     Baltimore    Md   21215  Northwest  625                   East Arlington   39.3327   -76.6679  (39.3327,-76.6679)    NaN 14652 days  40.115813     12          26  2019\n",
       "1423  1381960.500  612387.3750   1424    18-0725  2018-12-26   2020-12-07           Mcclain      Aaron        NaN  1992-04-13   Male    Black          3423 Merle Dr, Windsor Mill, 21244  Windsor Mill    Md   21206        NaN  NaN                              NaN   39.3479   -76.7538  (39.3479,-76.7538)    NaN  9753 days  26.702807     12          26  2018\n",
       "1424  1401335.500  595181.9375   1425    18-0724  2018-12-26   2021-07-14             Jones      Devin        NaN  1982-09-03   Male    Black                          1246 N Augusta Ave     Baltimore    Md   21229  Southwest  815                Edmondson Village   39.3005   -76.6855  (39.3005,-76.6855)    NaN 13263 days  36.312861     12          26  2018\n",
       "1425  1441535.000  586755.7500   1426    18-0723  2018-12-26   2020-12-07            Rhodes      Larry        NaN  1982-10-27   Male    Black                           1437 Broening Hwy     Baltimore    Md   21224  Southeast  231                          Medford   39.2769   -76.5436  (39.2769,-76.5436)    NaN 13209 days  36.165014     12          26  2018\n",
       "1426  1406875.500  612466.5625   1427    18-0722  2018-12-26   2021-05-04             Davis      Kevin        NaN  1988-08-21   Male    Black                             4812 Laurel Ave     Baltimore    Md   21215   Northern  532                          Cylburn   39.3479   -76.6657  (39.3479,-76.6657)    NaN 11084 days  30.346961     12          26  2018\n",
       "1427  1427260.625  613678.4375   1428    18-0721  2018-12-26   2020-12-07               Lee      Damon        NaN  1983-09-13   Male    Black                             1409 Kitmore Rd     Baltimore    Md   21239  Northeast  413                    New Northwood   39.3510   -76.5936   (39.351,-76.5936)    NaN 12888 days  35.286146     12          26  2018\n",
       "1428  1401533.250  595291.9375   1429    18-0720  2018-12-26   2021-04-15             Jones      David        NaN         NaT   Male    Black                           4119 Mountwood Rd     Baltimore    Md   21229  Southwest  815                Edmondson Village   39.3008   -76.6848  (39.3008,-76.6848)    NaN        NaT        NaN     12          26  2018\n",
       "1429  1412865.500  599340.1250   1430    18-0719  2018-12-26   2020-12-07           Stewart     Dashon        NaN  1990-10-14   Male    Black                       2646 Pennsylvania Ave     Baltimore    Md   21217    Western  733                       Penn North   39.3118   -76.6447  (39.3118,-76.6447)    NaN 10300 days  28.200442     12          26  2018\n",
       "1430  1384286.625  609844.0625   1431    18-0718  2018-12-26   2020-12-07       Carpintieri    Mancino       Paul  1996-08-28   Male    Black      7013 Bexhill Road, Windsor Mill, 21244  Windsor Mill    Md   21244        NaN  NaN                              NaN   39.3409   -76.7456  (39.3409,-76.7456)    NaN  8155 days  22.327632     12          26  2018\n",
       "1431  1407713.500  592327.2500   1432    18-0717  2018-12-26   2021-04-02            Wilson  Ellsworth        NaN  1978-09-14   Male    Black                          2823 W Mulberry St     Baltimore    Md   21223  Southwest  835  Penrose/Fayette Street Outreach   39.2926   -76.6630   (39.2926,-76.663)    NaN 14713 days  40.282826     12          26  2018\n",
       "1432  1424391.000  603722.0000   1433    18-0716  2018-12-26   2021-05-04            Dunbar     Walter        NaN  1988-05-19   Male    Black                        2926 Independence St     Baltimore    Md   21218   Northern  513                   Better Waverly   39.3237   -76.6039  (39.3237,-76.6039)    NaN 11178 days  30.604325     12          26  2018\n",
       "1433  1432902.250  598551.6875   1434    18-0715  2018-12-26   2020-12-07             Prout      Donta        NaN  1995-11-20   Male    Black                           3102 E Federal St     Baltimore    Md   21213    Eastern  332                            Berea   39.3094   -76.5739  (39.3094,-76.5739)    NaN  8437 days  23.099721     12          26  2018\n",
       "1434  1413831.000  591257.8750   1435    18-0714  2018-12-26   2021-07-20            Graham     Donald        NaN  1970-11-22   Male    Black                           1525 W Fayette St     Baltimore    Md   21223    Western  711                  Franklin Square   39.2896   -76.6414  (39.2896,-76.6414)    NaN 17566 days  48.094074     12          26  2018\n",
       "1435  1409701.875  597907.5000   1436    18-0713  2018-12-26   2020-12-07           Johnson     Tyrone   Dominick  1992-03-21   Male    Black                           1732 Moreland Ave     Baltimore    Md   21216    Western  723       Coppin Heights/Ash-Co-East   39.3079   -76.6559  (39.3079,-76.6559)    NaN  9776 days  26.765779     12          26  2018\n",
       "1436  1430533.125  615405.1250   1437    18-0712  2018-12-26   2021-06-02        Blackstone    Michael        NaN  1977-07-03   Male    Black                         1702 Woodbourne Ave     Baltimore    Md   21239  Northeast  415                       Loch Raven   39.3557   -76.5820   (39.3557,-76.582)    NaN 15151 days  41.482029     12          26  2018\n",
       "1437  1401907.875  601485.2500   1438    18-0710  2018-12-26   2021-05-27            Thomas     Carlos        NaN  1997-03-04   Male    Black                            3915 Bateman Ave     Baltimore    Md   21216  Northwest  624                    Windsor Hills   39.3178   -76.6834  (39.3178,-76.6834)    NaN  7967 days  21.812905     12          26  2018\n",
       "1438  1412880.750  580873.5000   1439    18-0709  2018-12-26   2021-08-17            Taylor   Cavontae        NaN  2000-05-26   Male    Black                               2629 Huron St     Baltimore    Md   21230   Southern  921                     Mount Winans   39.2611   -76.6449  (39.2611,-76.6449)    NaN  6788 days  18.584913     12          26  2018\n",
       "1439  1406372.750  595199.8125   1440    18-0708  2018-12-26   2020-12-07           Stanley    Michael        NaN  1988-12-23   Male    Black                           3009 Lyttleton Rd     Baltimore    Md   21216  Southwest  814                Franklintown Road   39.3005   -76.6677  (39.3005,-76.6677)    NaN 10960 days  30.007461     12          26  2018\n",
       "1440  1409586.625  598453.4375   1441    18-0707  2018-12-26   2021-07-19        Washington     Jerard        NaN  1974-09-03   Male    Black                            2425 W North Ave     Baltimore    Md   21216    Western  731       Coppin Heights/Ash-Co-East   39.3094   -76.6563  (39.3094,-76.6563)    NaN 16185 days  44.313025     12          26  2018\n",
       "1441  1408229.375  598193.3750   1442    18-0706  2018-12-26   2020-12-07          Holloway   Jonathan        NaN  1999-01-05   Male    Black                           1816 Braddish Ave     Baltimore    Md   21216    Western  723       Coppin Heights/Ash-Co-East   39.3087   -76.6611  (39.3087,-76.6611)    NaN  7295 days  19.973032     12          26  2018\n",
       "1442  1370979.875  628277.4375   1443    18-0705  2018-12-26   2021-06-02              Ward     Norman     Murace  1993-02-09   Male    Black  9347 Owings Choice Ct, Owings Mills, 21117  Owings Mills    Md   21117        NaN  NaN                              NaN   39.3916   -76.7925  (39.3916,-76.7925)    NaN  9451 days  25.875959     12          26  2018\n",
       "1443  1424860.625  612793.6875   1444    18-0704  2018-12-26   2021-07-21            Watson      Lamar        NaN  1992-11-08   Male    Black                      5017 Saint Georges Ave     Baltimore    Md   21212   Northern  524                  Kenilworth Park   39.3486   -76.6021  (39.3486,-76.6021)    NaN  9544 days  26.130584     12          26  2018\n",
       "1444  1411671.875  593362.0625   1445    18-0703  2018-12-26   2021-05-04           Wilkins      Jamal        NaN  1985-08-23   Male    Black                             607 N Payson St     Baltimore    Md   21223    Western  722                Midtown-Edmondson   39.2954   -76.6490   (39.2954,-76.649)    NaN 12178 days  33.342232     12          26  2018\n",
       "1445  1413700.875  595591.7500   1446    18-0702  2018-12-26   2020-12-07             Brown     Willie        NaN  1991-12-31   Male    Black                          1132 N Stricker St     Baltimore    Md   21217    Western  724              Sandtown-Winchester   39.3015   -76.6418  (39.3015,-76.6418)    NaN  9857 days  26.987549     12          26  2018\n",
       "1446  1371824.625  641865.8125   1447    18-0701  2018-12-26   2020-12-07         Ruiz-Cruz   Cristian        NaN  1997-06-27   Male  Unknown            14 Enchanted Hills Rd, Md, 21117  Owings Mills    Md   21117        NaN  NaN                              NaN   39.4289   -76.7894  (39.4289,-76.7894)    NaN  7852 days  21.498046     12          26  2018\n",
       "1447  1431220.500  595083.6875   1448    18-0700  2018-12-26   2020-12-07             Green    Marquis        NaN  1995-07-18   Male    Black                           2635 E Madison St     Baltimore    Md   21205    Eastern  333                  Madison-Eastend   39.2999   -76.5799  (39.2999,-76.5799)    NaN  8562 days  23.441960     12          26  2018\n",
       "1448  1407349.250  598991.4375   1449    18-0699  2018-12-26   2021-08-17         Whitehead      Tevin        NaN  1991-04-25   Male    Black                          2006 N Dukeland St     Baltimore    Md   21216  Southwest  811                         Walbrook   39.3109   -76.6642  (39.3109,-76.6642)    NaN 10107 days  27.672026     12          26  2018\n",
       "1449  1410941.375  599369.1250   1450    18-0698  2018-12-26   2020-12-07            Murray      James        NaN  1998-05-02   Male    Black                            2100 Clifton Ave     Baltimore    Md   21217    Western  731                        Mondawmin   39.3119   -76.6515  (39.3119,-76.6515)    NaN  7543 days  20.652033     12          26  2018\n",
       "1450  1426930.500  630687.2500   1451    18-0697  2018-12-26   2021-05-24            Cozart     Darius        NaN  1999-02-22   Male    Black               303 Hendrickson Ln, Md, 21286        Towson    Md   21286        NaN  NaN                              NaN   39.3977   -76.5945  (39.3977,-76.5945)    NaN  7247 days  19.841612     12          26  2018\n",
       "1451  1429495.000  619844.1875   1452    18-0696  2018-12-26   2021-08-13           Hagwood    Anthony        NaN  1987-06-11   Male    Black                            1443 Meridene Dr     Baltimore    Md   21239  Northeast  414                         Idlewood   39.3679   -76.5856  (39.3679,-76.5856)    NaN 11521 days  31.543427     12          26  2018\n",
       "1877  1423910.250  603683.5000   1878    17-0567  2017-12-26   2020-12-07           Johnson    Omanuel        NaN  1988-07-13   Male    Black                               841 E 30th St     Baltimore    Md   21218   Northern  513                   Better Waverly   39.3236   -76.6056  (39.3236,-76.6056)    NaN 10758 days  29.454404     12          26  2017"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dec_26_df = df[ (df['date_month']==26) & (df['month']==12) ]\n",
    "dec_26_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [],
   "source": [
    "day_after_xmas=dec_26_df.groupby('year')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "21215    268\n",
       "21213    198\n",
       "21216    197\n",
       "21217    194\n",
       "21218    183\n",
       "        ... \n",
       "20902      1\n",
       "20711      1\n",
       "21702      1\n",
       "21144      1\n",
       "21286      1\n",
       "Name: ZipCode, Length: 69, dtype: int64"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['ZipCode'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dtype('float64')"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Age']=df['Age'].apply(np.floor)\n",
    "df.Age.dtype"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['Age']=df['Age'].astype(str).apply(lambda x: x.replace('.0',''))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['Latitude']=df['Latitude'].astype(str).apply(lambda x: x.replace('.0',''))\n",
    "df['Longitude']=df['Longitude'].astype(str).apply(lambda x: x.replace('.0',''))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['coordinates'] = list(zip(df.Latitude, df.Longitude))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "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>X</th>\n",
       "      <th>Y</th>\n",
       "      <th>RowID</th>\n",
       "      <th>CaseNumber</th>\n",
       "      <th>CreatedDate</th>\n",
       "      <th>ModifiedDate</th>\n",
       "      <th>LastName</th>\n",
       "      <th>FirstName</th>\n",
       "      <th>MiddleName</th>\n",
       "      <th>DateOfBirth</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Race</th>\n",
       "      <th>FullAddress</th>\n",
       "      <th>City</th>\n",
       "      <th>State</th>\n",
       "      <th>ZipCode</th>\n",
       "      <th>District</th>\n",
       "      <th>Post</th>\n",
       "      <th>Neighborhood</th>\n",
       "      <th>Latitude</th>\n",
       "      <th>Longitude</th>\n",
       "      <th>GeoLocation</th>\n",
       "      <th>Shape</th>\n",
       "      <th>age</th>\n",
       "      <th>Age</th>\n",
       "      <th>month</th>\n",
       "      <th>date_month</th>\n",
       "      <th>year</th>\n",
       "      <th>coordinates</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>988</th>\n",
       "      <td>1413348.375</td>\n",
       "      <td>598868.5</td>\n",
       "      <td>989</td>\n",
       "      <td>19-0442</td>\n",
       "      <td>2019-07-31</td>\n",
       "      <td>2021-03-29</td>\n",
       "      <td>Fauntleroy</td>\n",
       "      <td>Dayvon</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1992-09-03</td>\n",
       "      <td>Male</td>\n",
       "      <td>Black</td>\n",
       "      <td>2535 Pennsylvania Ave</td>\n",
       "      <td>Baltimore</td>\n",
       "      <td>Md</td>\n",
       "      <td>21217</td>\n",
       "      <td>Western</td>\n",
       "      <td>733</td>\n",
       "      <td>Penn North</td>\n",
       "      <td>39.3105</td>\n",
       "      <td>-76.643</td>\n",
       "      <td>(39.3105,-76.643)</td>\n",
       "      <td>NaN</td>\n",
       "      <td>9827 days</td>\n",
       "      <td>26</td>\n",
       "      <td>7</td>\n",
       "      <td>31</td>\n",
       "      <td>2019</td>\n",
       "      <td>(39.3105, -76.643)</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               X         Y  RowID CaseNumber CreatedDate ModifiedDate    LastName FirstName MiddleName DateOfBirth Gender   Race            FullAddress       City State ZipCode District Post Neighborhood Latitude Longitude        GeoLocation  Shape       age Age  month  date_month  year         coordinates\n",
       "988  1413348.375  598868.5    989    19-0442  2019-07-31   2021-03-29  Fauntleroy    Dayvon        NaN  1992-09-03   Male  Black  2535 Pennsylvania Ave  Baltimore    Md   21217  Western  733   Penn North  39.3105   -76.643  (39.3105,-76.643)    NaN 9827 days  26      7          31  2019  (39.3105, -76.643)"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.sample()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='year'>"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "years_by_count.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [],
   "source": [
    "years_by_count.to_csv('years_by_count.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "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>X</th>\n",
       "      <th>Y</th>\n",
       "      <th>RowID</th>\n",
       "      <th>CaseNumber</th>\n",
       "      <th>CreatedDate</th>\n",
       "      <th>ModifiedDate</th>\n",
       "      <th>LastName</th>\n",
       "      <th>FirstName</th>\n",
       "      <th>MiddleName</th>\n",
       "      <th>DateOfBirth</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Race</th>\n",
       "      <th>FullAddress</th>\n",
       "      <th>City</th>\n",
       "      <th>State</th>\n",
       "      <th>ZipCode</th>\n",
       "      <th>District</th>\n",
       "      <th>Post</th>\n",
       "      <th>Neighborhood</th>\n",
       "      <th>Latitude</th>\n",
       "      <th>Longitude</th>\n",
       "      <th>GeoLocation</th>\n",
       "      <th>Shape</th>\n",
       "      <th>age</th>\n",
       "      <th>Age</th>\n",
       "      <th>month</th>\n",
       "      <th>date_month</th>\n",
       "      <th>year</th>\n",
       "      <th>coordinates</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>1399110.875</td>\n",
       "      <td>608724.0625</td>\n",
       "      <td>46</td>\n",
       "      <td>21-0333</td>\n",
       "      <td>2021-08-11</td>\n",
       "      <td>2021-08-11</td>\n",
       "      <td>Blue</td>\n",
       "      <td>Parris</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1998-07-06</td>\n",
       "      <td>Male</td>\n",
       "      <td>Black</td>\n",
       "      <td>4402 Fernhill Ave</td>\n",
       "      <td>Baltimore</td>\n",
       "      <td>Md</td>\n",
       "      <td>21215</td>\n",
       "      <td>Northwest</td>\n",
       "      <td>621</td>\n",
       "      <td>West Arlington</td>\n",
       "      <td>39.3377</td>\n",
       "      <td>-76.6932</td>\n",
       "      <td>(39.3377,-76.6932)</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8437 days</td>\n",
       "      <td>23</td>\n",
       "      <td>8</td>\n",
       "      <td>11</td>\n",
       "      <td>2021</td>\n",
       "      <td>(39.3377, -76.6932)</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              X            Y  RowID CaseNumber CreatedDate ModifiedDate LastName FirstName MiddleName DateOfBirth Gender   Race        FullAddress       City State ZipCode   District Post    Neighborhood Latitude Longitude         GeoLocation  Shape       age Age  month  date_month  year          coordinates\n",
       "45  1399110.875  608724.0625     46    21-0333  2021-08-11   2021-08-11     Blue    Parris        NaN  1998-07-06   Male  Black  4402 Fernhill Ave  Baltimore    Md   21215  Northwest  621  West Arlington  39.3377  -76.6932  (39.3377,-76.6932)    NaN 8437 days  23      8          11  2021  (39.3377, -76.6932)"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_dist = df.dropna(subset=['District'])\n",
    "df_dist.sample()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [],
   "source": [
    "dist_year=df_dist.groupby(['year', 'District']).size()\n",
    "dist_year.to_csv('dist.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "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>X</th>\n",
       "      <th>Y</th>\n",
       "      <th>RowID</th>\n",
       "      <th>Shape</th>\n",
       "      <th>age</th>\n",
       "      <th>month</th>\n",
       "      <th>date_month</th>\n",
       "      <th>year</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>2.530000e+03</td>\n",
       "      <td>2530.000000</td>\n",
       "      <td>2748.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2747</td>\n",
       "      <td>2748.000000</td>\n",
       "      <td>2748.000000</td>\n",
       "      <td>2748.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>1.416622e+06</td>\n",
       "      <td>599202.187500</td>\n",
       "      <td>1374.500000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>10367 days 06:55:41.827448064</td>\n",
       "      <td>6.561863</td>\n",
       "      <td>16.382824</td>\n",
       "      <td>2017.789665</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>3.901699e+04</td>\n",
       "      <td>22016.828125</td>\n",
       "      <td>793.423594</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3221 days 18:06:28.849161504</td>\n",
       "      <td>3.412566</td>\n",
       "      <td>8.614320</td>\n",
       "      <td>2.823560</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>-1.720966e+00</td>\n",
       "      <td>11.321385</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>840 days 00:00:00</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2008.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>1.406518e+06</td>\n",
       "      <td>593003.562500</td>\n",
       "      <td>687.750000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7997 days 12:00:00</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>2017.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>1.416737e+06</td>\n",
       "      <td>599001.250000</td>\n",
       "      <td>1374.500000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>9584 days 00:00:00</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>16.000000</td>\n",
       "      <td>2019.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>1.428932e+06</td>\n",
       "      <td>607934.750000</td>\n",
       "      <td>2061.250000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>11747 days 12:00:00</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>24.000000</td>\n",
       "      <td>2020.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>1.653035e+06</td>\n",
       "      <td>729759.562500</td>\n",
       "      <td>2748.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>29323 days 00:00:00</td>\n",
       "      <td>12.000000</td>\n",
       "      <td>31.000000</td>\n",
       "      <td>2021.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  X              Y        RowID  Shape                            age        month   date_month         year\n",
       "count  2.530000e+03    2530.000000  2748.000000    0.0                           2747  2748.000000  2748.000000  2748.000000\n",
       "mean   1.416622e+06  599202.187500  1374.500000    NaN  10367 days 06:55:41.827448064     6.561863    16.382824  2017.789665\n",
       "std    3.901699e+04   22016.828125   793.423594    NaN   3221 days 18:06:28.849161504     3.412566     8.614320     2.823560\n",
       "min   -1.720966e+00      11.321385     1.000000    NaN              840 days 00:00:00     1.000000     1.000000  2008.000000\n",
       "25%    1.406518e+06  593003.562500   687.750000    NaN             7997 days 12:00:00     4.000000    10.000000  2017.000000\n",
       "50%    1.416737e+06  599001.250000  1374.500000    NaN             9584 days 00:00:00     7.000000    16.000000  2019.000000\n",
       "75%    1.428932e+06  607934.750000  2061.250000    NaN            11747 days 12:00:00    10.000000    24.000000  2020.000000\n",
       "max    1.653035e+06  729759.562500  2748.000000    NaN            29323 days 00:00:00    12.000000    31.000000  2021.000000"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [],
   "source": [
    "# districts by count\n",
    "# dist_by_count=df_dist[['District','year', 'RowID']].groupby('District', 'year').agg('count')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.to_csv('data.csv', index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "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>X</th>\n",
       "      <th>Y</th>\n",
       "      <th>RowID</th>\n",
       "      <th>CaseNumber</th>\n",
       "      <th>CreatedDate</th>\n",
       "      <th>ModifiedDate</th>\n",
       "      <th>LastName</th>\n",
       "      <th>FirstName</th>\n",
       "      <th>MiddleName</th>\n",
       "      <th>DateOfBirth</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Race</th>\n",
       "      <th>FullAddress</th>\n",
       "      <th>City</th>\n",
       "      <th>State</th>\n",
       "      <th>ZipCode</th>\n",
       "      <th>District</th>\n",
       "      <th>Post</th>\n",
       "      <th>Neighborhood</th>\n",
       "      <th>Latitude</th>\n",
       "      <th>Longitude</th>\n",
       "      <th>GeoLocation</th>\n",
       "      <th>Shape</th>\n",
       "      <th>age</th>\n",
       "      <th>Age</th>\n",
       "      <th>month</th>\n",
       "      <th>date_month</th>\n",
       "      <th>year</th>\n",
       "      <th>coordinates</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>516</th>\n",
       "      <td>1428932.375</td>\n",
       "      <td>612957.4375</td>\n",
       "      <td>517</td>\n",
       "      <td>20-0226</td>\n",
       "      <td>2020-08-20</td>\n",
       "      <td>2021-08-25</td>\n",
       "      <td>Smallwood</td>\n",
       "      <td>Devin</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1991-04-13</td>\n",
       "      <td>Male</td>\n",
       "      <td>Black</td>\n",
       "      <td>1569 Winston Ave</td>\n",
       "      <td>Baltimore</td>\n",
       "      <td>Md</td>\n",
       "      <td>21239</td>\n",
       "      <td>Northeast</td>\n",
       "      <td>413</td>\n",
       "      <td>Stonewood-Pentwood-Winston</td>\n",
       "      <td>39.349</td>\n",
       "      <td>-76.5877</td>\n",
       "      <td>(39.349,-76.5877)</td>\n",
       "      <td>NaN</td>\n",
       "      <td>10722 days</td>\n",
       "      <td>29</td>\n",
       "      <td>8</td>\n",
       "      <td>20</td>\n",
       "      <td>2020</td>\n",
       "      <td>(39.349, -76.5877)</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               X            Y  RowID CaseNumber CreatedDate ModifiedDate   LastName FirstName MiddleName DateOfBirth Gender   Race       FullAddress       City State ZipCode   District Post                Neighborhood Latitude Longitude        GeoLocation  Shape        age Age  month  date_month  year         coordinates\n",
       "516  1428932.375  612957.4375    517    20-0226  2020-08-20   2021-08-25  Smallwood     Devin        NaN  1991-04-13   Male  Black  1569 Winston Ave  Baltimore    Md   21239  Northeast  413  Stonewood-Pentwood-Winston   39.349  -76.5877  (39.349,-76.5877)    NaN 10722 days  29      8          20  2020  (39.349, -76.5877)"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.sample()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [],
   "source": [
    "# BINS\n",
    "\n",
    "# # Create bins in which to place values based upon TED Talk views\n",
    "# bins = [0, 199999, 399999, 599999, 799999, 999999,\n",
    "#         1999999, 2999999, 3999999, 4999999, 50000000]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [],
   "source": [
    "# %pip install geopandas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [],
   "source": [
    "# %pip install shapely"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "488"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['District'].isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['year'].isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "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>X</th>\n",
       "      <th>Y</th>\n",
       "      <th>RowID</th>\n",
       "      <th>CaseNumber</th>\n",
       "      <th>CreatedDate</th>\n",
       "      <th>ModifiedDate</th>\n",
       "      <th>LastName</th>\n",
       "      <th>FirstName</th>\n",
       "      <th>MiddleName</th>\n",
       "      <th>DateOfBirth</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Race</th>\n",
       "      <th>FullAddress</th>\n",
       "      <th>City</th>\n",
       "      <th>State</th>\n",
       "      <th>ZipCode</th>\n",
       "      <th>District</th>\n",
       "      <th>Post</th>\n",
       "      <th>Neighborhood</th>\n",
       "      <th>Latitude</th>\n",
       "      <th>Longitude</th>\n",
       "      <th>GeoLocation</th>\n",
       "      <th>Shape</th>\n",
       "      <th>age</th>\n",
       "      <th>Age</th>\n",
       "      <th>month</th>\n",
       "      <th>date_month</th>\n",
       "      <th>year</th>\n",
       "      <th>coordinates</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1310</th>\n",
       "      <td>1399093.125</td>\n",
       "      <td>588763.8125</td>\n",
       "      <td>1311</td>\n",
       "      <td>19-0101</td>\n",
       "      <td>2019-02-25</td>\n",
       "      <td>2021-08-26</td>\n",
       "      <td>Ricks</td>\n",
       "      <td>Dontae</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1996-06-13</td>\n",
       "      <td>Male</td>\n",
       "      <td>Black</td>\n",
       "      <td>4726 Dartford Ave</td>\n",
       "      <td>Baltimore</td>\n",
       "      <td>Md</td>\n",
       "      <td>21229</td>\n",
       "      <td>Southwest</td>\n",
       "      <td>822</td>\n",
       "      <td>Tremont</td>\n",
       "      <td>39.2829</td>\n",
       "      <td>-76.6935</td>\n",
       "      <td>(39.2829,-76.6935)</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8292 days</td>\n",
       "      <td>22</td>\n",
       "      <td>2</td>\n",
       "      <td>25</td>\n",
       "      <td>2019</td>\n",
       "      <td>(39.2829, -76.6935)</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                X            Y  RowID CaseNumber CreatedDate ModifiedDate LastName FirstName MiddleName DateOfBirth Gender   Race        FullAddress       City State ZipCode   District Post Neighborhood Latitude Longitude         GeoLocation  Shape       age Age  month  date_month  year          coordinates\n",
       "1310  1399093.125  588763.8125   1311    19-0101  2019-02-25   2021-08-26    Ricks    Dontae        NaN  1996-06-13   Male  Black  4726 Dartford Ave  Baltimore    Md   21229  Southwest  822      Tremont  39.2829  -76.6935  (39.2829,-76.6935)    NaN 8292 days  22      2          25  2019  (39.2829, -76.6935)"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "district_df=df.dropna(subset=['District'])\n",
    "district_df.sample()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "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>X</th>\n",
       "      <th>Y</th>\n",
       "      <th>RowID</th>\n",
       "      <th>CaseNumber</th>\n",
       "      <th>CreatedDate</th>\n",
       "      <th>ModifiedDate</th>\n",
       "      <th>LastName</th>\n",
       "      <th>FirstName</th>\n",
       "      <th>MiddleName</th>\n",
       "      <th>DateOfBirth</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Race</th>\n",
       "      <th>FullAddress</th>\n",
       "      <th>City</th>\n",
       "      <th>State</th>\n",
       "      <th>ZipCode</th>\n",
       "      <th>District</th>\n",
       "      <th>Post</th>\n",
       "      <th>Neighborhood</th>\n",
       "      <th>Latitude</th>\n",
       "      <th>Longitude</th>\n",
       "      <th>GeoLocation</th>\n",
       "      <th>Shape</th>\n",
       "      <th>age</th>\n",
       "      <th>Age</th>\n",
       "      <th>month</th>\n",
       "      <th>date_month</th>\n",
       "      <th>year</th>\n",
       "      <th>coordinates</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2315</th>\n",
       "      <td>1411090.0</td>\n",
       "      <td>590081.6875</td>\n",
       "      <td>2316</td>\n",
       "      <td>15-0251</td>\n",
       "      <td>2015-05-27</td>\n",
       "      <td>2020-12-07</td>\n",
       "      <td>Walston</td>\n",
       "      <td>Damon</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1991-03-27</td>\n",
       "      <td>Male</td>\n",
       "      <td>Black</td>\n",
       "      <td>2147 Boyd St</td>\n",
       "      <td>Baltimore</td>\n",
       "      <td>Md</td>\n",
       "      <td>21223</td>\n",
       "      <td>Southwest</td>\n",
       "      <td>835</td>\n",
       "      <td>Boyd-Booth</td>\n",
       "      <td>39.2864</td>\n",
       "      <td>-76.6511</td>\n",
       "      <td>(39.2864,-76.6511)</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8827 days</td>\n",
       "      <td>24</td>\n",
       "      <td>5</td>\n",
       "      <td>27</td>\n",
       "      <td>2015</td>\n",
       "      <td>(39.2864, -76.6511)</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              X            Y  RowID CaseNumber CreatedDate ModifiedDate LastName FirstName MiddleName DateOfBirth Gender   Race   FullAddress       City State ZipCode   District Post Neighborhood Latitude Longitude         GeoLocation  Shape       age Age  month  date_month  year          coordinates\n",
       "2315  1411090.0  590081.6875   2316    15-0251  2015-05-27   2020-12-07  Walston     Damon        NaN  1991-03-27   Male  Black  2147 Boyd St  Baltimore    Md   21223  Southwest  835   Boyd-Booth  39.2864  -76.6511  (39.2864,-76.6511)    NaN 8827 days  24      5          27  2015  (39.2864, -76.6511)"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "age_df=df.dropna(subset=['Age'])\n",
    "age_df.sample()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "youngest: 15 \n",
      " oldest: 1574    80\n",
      "Name: Age, dtype: object \n",
      " median: 26.0\n"
     ]
    }
   ],
   "source": [
    "median_age=age_df['Age'].median()\n",
    "youngest=age_df['Age'].min()\n",
    "oldest_person=age_df.sort_values('age', ascending=False).head(1)\n",
    "oldest=oldest_person['Age']\n",
    "print(f'youngest: {youngest} \\n oldest: {oldest} \\n median: {median_age}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Male      2688\n",
       "Female      60\n",
       "Name: Gender, dtype: int64"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gender_profile=df['Gender'].value_counts()\n",
    "gender_profile"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:ylabel='Race'>"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAARcAAADnCAYAAAA0NhJ5AAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjQuMiwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8rg+JYAAAACXBIWXMAAAsTAAALEwEAmpwYAAAbAklEQVR4nO3deZwdVZ338c+vbpImISFkEwhbsTRKWMI6AaIBwhKw3HBGR57IKC7DIzgMjDKUzDwKr3GkfHxYxgWHUWQRnZFNZSxAeBQwgGg2SEIEshVJ2BJIOkmnO73ce+aPU500bdJ9u3Prnlv3/t6vV7/6dnfdvt+GzrdPVZ06JcYYlFKq0jzXAZRS9UnLRSmVCS0XpVQmtFyUUpnQclFKZULLRSmVCS0XpVQmtFyUUpnQclFKZULLRSmVCS0XpVQmtFyUUpnQclFKZULLRSmVCS0XpVQmtFyUUpnQclFKZULLRSmVCS0XpVQmtFyUUpnQclFKZULLRSmVCS0XpVQmtFyUUpnQclFKZULLRSmVCS0XpVQmhrkOoLLhh/FE4LBebwcAo4FRwJ7p+96Pm4BOoK3XWwuwAXg7fUuAl4GXkijYWLUfRuWS6I3o880P48nAe4ETeGeZ7JXxS78FvERaNunb3CQKXs34dVVOaLnkiB/GAkzBlknPm+8y006sAJ4AngQeT6Jgrds4yhUtlxqXjkwuAM4DTgPGu000aCuxZfME8FASBW87TaOqRsulBvlhfBDwceAvgWmAuE1UMV3Ao8BPgV8kUdDmOI/KkJZLjfDDeALwMWA2MJ36KZRd2Qr8Els0v06ioNtxHlVhWi6O+WF8LPAl4BPACMdxXHkLuAf4XhIFS12HUZWh5eJAemD2fOAfgLMcx6klBrvbdGMSBY+6DqN2j5ZLFflhvAdwEXAlcKTjOLVuEXA9cE8SBSXXYdTgablUQVoqV2BHKpPcpsmdZUAE/DiJgi7XYVT5tFwy5ofxJ7D/OA52nSXnXgKuSKLgEddBVHm0XDLih/E04CbgVNdZ6syvgCuTKFjuOojqn5ZLhaVzVCLs2Z96P53sSie2uL+eREGr6zBq57RcKsQP42HANUAIjHQcp1G8DlwN3J1Egf4i1xgtlwrww/gI4G7gZNdZGtTjwEV60WRt0fVcdpMfxpcCC9FicelMYJEfxhe4DqJ20JHLEPlhvB/wI+wFhap2/Af2gK9et+SYlssQ+GH8Uewv8QTXWdRO/Qm4MImC510HaWRaLoOQHrT9N+BS11nUgDqAMImCm10HaVRaLmVKr1q+F7t/r/LjbuCzSRR0ug7SaLRcyuCH8VHAg8ChrrOoIXkc+GgSBS2ugzQSLZcB+GE8E3gAGOs6i9otS4HzkyhY7TpIo9BT0f3ww/gi4BG0WOrBFOBZP4xPcB2kUWi57IIfxlcBdwHDXWdRFbMf8KQfxu93HaQRaLnshB/GXwL+r+scKhOjgQfTUanKkB5z6cMP478Dvu06h8pcCfhkEgX/6TpIvdJy6cUP40uAf3edQ1VNN3ay3X2ug9QjLZeUH8YXA7ehyyQ0mi7ggiQKYtdB6o2WC+CH8WzswVs9BtWY2oFzkih42nWQetLw5eKH8QeBnwMF11mUUy3AjCQKFrsOUi8aulz8MD4S+AMwxnUWVRNeA05KouB110HqQcPuBvhhPBZ7xz8tFtVjMnCvH8Y6t6kCGrJc/DD2sLcRbXadRdWc6cD/cx2iHjRkuQBfB3SWptqVy/0wvtB1iLxruGMufhh/DHtfYqX60wacogd4h66hysUP46OBZ4E9XWdRubAMODmJgk2ug+RRw+wWpQfp7kaLRZWvGbjTdYi8aphyAb4CTHUdQuXOh/0w/rTrEHnUELtFfhgfA8xHl09QQ7MBODKJgnWug+RJ3Y9c0kW1b0eLRQ3deOzC7GoQ6r5cgKuAE12HULn3CT+MA9ch8qSud4vS6f0LgSbXWVRdWANMSaKg1XWQPKjbkYsfxoK9I6IWi6qUA4FvuA6RF3VbLsBs4BTXIVTducwP42muQ+RBXZaLH8Z7AP/qOoeqSx7wTdch8qAuywW4HDjIdQhVt073w/hs1yFqXd2Vix/G47AT5pTK0r+4DlDr6q5cgC8Be7sOoereKXr/o/7V1aloP4wnAquw96ZRKmsLsCvX1c8/ogqqt5HLP6LFoqrnBOAjrkPUqroZufhhPAZ4FV22UlXXYmCqjl7+XD2NXC5Ci0VV3zHAB1yHqEX1VC5fcB1ANazLXAeoRXWxW+SH8QzgSdc5VMMywBFJFCx3HaSW1MvI5VLXAVRDE3Tk/GdyP3Lxw3gf7NWqul6LcultYHISBZ2ug9SKehi5fB4tFuXeBOBDrkPUklyXS7qswt+6zqFU6mLXAWpJrssFmIZdY0OpWjDLD+P9XIeoFXkvlw+7DqBULwV012g7LRelKkvX2U3l9myRH8aHY++Ip1QtaQMmJFGwzXUQ1/I8ctFRi6pFo4AzXIeoBVouSlWe7hqR03JJ1205zXUOpXZBF5Eip+UCnIc9Mq9ULTrUD+P3uA7hWl7LRW8Zompdw49e8louJ7sOoNQA/sJ1ANdyVy5+GA8HprrOodQAjncdwLXclQt25S+9Rauqdc1+GDf0es55LBfdJVJ5IMBxrkO4pOWiVHYaetcoj+VykusASpVJy2UgIlIUkedE5HkRWSAip6Wf90VkyVBeWESeEJFBFYUfxk3AUUN5PaUc0HIpQ7sx5jhjzFTsfZivzzBTf3xgmKPXVmqwjkrPbjakoewW7QVs7PvJdBQzJx3ZbB/dpF+7WkQWpyOfqM/zPBG5Q0S+XsZr+0PIq5Qrw4HJrkO4Uu4oYKSIPAfsAewHzNzJNuuAc4wx20SkGfhP4CQROR97keE0Y0ybiIzv8/o/AZYYY/61jBx+mXmVqhX7Aq+4DuFCueXSbow5DkBETgXuEpGj+2wzHPiuiBwHFIEj0s+fDdxujGkDMMZs6PWcW4F7yiwWgIPL3E6pWrGv6wCulLtbJCJym4g8bIz5PbAP8Pd9trkSeBM7e/YkYEQZ3/cZ4EwR2aPMHA07xFS51bBr6pZbLk3Ar4HJItJztedn+2wzFnjdGFPC3re556rlx4CLRWQUQJ/dotuAh4B7RKScUdQ+ZeZVqlboyGUABeAaoBn4GfAp7K5Pb7cAnxKR54H3AFsBjDGPAA8C89LjNl/u/SRjzI3AQuDHIjJQnob9H6Vyq2FHLuUec3kS+EvgMWPMCSJyCrDJGJMARwMYY5YBx/Z6ztU9D4wxEfCOs0TGmDN6Pf5amTl05KLypmH/IJZbLv+AHX0cJiJPA5OAv8os1a6NcvCaSu2Ohi2XsnaLjDELgNOxS0teAhxljFmUZbBd0NXnVN7sVe6GItI6mG8sImeIyK/Sxx8SkXCw4bJU7vT/y4DRxpgXjDFLgNEicmm20XZKZ+eqvBnUH0QR+YiImF4nTna13UPA9iUdjDEPpocfaka5B3Q/b4xp6fnAGLMRewP4atORi8qbwf5BvBBYDDwiIveJyIsi8hMREQAROU9EXsTubp3b8yQR+bSIfDd9/EER+YOILBSR/y8i+6Sfv1ZEfpRe17dSRC6vyE+4C+X+4AUREZPeQU1ECpQ3j6XSdOSi8qas31kR8bHHFN+LPRlyJ/AA8AJ2DtnLItIJTEy3eSx9P05EfoFdO2aMiCwC7sWuM70FeA1YLCIrgTnYM7lnAmOAl0Tk+8aYrkr8oH2V+4/1EeBnInJr+vEl6eeqxg9jwS7AozLwLt5ce2JhycrjC0u2HuKtGj5C2ps2i9e9hWFdm71CqZVCaSsF04pHu4i0Uyh0IoVOZFg3DCshI8R4eAYjJUyhRKlgMIWSGK8kJc+IKZQwXkkolCh5xr63XwfPbotnMPZrYryi/bhgerYRCkWDh2ffG0GMGM+AVxLxDNjH9HxNCiVEEOOVjPR8zjMgBikYBAMC6dcQzyDYbQQEzxhP0j+p9rEI9i+tiBEP0k8Y4xkRY7b/igqAMSIbBnkbo0eAtdhi2BM4HPgT8B1gKfAdY8yydCAzFzt6+Qz2HtXTgMvT7b6aPn8qsABYhJ3cGhtjOoAOEVmHPQO7dki/NAMot1yuxhbKF9KPHwN+mEWgfuioJUPr2OeAh4v7HPBw8SwAJtKyfmZh4apZ3rz287zlY8expVmEPfv7Hm0ibVs8r3XzMG9rS8Frb/G8jo2FQudGz+tuKXjFjYUCmzzPtHje8FbPK7R5MnybSFOHyB7dIiOLsKeBMYjk8kpiMabklSh6hqJXSt8Ma54dxLcA/it9vBa7pOtC7J0Evoo9jDFZRN7X53mXA5cC44B24JfY2e8mfc6BwO3YS3HOEZEL06/tCQwTke8BvzbGPCgiPwc2GmM+IyKfAQ4DfgA8DDyFPanzKvBhY0x7fz9MWf9g01m330/fXNHjLVX0FntPuqd45qR7imcC4FEqTpUVL51X+OObp3uLCofK6/uPkG6/93NGGTNqVLE4ap9iEXZjoN0hdGzxvC2bPa9tk+e1tRQKHRs9r2NDodC1seAVWzzPtBQKbPY8afWksNXzhreLjOgpqW5bUqMp/7KSijAiXrGAV7TX2fUoN8PY9P0PsTPixwKd2F2j9enjItDNjjljJ6bf/+PAJmypNAMnpJ9vA54Fzk+fOw57EPhI7O7VGuy0kjnA+7DTTfZnx8S/97Gj7JqBC40xnxeRe7Dz3u7u7wcqd3+wGbuGyxR6/ccyxhxazvMrpBMokc/V83KvhFdYaJrfvbC7+d3XMxuAcWzeMNN7bsW5hbltJ3rLxkxgc7MIY3b3tZoMTU3FUtPEYmm3vk8XdLV63pZNnrd1kx1JbdtYKHRsLHhdLV6huLHglVo8j82eJ1s8r7DV84a1ezKiQ6SpMx1JlWxJ7c78qn7/uvdyPoAx5mAROQM7k/1o4BDs7/0vgDewo5izsSOPVcDe2H+TX8EegzkFu1s0FRgJrGBH2e0JPG6MKQJvikhbut3DwBUiMiV97jgR2Q84FTsqmgCsMsY8l36f+ZSxQkG5uxq3A18DbsIeDLqYKv8jT6Kg5IfxBmzjqhqwkb3G31+aMf7+0gwAhFLpGFm1bFZh3htneM/J4fLq5BF0HyLi5ljZcBg+rlQaP65UGk/30L9PEYqtnte62fNa7UjK25bu8nVtLHjdLZ5XarG7fGzxPK/Vk+Ft4o3o8KRJDCvLfJn3A28DGGOeAJ4QkSexo5JR2Emr67EXDE9Jn3M/dnQyA3vo4nnsSGUb8A3gDmPMVSLSczhjPfYawR7/DawzxrwqIntj72T6O2B8+rqtxpgtIjIB6HjnfxJGDvQDlb2eizHmN+kZo1eAa0VkPnY/sJrWo+VSswyet8gc1ryo+7Dmb/HXAIylteV07/kVswrztpzsvThmEpsOF9m+C5ALBSiMLZXGji2Vxh44+KeXdbDUGDNDROaJyExjzG/TC3z3w45EbgO+bIyZJyIT0+19Efk0dvTyJezu0z8ZY14QkTvSbXrPg7lPRErAJSJyJ7ZAZgBXpZs8C1yBXatpAnBf+jZk5ZZLR3pR4TIR+SL2gI6Le7K85eA11W7YxOi9HyxNP/HB0vT0M8ZMkVdWnFuY99pMbyFHyNp9m+g6TKRud3c3DLzJdn8DfE9Ebkw/vs4YsyI9M7RLxpgXRWQ2cK+IfLCfTX+O3dV5Hrtb9Y/GmDfSr80BzjXGLBeRV7DlM2cQ2f+MpFNX+t9I5GTs6bC9gX/BHmz6pjHmD7vz4oPlh/H9wEer+Zoqe6Np2zzDW7x8VuGPW6Z5L456FxsP94RxrnNVyC1cu+ky1yFcKPds0dz0YSt2bZYC8AmgquWC3S1SdaaVUXs9VJp2wkOlads/d4SsWXWuN++1mYWFxffI6n1H0nmYSC7PGDbs72y/5SIiewGXYU9PPYid33IZdh9vEXb922rS3aIG8bI58JCXiwce8t3iBQDsSXvrdO+F5bMKc1tO8ZaO2o8Nh3pi8nD8bfVAG4jITcArxpib049/Dawxxnwu/fgG7KGImcaYD+zk+T8EbjTGLBWRa4wx36jkDzBUA41cfoxd6f/3wOewC0YJcEGv01LV1LB/BRrdVkaOfrR00nGPlnbc6upQeW31Od78NWcXFhSnyCuTRrGtWaTmJlsmZWzzNPbszM3psc2JvPNq6tOwc1h2qqeEUtdgzxQ5N9D/iEONMcfA9nZ8HTjIGLMt82Q7t8bR66oatNJMPujW4uSDbi3aY5h70NF+qrf0hfO8uS2nei807S9vHVoQ8y7HMctZ+f8Z7DQPsDf9WwLsJyLjsKeaj8ROBTlPRO7Dzn+ZD3zSGGNE5AnsvJi/YsedOl4wxswWkU9i56qMwB7GuDSd55K5gcpl+zxLY0xRRNY6LBaw/9GV2qltNI18vHT81MdLO250eJC8ufYcb/6as735nUd7ycTRtDeLVO2i2y7KKBdjzGsi0i0iB2FHKb/HHoo4FTvzdjF2Eunx2PJ5DTvamY6dkt/zfUIR+WKvO3UcCfw1MN0Y0yUitwCzgbsq9hP2Y6BymSoim9PHgm3FzeljY4wpeyGcClmObXJdkU6VZbXZ54Dbiu8/4Lbi+wFoonPbX3gvLpnlzX37vd6SpgNk/cHDpJTVOrcruXZTudP3nsEWy2nAjdhyOQ1bLk+n2/zRGLMWIB2d+PQql504C3uJwNz0dPZI7P3FqqLfcjHG1NTR+XSW7lL0ZvRqiDoYscec0rFHzyntWO55f9a/cVZhwapzvPmdx3orx+9F2xEiNFXg5V4exLZPY8vkGOwIfQ32xMlm7Ax5G3+HIgMPDgS40xjzlUHkqJhaO/hVjp5Lx5WqiFeZtO9dxVn73lWcBcBwujtP9F564Txv7tvv8xYPO1jePHiYlPYfwrdeOohtn8EeN1mZHhPZkE7JPwq7MFvfmxDuSpeIDE/XaPkN8EsRuckYsy6d9TsmnWWfuTyWy2LXAVR962LYiGdLRx31bOmo7Z/blw1vziwsSM7x5rcf5y0fvzdbm0UGvL5m3iBedjH2LNFP+3xutDHmrYFm6fbyH8AiEVmQHtD9Z+DR9CxUF3YqSVXKpawZurXED+OZ2EZWypkCxe4TZNnyWYV562Z4zw87RN7Yf7gU+95u2OfaTQ15n2jIZ7lMROe7qBo0gU1vzSwsXDnLm9c+1VvhTbpu9QzXmVzKXbkA+GG8Gru6llK16pdJFHzEdQiX8nol6uOuAyg1gN26orge5LVcfus6gFID6G/+SUPIa7noAV1Vy9qwK+43tFyWSxIFa4EXXedQahceTaIgk3sB5UkuyyUVuw6g1C7c7zpALdByUaqyurALXze8PJfLU9iLupSqJb9JokB/L8lxuaT7tL9ynUOpPh5wHaBW5LZcUrcPvIlSVVPE3rxMkf9y+S3lLSOoVDXMSaJAL01J5bpckigwwB2ucyiV2q2biNWbXJdL6g7sDZ6UcmkrA9yYvdHkvlySKHgFvRxAuXenniV6p9yXS+pHrgOohmaA77gOUWvqpVweAFpch1AN67EkCvRylD7qolySKNgGfN91DtWw/s11gFpUF+WSugF7L2ulqmkZ8LDrELWobsoliYK3ge+5zqEaznfTKRGqj7opl9QN2FOCSlXDevRkwi7VVbmksyP12IuqluuSKNBd8V2oq3JJfQu7EphSWXoZuNV1iFpWd+WSRME64N9d51B175okCsq9D3RDqrtySX0TXetFZeeZJAp0tbkB1GW5pKOXf3KdQ9Wtq1wHyIO6LJfU94G5rkOouvPzJAqecR0iD+q2XJIoKAGXYBfwUaoSOoCrXYfIi7otF4AkChaiF5SpyrkuiYJlrkPkRV2XS+r/AGtdh1C5Nx87zUGVqe7LJZ3k9Peuc6hc6wI+o6eeB6fuywUgiYIH0FXZ1dBdm0TBItch8qYhyiX1OWC16xAqd54CItch8qhhyiWJgo3A/0LPHqnybQYuSs88qkFqmHIBSKLgaeCrrnOo3LgkiYLEdYi8aqhySV2P3qlRDewbSRT8l+sQedZw5ZIu7HMRsMJ1FlWzfgH8s+sQeSfGNOYiWn4YHwM8C4xynUXVlEXAdF2nZfc13MilRxIFi4GPAzp3QfVYD3xIi6UyGrZcAJIoiIGL0Ts2KugEPpreZE9VQEOXC0ASBXcDV7jOoZwy2DNDT7kOUk8avlwAkij4NvB11zmUEwb4YhIFd7gOUm8a9oDuzvhhfAvwBdc5VNX0FMstroPUIx25vNMXAZ3b0Bi0WDKm5dJLOs37IuBO11lUprRYqkB3i3bBD+PrgdB1DlVxWixVouXSDz+M/w64GR3h1Ytu4NIkCn7gOkgj0HIZgB/GHwN+DDS5zqJ2ywbgY0kU/NZ1kEah5VIGP4zPwF5vMtZtEjVEf8LOvF3uOkgj0eF+GZIoeAKYAaxyHEUN3sPAKVos1aflUqZ0mcPjgXtdZ1FluwH4QBIFm10HaUS6WzQEfhj/b+AmYA/XWdROtQNfSKJApxQ4pOUyRH4YHwvcA7zbdRb1DguB2UkU/Ml1kEanu0VDlO4mnQjc5TqLAqCEXUh7mhZLbdCRSwX4YTwbOx9mouMojepF4LN6D+faoiOXCkii4CfAe4A7HEdpNN3Y0cpxWiy1R0cuFeaH8enALcAU11nq3O+AK5MoWOA6iNo5LZcM+GE8DLgUuBYY5zZN3VkKhEkU/LfrIKp/Wi4Z8sN4InAd9m6PIxzHybvXga8BP0qiQG9slwNaLlXgh/H+wJeBv0XvNjBYW4BvATckUdDmOowqn5ZLFflhPAm7Xu9l6HVKA3kL+AFwcxIF61yHUYOn5eKAH8ZjsQVzJXr6uq/ngW8DP02iYJvrMGrotFwc8sN4FHAh8CngvYC4TeRMEXvV+beTKPid4yyqQrRcaoQfxodgl9i8CDjccZxqWQH8DLg1iYLVrsOoytJyqUF+GE8H/gZ7R8i93aapuCXAA8D96SUUqk5pudQwP4ybgOnAWcDZ2GuZCk5DDc1cbKE8kETBy67DqOrQcsmR9EDwmdiyOQs40m2inSphb+b+VPo2J4mC19xGUi5oueSYH8aTsaOZqcCx6fvDqN7opgNIsMsczEvfFiRRsKVKr69qmJZLnfHDeDi2YJqBI4DJ2EsQxgHj+7zvO6GvC1sYHdgbs3cArcBq4JX0Len1+I0kCvQXSO2UlksD88N4BDCStFC0KFQlabkopTKh67kopTKh5aKUyoSWi1IqE1ouSqlMaLkopTKh5aKUyoSWi1IqE1ouSqlMaLkopTKh5aKUyoSWi1IqE1ouSqlMaLkopTKh5aKUyoSWi1IqE1ouSqlMaLkopTKh5aKUyoSWi1IqE1ouSqlMaLkopTKh5aKUyoSWi1IqE1ouSqlMaLkopTKh5aKUyoSWi1IqE/8D1goSpuYwuDgAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "racial_profile=df['Race'].value_counts()\n",
    "racial_profile.plot(kind='pie')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "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>X</th>\n",
       "      <th>Y</th>\n",
       "      <th>RowID</th>\n",
       "      <th>CaseNumber</th>\n",
       "      <th>CreatedDate</th>\n",
       "      <th>ModifiedDate</th>\n",
       "      <th>LastName</th>\n",
       "      <th>FirstName</th>\n",
       "      <th>MiddleName</th>\n",
       "      <th>DateOfBirth</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Race</th>\n",
       "      <th>FullAddress</th>\n",
       "      <th>City</th>\n",
       "      <th>State</th>\n",
       "      <th>ZipCode</th>\n",
       "      <th>District</th>\n",
       "      <th>Post</th>\n",
       "      <th>Neighborhood</th>\n",
       "      <th>Latitude</th>\n",
       "      <th>Longitude</th>\n",
       "      <th>GeoLocation</th>\n",
       "      <th>Shape</th>\n",
       "      <th>age</th>\n",
       "      <th>Age</th>\n",
       "      <th>month</th>\n",
       "      <th>date_month</th>\n",
       "      <th>year</th>\n",
       "      <th>coordinates</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1574</th>\n",
       "      <td>1361726.875</td>\n",
       "      <td>619188.4375</td>\n",
       "      <td>1575</td>\n",
       "      <td>18-0563</td>\n",
       "      <td>2018-10-10</td>\n",
       "      <td>2021-03-30</td>\n",
       "      <td>Chavis</td>\n",
       "      <td>Gordon</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1938-06-29</td>\n",
       "      <td>Male</td>\n",
       "      <td>Black</td>\n",
       "      <td>9620 Mendoza Road, Randallstown, 21133</td>\n",
       "      <td>Randallstown</td>\n",
       "      <td>Md</td>\n",
       "      <td>21133</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>39.3667</td>\n",
       "      <td>-76.8253</td>\n",
       "      <td>(39.3667,-76.8253)</td>\n",
       "      <td>NaN</td>\n",
       "      <td>29323 days</td>\n",
       "      <td>80</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>2018</td>\n",
       "      <td>(39.3667, -76.8253)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1477</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1478</td>\n",
       "      <td>18-0667</td>\n",
       "      <td>2018-12-06</td>\n",
       "      <td>2020-12-07</td>\n",
       "      <td>Gomez</td>\n",
       "      <td>Irvin</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1944-08-31</td>\n",
       "      <td>Male</td>\n",
       "      <td>Black</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>(,)</td>\n",
       "      <td>NaN</td>\n",
       "      <td>27125 days</td>\n",
       "      <td>74</td>\n",
       "      <td>12</td>\n",
       "      <td>6</td>\n",
       "      <td>2018</td>\n",
       "      <td>(nan, nan)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>643</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>644</td>\n",
       "      <td>20-0096</td>\n",
       "      <td>2020-03-02</td>\n",
       "      <td>2020-12-07</td>\n",
       "      <td>Wynne</td>\n",
       "      <td>Hardaway</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1948-01-29</td>\n",
       "      <td>Male</td>\n",
       "      <td>Black</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>(,)</td>\n",
       "      <td>NaN</td>\n",
       "      <td>26331 days</td>\n",
       "      <td>72</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>2020</td>\n",
       "      <td>(nan, nan)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2349</th>\n",
       "      <td>1402956.375</td>\n",
       "      <td>592965.7500</td>\n",
       "      <td>2350</td>\n",
       "      <td>15-0119</td>\n",
       "      <td>2015-03-23</td>\n",
       "      <td>2021-08-24</td>\n",
       "      <td>Cobb</td>\n",
       "      <td>Jessie</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1945-05-02</td>\n",
       "      <td>Male</td>\n",
       "      <td>Black</td>\n",
       "      <td>607 Mount Holly St</td>\n",
       "      <td>Baltimore</td>\n",
       "      <td>Md</td>\n",
       "      <td>21229</td>\n",
       "      <td>Southwest</td>\n",
       "      <td>815</td>\n",
       "      <td>Edgewood</td>\n",
       "      <td>39.2944</td>\n",
       "      <td>-76.6798</td>\n",
       "      <td>(39.2944,-76.6798)</td>\n",
       "      <td>NaN</td>\n",
       "      <td>25527 days</td>\n",
       "      <td>69</td>\n",
       "      <td>3</td>\n",
       "      <td>23</td>\n",
       "      <td>2015</td>\n",
       "      <td>(39.2944, -76.6798)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1144</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1145</td>\n",
       "      <td>19-0279</td>\n",
       "      <td>2019-05-14</td>\n",
       "      <td>2020-12-07</td>\n",
       "      <td>Bass</td>\n",
       "      <td>Gold</td>\n",
       "      <td>Leroy</td>\n",
       "      <td>1950-04-08</td>\n",
       "      <td>Male</td>\n",
       "      <td>Black</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>(,)</td>\n",
       "      <td>NaN</td>\n",
       "      <td>25238 days</td>\n",
       "      <td>69</td>\n",
       "      <td>5</td>\n",
       "      <td>14</td>\n",
       "      <td>2019</td>\n",
       "      <td>(nan, nan)</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",
       "      <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>2679</th>\n",
       "      <td>1426750.125</td>\n",
       "      <td>594844.9375</td>\n",
       "      <td>2680</td>\n",
       "      <td>10-0354</td>\n",
       "      <td>2010-08-31</td>\n",
       "      <td>2020-12-07</td>\n",
       "      <td>Hall</td>\n",
       "      <td>Anthony</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1994-07-07</td>\n",
       "      <td>Male</td>\n",
       "      <td>Black</td>\n",
       "      <td>1621 E Madison St</td>\n",
       "      <td>Baltimore</td>\n",
       "      <td>Md</td>\n",
       "      <td>21287</td>\n",
       "      <td>Eastern</td>\n",
       "      <td>323</td>\n",
       "      <td>Gay Street</td>\n",
       "      <td>39.2993</td>\n",
       "      <td>-76.5957</td>\n",
       "      <td>(39.2993,-76.5957)</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5899 days</td>\n",
       "      <td>16</td>\n",
       "      <td>8</td>\n",
       "      <td>31</td>\n",
       "      <td>2010</td>\n",
       "      <td>(39.2993, -76.5957)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2624</th>\n",
       "      <td>1409505.750</td>\n",
       "      <td>589857.1250</td>\n",
       "      <td>2625</td>\n",
       "      <td>11-0487</td>\n",
       "      <td>2011-11-21</td>\n",
       "      <td>2020-12-07</td>\n",
       "      <td>Holt</td>\n",
       "      <td>Arteesha</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1996-04-23</td>\n",
       "      <td>Female</td>\n",
       "      <td>Black</td>\n",
       "      <td>2526 W Lombard St</td>\n",
       "      <td>Baltimore</td>\n",
       "      <td>Md</td>\n",
       "      <td>21223</td>\n",
       "      <td>Southwest</td>\n",
       "      <td>835</td>\n",
       "      <td>Shipley Hill</td>\n",
       "      <td>39.2858</td>\n",
       "      <td>-76.6567</td>\n",
       "      <td>(39.2858,-76.6567)</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5690 days</td>\n",
       "      <td>15</td>\n",
       "      <td>11</td>\n",
       "      <td>21</td>\n",
       "      <td>2011</td>\n",
       "      <td>(39.2858, -76.6567)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2710</th>\n",
       "      <td>1426328.375</td>\n",
       "      <td>568545.4375</td>\n",
       "      <td>2711</td>\n",
       "      <td>09-0260</td>\n",
       "      <td>2009-08-13</td>\n",
       "      <td>2021-08-23</td>\n",
       "      <td>Dorsey</td>\n",
       "      <td>Raheem</td>\n",
       "      <td>Jabar</td>\n",
       "      <td>1994-06-02</td>\n",
       "      <td>Male</td>\n",
       "      <td>Black</td>\n",
       "      <td>1107 Seaboard Ct</td>\n",
       "      <td>Baltimore</td>\n",
       "      <td>Md</td>\n",
       "      <td>21225</td>\n",
       "      <td>Southern</td>\n",
       "      <td>913</td>\n",
       "      <td>Brooklyn</td>\n",
       "      <td>39.2271</td>\n",
       "      <td>-76.5976</td>\n",
       "      <td>(39.2271,-76.5976)</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5551 days</td>\n",
       "      <td>15</td>\n",
       "      <td>8</td>\n",
       "      <td>13</td>\n",
       "      <td>2009</td>\n",
       "      <td>(39.2271, -76.5976)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>206</th>\n",
       "      <td>1411806.250</td>\n",
       "      <td>595220.1250</td>\n",
       "      <td>207</td>\n",
       "      <td>21-0171</td>\n",
       "      <td>2021-06-04</td>\n",
       "      <td>2021-06-04</td>\n",
       "      <td>Chance</td>\n",
       "      <td>Brion</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2019-02-15</td>\n",
       "      <td>Male</td>\n",
       "      <td>Black</td>\n",
       "      <td>1022 Appleton St</td>\n",
       "      <td>Baltimore</td>\n",
       "      <td>Md</td>\n",
       "      <td>21217</td>\n",
       "      <td>Western</td>\n",
       "      <td>722</td>\n",
       "      <td>Midtown-Edmondson</td>\n",
       "      <td>39.3005</td>\n",
       "      <td>-76.6485</td>\n",
       "      <td>(39.3005,-76.6485)</td>\n",
       "      <td>NaN</td>\n",
       "      <td>840 days</td>\n",
       "      <td>2</td>\n",
       "      <td>6</td>\n",
       "      <td>4</td>\n",
       "      <td>2021</td>\n",
       "      <td>(39.3005, -76.6485)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1428</th>\n",
       "      <td>1401533.250</td>\n",
       "      <td>595291.9375</td>\n",
       "      <td>1429</td>\n",
       "      <td>18-0720</td>\n",
       "      <td>2018-12-26</td>\n",
       "      <td>2021-04-15</td>\n",
       "      <td>Jones</td>\n",
       "      <td>David</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaT</td>\n",
       "      <td>Male</td>\n",
       "      <td>Black</td>\n",
       "      <td>4119 Mountwood Rd</td>\n",
       "      <td>Baltimore</td>\n",
       "      <td>Md</td>\n",
       "      <td>21229</td>\n",
       "      <td>Southwest</td>\n",
       "      <td>815</td>\n",
       "      <td>Edmondson Village</td>\n",
       "      <td>39.3008</td>\n",
       "      <td>-76.6848</td>\n",
       "      <td>(39.3008,-76.6848)</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaT</td>\n",
       "      <td>nan</td>\n",
       "      <td>12</td>\n",
       "      <td>26</td>\n",
       "      <td>2018</td>\n",
       "      <td>(39.3008, -76.6848)</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2748 rows × 29 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                X            Y  RowID CaseNumber CreatedDate ModifiedDate LastName FirstName MiddleName DateOfBirth  Gender   Race                             FullAddress          City State ZipCode   District Post       Neighborhood Latitude Longitude         GeoLocation  Shape        age  Age  month  date_month  year          coordinates\n",
       "1574  1361726.875  619188.4375   1575    18-0563  2018-10-10   2021-03-30   Chavis    Gordon        NaN  1938-06-29    Male  Black  9620 Mendoza Road, Randallstown, 21133  Randallstown    Md   21133        NaN  NaN                NaN  39.3667  -76.8253  (39.3667,-76.8253)    NaN 29323 days   80     10          10  2018  (39.3667, -76.8253)\n",
       "1477          NaN          NaN   1478    18-0667  2018-12-06   2020-12-07    Gomez     Irvin        NaN  1944-08-31    Male  Black                                     NaN           NaN   NaN     NaN        NaN  NaN                NaN      nan       nan                 (,)    NaN 27125 days   74     12           6  2018           (nan, nan)\n",
       "643           NaN          NaN    644    20-0096  2020-03-02   2020-12-07    Wynne  Hardaway        NaN  1948-01-29    Male  Black                                     NaN           NaN   NaN     NaN        NaN  NaN                NaN      nan       nan                 (,)    NaN 26331 days   72      3           2  2020           (nan, nan)\n",
       "2349  1402956.375  592965.7500   2350    15-0119  2015-03-23   2021-08-24     Cobb    Jessie        NaN  1945-05-02    Male  Black                      607 Mount Holly St     Baltimore    Md   21229  Southwest  815           Edgewood  39.2944  -76.6798  (39.2944,-76.6798)    NaN 25527 days   69      3          23  2015  (39.2944, -76.6798)\n",
       "1144          NaN          NaN   1145    19-0279  2019-05-14   2020-12-07     Bass      Gold      Leroy  1950-04-08    Male  Black                                     NaN           NaN   NaN     NaN        NaN  NaN                NaN      nan       nan                 (,)    NaN 25238 days   69      5          14  2019           (nan, nan)\n",
       "...           ...          ...    ...        ...         ...          ...      ...       ...        ...         ...     ...    ...                                     ...           ...   ...     ...        ...  ...                ...      ...       ...                 ...    ...        ...  ...    ...         ...   ...                  ...\n",
       "2679  1426750.125  594844.9375   2680    10-0354  2010-08-31   2020-12-07     Hall   Anthony        NaN  1994-07-07    Male  Black                       1621 E Madison St     Baltimore    Md   21287    Eastern  323         Gay Street  39.2993  -76.5957  (39.2993,-76.5957)    NaN  5899 days   16      8          31  2010  (39.2993, -76.5957)\n",
       "2624  1409505.750  589857.1250   2625    11-0487  2011-11-21   2020-12-07     Holt  Arteesha        NaN  1996-04-23  Female  Black                       2526 W Lombard St     Baltimore    Md   21223  Southwest  835       Shipley Hill  39.2858  -76.6567  (39.2858,-76.6567)    NaN  5690 days   15     11          21  2011  (39.2858, -76.6567)\n",
       "2710  1426328.375  568545.4375   2711    09-0260  2009-08-13   2021-08-23   Dorsey    Raheem      Jabar  1994-06-02    Male  Black                        1107 Seaboard Ct     Baltimore    Md   21225   Southern  913           Brooklyn  39.2271  -76.5976  (39.2271,-76.5976)    NaN  5551 days   15      8          13  2009  (39.2271, -76.5976)\n",
       "206   1411806.250  595220.1250    207    21-0171  2021-06-04   2021-06-04   Chance     Brion        NaN  2019-02-15    Male  Black                        1022 Appleton St     Baltimore    Md   21217    Western  722  Midtown-Edmondson  39.3005  -76.6485  (39.3005,-76.6485)    NaN   840 days    2      6           4  2021  (39.3005, -76.6485)\n",
       "1428  1401533.250  595291.9375   1429    18-0720  2018-12-26   2021-04-15    Jones     David        NaN         NaT    Male  Black                       4119 Mountwood Rd     Baltimore    Md   21229  Southwest  815  Edmondson Village  39.3008  -76.6848  (39.3008,-76.6848)    NaN        NaT  nan     12          26  2018  (39.3008, -76.6848)\n",
       "\n",
       "[2748 rows x 29 columns]"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "age_df.sort_values('age', ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [],
   "source": [
    "## example to adjust\n",
    "#  urban_avg_fare = urban_cities_df.groupby([\"city\"]).mean()[\"fare\"]\n",
    "# urban_avg_fare.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [],
   "source": [
    "# dbc=pd.read_csv('districtsCount.csv', index_col=None)\n",
    "# dbc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [],
   "source": [
    "zips_by_count=df[['District', 'RowID','ZipCode',]].groupby(['District', 'ZipCode',]).agg('count')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [],
   "source": [
    "## remember to adjust column names after writing\n",
    "\n",
    "# zips_by_count.to_csv('zips_by_count.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [],
   "source": [
    "# import pandas as pd\n",
    "# import geopandas as gpd\n",
    "# from shapely import wkt  \n",
    "# import json  \n",
    "# geo_df= pd.read_csv(\"data.csv\", low_memory=False)\n",
    "# geo_df['geometry'] = geo_df['coordinates'].apply(wkt.loads)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [],
   "source": [
    "# from: https://geoffboeing.com/2015/10/exporting-python-data-geojson/\n",
    "\n",
    "# def df_to_geojson(df, properties, lat='Latitude', lon='Longitude'):\n",
    "#     geojson = {'type':'FeatureCollection', 'features':[]}\n",
    "#     for _, row in df.iterrows():\n",
    "#         feature = {'type':'Feature',\n",
    "#                    'properties':{},\n",
    "#                    'geometry':{'type':'Point',\n",
    "#                                'coordinates':[]}}\n",
    "#         feature['geometry']['coordinates'] = [row[lon],row[lat]]\n",
    "#         for prop in properties:\n",
    "#             feature['properties'][prop] = row[prop]\n",
    "#         geojson['features'].append(feature)\n",
    "#     return geojson"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [],
   "source": [
    "# df.sample()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [],
   "source": [
    "#  from: https://stackoverflow.com/questions/49243736/how-do-i-handle-object-of-type-timestamp-is-not-json-serializable-in-python\n",
    "\n",
    "# def cast_for_gsheets(df):\n",
    "#     # casting as string if not serializable\n",
    "#     for column, dt in zip(df.columns, df.dtypes):\n",
    "#         if dt.type not in [\n",
    "#             np.int64,\n",
    "#             np.float_,\n",
    "#             np.bool_,\n",
    "#         ]:\n",
    "#             df.loc[:, column] = df[column].astype(str)\n",
    "#     return df\n",
    "\n",
    "# df = cast_for_gsheets(df)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [],
   "source": [
    "# df.to_json('geoJson_data.json', orient=\"records\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [],
   "source": [
    "# result = df.to_json(\"geoJson_data.json\")\n",
    "# parsed = json.loads(result)\n",
    "# json.dumps(parsed, orient=\"records\", indent=4) \n",
    "# result"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [],
   "source": [
    "# cols = ['CaseNumber', 'CreatedDate', 'ModifiedDate', 'LastName', 'FirstName', 'DateOfBirth', 'Gender', 'Race', 'FullAddress', 'City', 'State', 'ZipCode', 'Post', 'Neighborhood', 'Age', 'year']\n",
    "# geojson = df_to_geojson(df, cols)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [],
   "source": [
    "# output_filename = 'geoJson_data.js'\n",
    "# with open(output_filename, 'w') as output_file:\n",
    "#     # output_file.write('var data = ')\n",
    "#     json.dump(geojson, output_filen, indent=2, orient) \n",
    "\n"
   ]
  }
 ],
 "metadata": {
  "interpreter": {
   "hash": "e7cb1b9ae4d417fedf7f40a8eec98f7cfbd359e096bd857395a915f4609834ce"
  },
  "kernelspec": {
   "display_name": "Python 3.8.5 64-bit (conda)",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.5"
  },
  "orig_nbformat": 4
 },
 "nbformat": 4,
 "nbformat_minor": 2
}