generate_summary.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import time\n",
    "from tqdm import tqdm\n",
    "import multiprocessing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv(\"/home/bh_parijat/data/master_mapped_data.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(4791859, 22)\n"
     ]
    }
   ],
   "source": [
    "print(df.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Index(['index', 'ARREST_KEY', 'ARREST_DATE', 'PD_CD', 'PD_DESC', 'KY_CD',\n",
      "       'OFNS_DESC', 'LAW_CODE', 'LAW_CAT_CD', 'ARREST_BORO', 'ARREST_PRECINCT',\n",
      "       'JURISDICTION_CODE', 'AGE_GROUP', 'PERP_SEX', 'PERP_RACE', 'X_COORD_CD',\n",
      "       'Y_COORD_CD', 'Latitude', 'Longitude', 'org_index', 'zipcode', 'city'],\n",
      "      dtype='object')\n"
     ]
    }
   ],
   "source": [
    "print(df.columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "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>index</th>\n",
       "      <th>ARREST_KEY</th>\n",
       "      <th>ARREST_DATE</th>\n",
       "      <th>PD_CD</th>\n",
       "      <th>PD_DESC</th>\n",
       "      <th>KY_CD</th>\n",
       "      <th>OFNS_DESC</th>\n",
       "      <th>LAW_CODE</th>\n",
       "      <th>LAW_CAT_CD</th>\n",
       "      <th>ARREST_BORO</th>\n",
       "      <th>...</th>\n",
       "      <th>AGE_GROUP</th>\n",
       "      <th>PERP_SEX</th>\n",
       "      <th>PERP_RACE</th>\n",
       "      <th>X_COORD_CD</th>\n",
       "      <th>Y_COORD_CD</th>\n",
       "      <th>Latitude</th>\n",
       "      <th>Longitude</th>\n",
       "      <th>org_index</th>\n",
       "      <th>zipcode</th>\n",
       "      <th>city</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>173130602</td>\n",
       "      <td>12/31/2017</td>\n",
       "      <td>566.0</td>\n",
       "      <td>MARIJUANA, POSSESSION</td>\n",
       "      <td>678.0</td>\n",
       "      <td>MISCELLANEOUS PENAL LAW</td>\n",
       "      <td>PL 2210500</td>\n",
       "      <td>V</td>\n",
       "      <td>Q</td>\n",
       "      <td>...</td>\n",
       "      <td>25-44</td>\n",
       "      <td>M</td>\n",
       "      <td>BLACK</td>\n",
       "      <td>1063056.0</td>\n",
       "      <td>207463.0</td>\n",
       "      <td>40.735772</td>\n",
       "      <td>-73.715638</td>\n",
       "      <td>0.0</td>\n",
       "      <td>11426</td>\n",
       "      <td>Bellerose</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>173114463</td>\n",
       "      <td>12/31/2017</td>\n",
       "      <td>478.0</td>\n",
       "      <td>THEFT OF SERVICES, UNCLASSIFIED</td>\n",
       "      <td>343.0</td>\n",
       "      <td>OTHER OFFENSES RELATED TO THEFT</td>\n",
       "      <td>PL 1651503</td>\n",
       "      <td>M</td>\n",
       "      <td>Q</td>\n",
       "      <td>...</td>\n",
       "      <td>25-44</td>\n",
       "      <td>M</td>\n",
       "      <td>ASIAN / PACIFIC ISLANDER</td>\n",
       "      <td>1009113.0</td>\n",
       "      <td>219613.0</td>\n",
       "      <td>40.769437</td>\n",
       "      <td>-73.910241</td>\n",
       "      <td>1.0</td>\n",
       "      <td>11105</td>\n",
       "      <td>Astoria</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>173113513</td>\n",
       "      <td>12/31/2017</td>\n",
       "      <td>849.0</td>\n",
       "      <td>NY STATE LAWS,UNCLASSIFIED VIOLATION</td>\n",
       "      <td>677.0</td>\n",
       "      <td>OTHER STATE LAWS</td>\n",
       "      <td>LOC000000V</td>\n",
       "      <td>V</td>\n",
       "      <td>K</td>\n",
       "      <td>...</td>\n",
       "      <td>18-24</td>\n",
       "      <td>M</td>\n",
       "      <td>BLACK</td>\n",
       "      <td>1010719.0</td>\n",
       "      <td>186857.0</td>\n",
       "      <td>40.679525</td>\n",
       "      <td>-73.904572</td>\n",
       "      <td>2.0</td>\n",
       "      <td>11207</td>\n",
       "      <td>Brooklyn</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>173113423</td>\n",
       "      <td>12/31/2017</td>\n",
       "      <td>101.0</td>\n",
       "      <td>ASSAULT 3</td>\n",
       "      <td>344.0</td>\n",
       "      <td>ASSAULT 3 &amp; RELATED OFFENSES</td>\n",
       "      <td>PL 1200001</td>\n",
       "      <td>M</td>\n",
       "      <td>M</td>\n",
       "      <td>...</td>\n",
       "      <td>25-44</td>\n",
       "      <td>M</td>\n",
       "      <td>WHITE</td>\n",
       "      <td>987831.0</td>\n",
       "      <td>217446.0</td>\n",
       "      <td>40.763523</td>\n",
       "      <td>-73.987074</td>\n",
       "      <td>3.0</td>\n",
       "      <td>10019</td>\n",
       "      <td>New York</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>173113421</td>\n",
       "      <td>12/31/2017</td>\n",
       "      <td>101.0</td>\n",
       "      <td>ASSAULT 3</td>\n",
       "      <td>344.0</td>\n",
       "      <td>ASSAULT 3 &amp; RELATED OFFENSES</td>\n",
       "      <td>PL 1200001</td>\n",
       "      <td>M</td>\n",
       "      <td>M</td>\n",
       "      <td>...</td>\n",
       "      <td>45-64</td>\n",
       "      <td>M</td>\n",
       "      <td>BLACK</td>\n",
       "      <td>987073.0</td>\n",
       "      <td>216078.0</td>\n",
       "      <td>40.759768</td>\n",
       "      <td>-73.989811</td>\n",
       "      <td>4.0</td>\n",
       "      <td>10036</td>\n",
       "      <td>New York</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 22 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   index  ARREST_KEY ARREST_DATE  PD_CD                               PD_DESC  \\\n",
       "0      0   173130602  12/31/2017  566.0                 MARIJUANA, POSSESSION   \n",
       "1      1   173114463  12/31/2017  478.0       THEFT OF SERVICES, UNCLASSIFIED   \n",
       "2      2   173113513  12/31/2017  849.0  NY STATE LAWS,UNCLASSIFIED VIOLATION   \n",
       "3      3   173113423  12/31/2017  101.0                             ASSAULT 3   \n",
       "4      4   173113421  12/31/2017  101.0                             ASSAULT 3   \n",
       "\n",
       "   KY_CD                        OFNS_DESC    LAW_CODE LAW_CAT_CD ARREST_BORO  \\\n",
       "0  678.0          MISCELLANEOUS PENAL LAW  PL 2210500          V           Q   \n",
       "1  343.0  OTHER OFFENSES RELATED TO THEFT  PL 1651503          M           Q   \n",
       "2  677.0                 OTHER STATE LAWS  LOC000000V          V           K   \n",
       "3  344.0     ASSAULT 3 & RELATED OFFENSES  PL 1200001          M           M   \n",
       "4  344.0     ASSAULT 3 & RELATED OFFENSES  PL 1200001          M           M   \n",
       "\n",
       "   ...  AGE_GROUP  PERP_SEX                 PERP_RACE X_COORD_CD Y_COORD_CD  \\\n",
       "0  ...      25-44         M                     BLACK  1063056.0   207463.0   \n",
       "1  ...      25-44         M  ASIAN / PACIFIC ISLANDER  1009113.0   219613.0   \n",
       "2  ...      18-24         M                     BLACK  1010719.0   186857.0   \n",
       "3  ...      25-44         M                     WHITE   987831.0   217446.0   \n",
       "4  ...      45-64         M                     BLACK   987073.0   216078.0   \n",
       "\n",
       "    Latitude  Longitude  org_index  zipcode       city  \n",
       "0  40.735772 -73.715638        0.0    11426  Bellerose  \n",
       "1  40.769437 -73.910241        1.0    11105    Astoria  \n",
       "2  40.679525 -73.904572        2.0    11207   Brooklyn  \n",
       "3  40.763523 -73.987074        3.0    10019   New York  \n",
       "4  40.759768 -73.989811        4.0    10036   New York  \n",
       "\n",
       "[5 rows x 22 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = df.drop(columns=['index','org_index'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "crimes_by_age = df.AGE_GROUP.value_counts().sort_values(ascending = False)[:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "25-44    2199242\n",
       "18-24    1272256\n",
       "45-64     879628\n",
       "<18       401573\n",
       "65+        38965\n",
       "Name: AGE_GROUP, dtype: int64"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "crimes_by_age[:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Number of Arrests')"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,8))\n",
    "sns.barplot(x=crimes_by_age.index, y = crimes_by_age )\n",
    "plt.title(\"Crime by Age Group\")\n",
    "plt.xlabel(\"Age group\")\n",
    "plt.ylabel(\"Number of Arrests\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "crimes_by_city = df.city.value_counts().sort_values(ascending = False)[:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Number of Arrests')"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(15,10))\n",
    "sns.barplot(x=crimes_by_city.index, y = crimes_by_city )\n",
    "plt.title(\"Crime by City\")\n",
    "plt.xlabel(\"City\")\n",
    "plt.ylabel(\"Number of Arrests\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['Year'] = df.ARREST_DATE.apply(lambda x:np.int(x.split(\"/\")[-1]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['Month'] = df.ARREST_DATE.apply(lambda x:np.int(x.split(\"/\")[0]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['Day'] = df.ARREST_DATE.apply(lambda x:np.int(x.split(\"/\")[1]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "crimes_by_year = df.Year.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Number of Arrests')"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(15,10))\n",
    "sns.barplot(x=crimes_by_year.index, y = crimes_by_year,palette='deep' )\n",
    "plt.title(\"Crime by year\")\n",
    "plt.xlabel(\"Year\")\n",
    "plt.ylabel(\"Number of Arrests\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'12/31/2018'"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df.Year == 2018]['ARREST_DATE'].max()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2018, 2006)"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.Year.max(), df.Year.min()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "arrests_by_month = df.Month.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Number of Arrests')"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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nrPUeAAAAbIe5fiZyOmL48SQXJTk9yReSfKu7fzRtckGSfabb+yT5apJMj387yZ4r17d6zlrre67zHlvP95SqOquqzrr44ou350cFAADYJcw1Irv7qu4+OMm+mR05vOtqm03fa43Hrq/11eZ7ZXcf0t2HbN68ebVNAAAAWGFDrs7a3d9KcmaSQ5PsUVWbpof2TXLhdPuCJPslyfT4rZJcunJ9q+estX7JOu8BAADAdpjn1Vk3V9Ue0+2bJPmlJOcmeXeSR0+bHZ3kLdPtU6b7mR5/V3f3tH7UdPXWOyQ5MMmHk3wkyYHTlVhvlNnFd06ZnrPWewAAALAdNm17k+vsdklOmK6ieoMkJ3X3W6vqM0lOrKr/nuRjSY6btj8uyWur6rzMjkAelSTdfU5VnZTkM0l+lOSp3X1VklTV05KclmS3JMd39znTaz17jfcAAABgO8wtIrv7k0nuscr6+Zl9PnLr9e8lecwar/XCJC9cZf3UJKeOvgcAAADbZ0M+EwkAAMDOQUQCAAAwTEQCAAAwTEQCAAAwTEQCAAAwTEQCAAAwTEQCAAAwTEQCAAAwTEQCAAAwTEQCAAAwTEQCAAAwTEQCAAAwTEQCAAAwTEQCAAAwTEQCAAAwTEQCAAAwTEQCAAAwTEQCAAAwTEQCAAAwTEQCAAAwTEQCAAAwTEQCAAAwTEQCAAAwTEQCAAAwTEQCAAAwTEQCAAAwTEQCAAAwTEQCAAAwTEQCAAAwTEQCAAAwTEQCAAAwTEQCAAAwTEQCAAAwTEQCAAAwTEQCAAAwbJsRWVV/VFW3rKobVtUZVXVJVT1hI4YDAABguYwciXxwd1+e5GFJLkhy5yS/PdepAAAAWEojEXnD6ftDkry+uy+d4zwAAAAssU0D2/xdVX02yT8l+c2q2pzke/MdCwAAgGU0ciTyeUnuk+SQ7v5hkiuTHDnXqQAAAFhKIxH5ge6+rLuvSpLu/m6St893LAAAAJbRmqezVtVPJNknyU2q6h5JanrolkluugGzAQAAsGTW+0zkYUn+XZJ9k7x0xfoVSZ47x5kAAABYUmtGZHefkOSEqvrV7n7DBs4EAADAkhr5TOS+VXXLmvmrqvpoVT147pMBAACwdEYi8t939+VJHpzktkmenORFc50KAACApTQSkVsuqPOQJK/q7k+sWAMAAGAXMhKRZ1fVOzOLyNOq6hZJrp7vWAAAACyj9a7OusUxSQ5Ocn53X1lVe2Z2SisAAAC7mJEjkZ3koCRPn+7fLMnuc5sIAACApTUSkX+Z5D5JHjvdvyLJX8xtIgAAAJbWyOms9+7ue1bVx5Kkuy+rqhvNeS4AAACW0MiRyB9W1W6ZndaaqtocF9YBAADYJY1E5MuTvCnJbavqhUnel+R/zHUqAAAAltI2T2ft7tdV1dlJHpTZ74d8ZHefO/fJAAAAWDrrRmRV3SDJJ7v7bkk+uzEjAQAAsKzWPZ21u69O8omq2n+D5gEAAGCJjVyd9XZJzqmqDyf57pbF7j5yblMBAACwlEYi8vlznwIAAIAdwrY+E7lbkv/W3b+0QfMAAACwxLb1mcirklxZVbfaoHkAAABYYiOns34vyaeq6vT8y89EPn1uUwEAALCURiLybdPXSj2HWQAAAFhy24zI7j5h5f2q2i/JUXObCAAAgKW17mcit6iqvarqN6rqvUnOTLL3XKcCAABgKa15JLKqbpHkUUkel+TOSd6U5I7dve8GzQYAAMCSWe901ouSfDjJ7yZ5X3d3VT1qY8YCAABgGa13Outzk+ye5BVJnlNVP7UxIwEAALCs1ozI7n5Zd987yZFJKsmbk9y+qp5dVXfeqAEBAABYHtu8sE53n9/dL+zun0ny80luleTtc58MAACApTN0ddYtuvtT3f3c7nZqKwAAwC7oWkUkAAAAuzYRCQAAwLA1I7Kqzpi+v3jjxgEAAGCZrfd7Im9XVb+Y5MiqOjGzK7T+s+7+6FwnAwAAYOmsF5G/l+TYJPsmeelWj3WSB85rKAAAAJbTmhHZ3ScnObmq/lt3v2ADZwIAAGBJrXckMknS3S+oqiOT3G9aOrO73zrfsQAAAFhG27w6a1X9YZJnJPnM9PWMaQ0AAIBdzDaPRCZ5aJKDu/vqJKmqE5J8LMlz5jkYAAAAy2f090TuseL2reYxCAAAAMtv5EjkHyb5WFW9O7Nf83G/OAoJAACwSxq5sM7rq+rMJD+fWUQ+u7u/Pu/BAAAAWD4jRyLT3V9LcsqcZwEAAGDJjX4mEgAAAEQkAAAA49aNyKq6QVV9eqOGAQAAYLmtG5HT74b8RFXtv0HzAAAAsMRGLqxzuyTnVNWHk3x3y2J3Hzm3qQAAAFhKIxH5/LlPAQAAwA5h5PdEvqeqfjLJgd39f6vqpkl2m/9oAAAALJttXp21qn4tyclJ/te0tE+SN89zKAAAAJbTyK/4eGqS+ya5PEm6+/NJbjvPoQAAAFhOIxH5/e7+wZY7VbUpSc9vJAAAAJbVSES+p6qem+QmVfXLSf5Pkr+b71gAAAAso5GIPDbJxUk+leTXk5ya5HfnORQAAADLaeTqrFdX1QlJPpTZaayf626nswIAAOyCthmRVfXQJP8zyReSVJI7VNWvd/fb5z0cAAAAy2WbEZnkT5I8oLvPS5Kq+qkkb0siIgEAAHYxI5+JvGhLQE7OT3LRnOYBAABgia15JLKqfmW6eU5VnZrkpMw+E/mYJB/ZgNkAAABYMuudzvrwFbe/keQXp9sXJ7n13CYCAABgaa0Zkd395I0cBAAAgOU3cnXWOyT5T0kOWLl9dx85v7EAAABYRiNXZ31zkuOS/F2Sq+c7DgAAAMtsJCK/190vn/skAAAALL2RiPyzqnpekncm+f6Wxe7+6NymAgAAYCmNROTPJHlikgfmx6ez9nQfAACAXchIRD4qyR27+wfzHgYAAIDldoOBbT6RZI9r+8JVtV9Vvbuqzq2qc6rqGdP6barq9Kr6/PT91tN6VdXLq+q8qvpkVd1zxWsdPW3/+ao6esX6z1XVp6bnvLyqar33AAAAYPuMROTeST5bVadV1Slbvgae96Mkz+ruuyY5NMlTq+qgJMcmOaO7D0xyxnQ/SY5IcuD09ZQkr0hmQZjkeUnuneReSZ63IgpfMW275XmHT+trvQcAAADbYeR01uddlxfu7q8l+dp0+4qqOjfJPkkekeT+02YnJDkzybOn9dd0dyf5YFXtUVW3m7Y9vbsvTZKqOj3J4VV1ZpJbdvcHpvXXJHlkkrev8x4AAABsh21GZHe/Z3vfpKoOSHKPJB9KsvcUmOnur1XVbafN9kny1RVPu2BaW2/9glXWs857bD3XUzI7kpn999//Ov50AAAAu45tns5aVVdU1eXT1/eq6qqqunz0Darq5knekOSZ3b3e82qVtb4O68O6+5XdfUh3H7J58+Zr81QAAIBd0jYjsrtv0d23nL52T/KrSf585MWr6oaZBeTruvuN0/I3ptNUM32/aFq/IMl+K56+b5ILt7G+7yrr670HAAAA22Hkwjr/Qne/OQO/I3K6UupxSc7t7peueOiUJFuusHp0kresWH/SdJXWQ5N8ezol9bQkD66qW08X1HlwktOmx66oqkOn93rSVq+12nsAAACwHbb5mciq+pUVd2+Q5JCMnTZ63yRPTPKpqvr4tPbcJC9KclJVHZPkK0keMz12apKHJDkvyZVJnpwk3X1pVb0gyUem7f5gy0V2kvxGklcnuUlmF9R5+7S+1nsAAACwHUauzvrwFbd/lORLmV39dF3d/b6s/rnFJHnQKtt3kqeu8VrHJzl+lfWzktxtlfVvrvYeAAAAbJ+Rq7M+eSMGAQAAYPmtGZFV9XvrPK+7+wVzmAcAAIAltt6RyO+usnazJMck2TOJiAQAANjFrBmR3f0nW25X1S2SPCOzi92cmORP1noeAAAAO691PxNZVbdJ8ltJHp/khCT37O7LNmIwAAAAls96n4l8SZJfSfLKJD/T3d/ZsKkAAABYSjdY57FnJbl9kt9NcmFVXT59XVFVl2/MeAAAACyT9T4TuV5gAgAAsAsSigAAAAwTkQAAAAwTkQAAAAwTkQAAAAwTkQAAAAwTkQAAAAwTkQAAAAwTkQAAAAwTkQAAAAwTkQAAAAwTkQAAAAwTkQAAAAwTkQAAAAwTkQAAAAwTkQAAAAwTkQAAAAwTkQAAAAwTkQAAAAwTkQAAAAwTkQAAAAwTkQAAAAwTkQAAAAwTkQAAAAwTkQAAAAwTkQAAAAwTkQAAAAwTkQAAAAwTkQAAAAwTkQAAAAwTkQAAAAwTkQAAAAwTkQAAAAwTkQAAAAwTkQAAAAwTkQAAAAwTkQAAAAwTkQAAAAwTkQAAAAwTkQAAAAwTkQAAAAwTkQAAAAwTkQAAAAwTkQAAAAwTkQAAAAwTkQAAAAwTkQAAAAwTkQAAAAwTkQAAAAwTkQAAAAwTkQAAAAwTkQAAAAwTkQAAAAwTkQAAAAwTkQAAAAwTkQAAAAwTkQAAAAwTkQAAAAwTkQAAAAwTkQAAAAwTkQAAAAwTkQAAAAwTkQAAAAwTkQAAAAwTkQAAAAwTkQAAAAwTkQAAAAwTkQAAAAwTkQAAAAwTkQAAAAwTkQAAAAwTkQAAAAwTkQAAAAwTkQAAAAwTkQAAAAwTkQAAAAwTkQAAAAwTkQAAAAwTkQAAAAwTkQAAAAwTkQAAAAwTkQAAAAwTkQAAAAwTkQAAAAwTkQAAAAwTkQAAAAwTkQAAAAwTkQAAAAwTkQAAAAwTkQAAAAwTkQAAAAwTkQAAAAwTkQAAAAwTkQAAAAwTkQAAAAwTkQAAAAwTkQAAAAwTkQAAAAwTkQAAAAwTkQAAAAwTkQAAAAwTkQAAAAwTkQAAAAybW0RW1fFVdVFVfXrF2m2q6vSq+vz0/dbTelXVy6vqvKr6ZFXdc8Vzjp62/3xVHb1i/eeq6lPTc15eVbXeewAAALD95nkk8tVJDt9q7dgkZ3T3gUnOmO4nyRFJDpy+npLkFcksCJM8L8m9k9wryfNWROErpm23PO/wbbwHAAAA22luEdnd701y6VbLj0hywnT7hCSPXLH+mp75YJI9qup2SQ5Lcnp3X9rdlyU5Pcnh02O37O4PdHcnec1Wr7XaewAAALCdNvozkXt399eSZPp+22l9nyRfXbHdBdPaeusXrLK+3ntcQ1U9parOqqqzLr744uv8QwEAAOwqluXCOrXKWl+H9Wulu1/Z3Yd09yGbN2++tk8HAADY5Wx0RH5jOhU10/eLpvULkuy3Yrt9k1y4jfV9V1lf7z0AAADYThsdkack2XKF1aOTvGXF+pOmq7QemuTb06mopyV5cFXderqgzoOTnDY9dkVVHTpdlfVJW73Wau8BAADAdto0rxeuqtcnuX+SvarqgsyusvqiJCdV1TFJvpLkMdPmpyZ5SJLzklyZ5MlJ0t2XVtULknxk2u4PunvLxXp+I7MrwN4kydunr6zzHgAAAGynuUVkdz92jYcetMq2neSpa7zO8UmOX2X9rCR3W2X9m6u9BwAAANtvWS6sAwAAwA5ARAIAADBMRAIAADBMRAIAADBMRAIAADBMRAIAADBMRAIAADBMRAIAADBMRAIAADBMRAIAADBMRAIAADBMRAIAADBMRAIAADBMRAIAADBMRAIAADBMRAIAADBMRAIAADBMRAIAADBMRAIAADBMRAIAADBMRAIAADBMRAIAADBMRAIAADBMRAIAADBMRAIAADBMRAIAADBMRAIAADBMRAIAADBMRAIAADBMRAIAADBMRAIAADBMRAIAADBMRAIAADBMRAIAADBMRAIAADBMRAIAADBMRAIAADBMRAIAADBMRAIAADBMRAIAADBMRAIAADBMRAIAADBMRAIAADBMRAIAADBMRAIAADBMRAIAADBMRAIAADBMRAIAADBMRAIAADBMRAIAADBMRAIAADBMRAIAADBMRAIAADBMRAIAADBMRAIAADBMRAIAADBMRAIAADBMRAIAADBMRAIAADBMRAIAADBMRAIAADBMRAIAADBMRAIAADBMRAIAADBMRAIAADBMRAIAADBMRAIAADBMRAIAADBMRAIAADBMRAIAADBMRAIAADBMRAIAADBMRAIAADBMRAIAADB5vAfMAAAJbUlEQVRMRAIAADBMRAIAADBMRAIAADBMRAIAADBMRAIAADBMRAIAADBMRAIAADBMRAIAADBMRAIAADBMRAIAADBMRAIAADBMRAIAADBMRAIAADBMRAIAADBMRAIAADBMRAIAADBMRAIAADBMRAIAADBMRAIAADBMRAIAADBMRAIAADBMRAIAADBMRAIAADBMRAIAADBMRAIAADBMRAIAADBMRAIAADBMRAIAADBMRAIAADBMRAIAADBMRAIAADBMRAIAADBMRAIAADBMRAIAADBMRAIAADBMRAIAADBsp43Iqjq8qj5XVedV1bGLngcAAGBnsFNGZFXtluQvkhyR5KAkj62qgxY7FQAAwI5vp4zIJPdKcl53n9/dP0hyYpJHLHgmAACAHV5196JnuN5V1aOTHN7d/2G6/8Qk9+7up2213VOSPGW6e5ckn9vQQde2V5JLFj3EkrFPVme/rM5+WZ39ck32yersl9XZL6uzX67JPlmd/bK6ZdovP9ndm7e10aaNmGQBapW1a9Ryd78yySvnP861U1Vndfchi55jmdgnq7NfVme/rM5+uSb7ZHX2y+rsl9XZL9dkn6zOflndjrhfdtbTWS9Ist+K+/smuXBBswAAAOw0dtaI/EiSA6vqDlV1oyRHJTllwTMBAADs8HbK01m7+0dV9bQkpyXZLcnx3X3Ogse6NpbuFNslYJ+szn5Znf2yOvvlmuyT1dkvq7NfVme/XJN9sjr7ZXU73H7ZKS+sAwAAwHzsrKezAgAAMAciEgAAgGEicolU1fFVdVFVfXrRsyyLqtqvqt5dVedW1TlV9YxFz7QMqmr3qvpwVX1i2i/PX/RMy6Kqdquqj1XVWxc9y7Koqi9V1aeq6uNVddai51kWVbVHVZ1cVZ+d/h1zn0XPtGhVdZfpz8mWr8ur6pmLnmvRquo/T/+u/XRVvb6qdl/0TMugqp4x7ZNzduU/J6v9/a2qblNVp1fV56fvt17kjIuwxn55zPTn5eqq2qF+pcX1ZY398pLpv0WfrKo3VdUei5xxhIhcLq9Ocviih1gyP0ryrO6+a5JDkzy1qg5a8EzL4PtJHtjdd09ycJLDq+rQBc+0LJ6R5NxFD7GEHtDdB+9ov4dqzv4syTu6+18luXv8uUl3f276c3Jwkp9LcmWSNy14rIWqqn2SPD3JId19t8wu2HfUYqdavKq6W5JfS3KvzP75eVhVHbjYqRbm1bnm39+OTXJGdx+Y5Izp/q7m1bnmfvl0kl9J8t4Nn2Z5vDrX3C+nJ7lbd/9skn9I8pyNHuraEpFLpLvfm+TSRc+xTLr7a9390en2FZn9JW+fxU61eD3znenuDaevXf4qWVW1b5KHJvmrRc/CcquqWya5X5LjkqS7f9Dd31rsVEvnQUm+0N1fXvQgS2BTkptU1aYkN43fPZ0kd03ywe6+srt/lOQ9SR614JkWYo2/vz0iyQnT7ROSPHJDh1oCq+2X7j63uz+3oJGWwhr75Z3TP0dJ8sHMfsf9UhOR7DCq6oAk90jyocVOshym0zY/nuSiJKd3t/2S/GmS30ly9aIHWTKd5J1VdXZVPWXRwyyJOya5OMmrptOf/6qqbrbooZbMUUlev+ghFq27/zHJHyf5SpKvJfl2d79zsVMthU8nuV9V7VlVN03ykCT7LXimZbJ3d38tmf0P8SS3XfA87Dj+fZK3L3qIbRGR7BCq6uZJ3pDkmd19+aLnWQbdfdV0ytm+Se41nVq0y6qqhyW5qLvPXvQsS+i+3X3PJEdkdkr4/RY90BLYlOSeSV7R3fdI8t3smqebraqqbpTkyCT/Z9GzLNr0WbZHJLlDktsnuVlVPWGxUy1ed5+b5MWZnYb3jiSfyOwjKMB1VFX/NbN/jl636Fm2RUSy9KrqhpkF5Ou6+42LnmfZTKfgnRmfp71vkiOr6ktJTkzywKr668WOtBy6+8Lp+0WZfb7tXoudaClckOSCFUfwT84sKpk5IslHu/sbix5kCfxSki9298Xd/cMkb0zyCwueaSl093Hdfc/uvl9mp+d9ftEzLZFvVNXtkmT6ftGC52HJVdXRSR6W5PHdvfQfURKRLLWqqsw+s3Rud7900fMsi6ravOXKXVV1k8z+kvPZxU61WN39nO7et7sPyOw0vHd19y5/tKCqblZVt9hyO8mDMzsNbZfW3V9P8tWqusu09KAkn1ngSMvmsXEq6xZfSXJoVd10+m/Sg+IiTEmSqrrt9H3/zC6W4s/Mj52S5Ojp9tFJ3rLAWVhyVXV4kmcnObK7r1z0PCM2LXoAfqyqXp/k/kn2qqoLkjyvu49b7FQLd98kT0zyqenzf0ny3O4+dYEzLYPbJTmhqnbL7H8GndTdfqUFq9k7yZtmf/fNpiR/093vWOxIS+M/JXnddOrm+UmevOB5lsL0+bZfTvLri55lGXT3h6rq5CQfzew0s48leeVip1oab6iqPZP8MMlTu/uyRQ+0CKv9/S3Ji5KcVFXHZPY/Ih6zuAkXY439cmmS/zfJ5iRvq6qPd/dhi5ty462xX56T5MZJTp/+e/3B7v6PCxtyQO0AR0sBAABYEk5nBQAAYJiIBAAAYJiIBAAAYJiIBAAAYJiIBAAAYJiIBIDrUVV1Vb12xf1NVXVxVV2nX8NTVXtU1W+uuH//6/paAHB9EJEAcP36bpK7VdVNpvu/nOQft+P19kjym9vcCgA2iIgEgOvf25M8dLr92CSv3/JAVd2mqt5cVZ+sqg9W1c9O679fVcdX1ZlVdX5VPX16youS/FRVfbyqXjKt3byqTq6qz1bV62r67dQAsBFEJABc/05MclRV7Z7kZ5N8aMVjz0/yse7+2STPTfKaFY/9qySHJblXkudV1Q2THJvkC919cHf/9rTdPZI8M8lBSe6Y5L7z/GEAYCURCQDXs+7+ZJIDMjsKeepWD//rJK+dtntXkj2r6lbTY2/r7u939yVJLkqy9xpv8eHuvqC7r07y8em9AGBDbFr0AACwkzolyR8nuX+SPVesr3bqaU/fv79i7aqs/d/p0e0A4HrnSCQAzMfxSf6guz+11fp7kzw+mV1pNckl3X35Oq9zRZJbzGVCALgO/J9LAJiD7r4gyZ+t8tDvJ3lVVX0yyZVJjt7G63yzqt5fVZ/O7II9b7u+ZwWAa6O6e9tbAQAAQJzOCgAAwLUgIgEAABgmIgEAABgmIgEAABgmIgEAABgmIgEAABgmIgEAABj2/wNR2R9DlfcH+QAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 1080x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(15,10))\n",
    "sns.barplot(x=arrests_by_month.index, y = arrests_by_month )\n",
    "plt.title(\"Arrests by Month\")\n",
    "plt.xlabel(\"Month\")\n",
    "plt.ylabel(\"Number of Arrests\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "arrests_by_month = arrests_by_month/ arrests_by_month.min()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Number of Arrests')"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(15,10))\n",
    "sns.barplot(x=arrests_by_month.index, y = arrests_by_month,palette='deep')\n",
    "plt.title(\"Arrests by Month\")\n",
    "plt.xlabel(\"Month\")\n",
    "plt.ylabel(\"Number of Arrests\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "31"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[(df.Year==2006) & (df.Month == 1)].Day.max()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[(df.Year==2006) & (df.Month == 1)].Day.min()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "date = pd.to_datetime('01-01-2006')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Timestamp('2006-01-01 00:00:00')"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "date"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "#pd.timedelta_range(start='1-1-2006',end='3-02-2006',freq='1d')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Timestamp('2006-01-02 00:00:00')"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "date+pd.to_timedelta(1,unit='d')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "\n",
    "def count_days_in_month(date = None):\n",
    "    \n",
    "    print(date)\n",
    "    day, month, year = None, None, None\n",
    "\n",
    "    map_days = {}\n",
    "    next_date = None\n",
    "    while day!=31 or month!=12 or year!=2018:\n",
    "    \n",
    "        try:\n",
    "\n",
    "\n",
    "            day = date.day\n",
    "\n",
    "            month = date.month\n",
    "\n",
    "            year = date.year\n",
    "\n",
    "            #print(day,month,year)\n",
    "            map_days[month] = map_days.get(month,0) + 1\n",
    "\n",
    "            next_date = date + pd.to_timedelta(1.0,unit='d')\n",
    "            date = next_date \n",
    "\n",
    "        except:\n",
    "            print(next_date)\n",
    "\n",
    "    \n",
    "    return map_days\n",
    "    \n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "#print(map_days[1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2006-01-01 00:00:00\n"
     ]
    }
   ],
   "source": [
    "map_days = count_days_in_month(date = date)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "per_day_crime = df.Month.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3     436176\n",
       "5     425422\n",
       "1     418911\n",
       "4     414315\n",
       "10    412236\n",
       "8     412110\n",
       "7     400632\n",
       "6     398271\n",
       "2     390476\n",
       "9     385587\n",
       "11    364837\n",
       "12    332886\n",
       "Name: Month, dtype: int64"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "per_day_crime"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "for i in range(1,13):\n",
    "    per_day_crime[i]/= map_days[1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3     1082\n",
       "5     1055\n",
       "1     1039\n",
       "4     1028\n",
       "10    1022\n",
       "8     1022\n",
       "7      994\n",
       "6      988\n",
       "2      968\n",
       "9      956\n",
       "11     905\n",
       "12     826\n",
       "Name: Month, dtype: int64"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "per_day_crime"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Number of Arrests')"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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AYGBbVl0Am8Ppz/vVVZewUD/55JetugQAANiUjBQCAAAMTCgEAAAYmFAIAAAwMKEQAABgYG40AwCwSb3v75+x6hIW6q4PeMaqSwBipBAAAGBoQiEAAMDAhEIAAICBCYUAAAADEwoBAAAGJhQCAAAMTCgEAAAYmFAIAAAwMKEQAABgYEIhAADAwIRCAACAgQmFAAAAAxMKAQAABiYUAgAADEwoBAAAGJhQCAAAMDChEAAAYGBCIQAAwMCEQgAAgIEJhQAAAAMTCgEAAAYmFAIAAAxMKAQAABiYUAgAADCwLasuAAAAuOJ++cknrrqEhXrN8x6x6hL2WUYKAQAABiYUAgAADEwoBAAAGJhQCAAAMDChEAAAYGBCIQAAwMCEQgAAgIEJhQAAAAMTCgEAAAYmFAIAAAxMKAQAABiYUAgAADAwoRAAAGBgQiEAAMDAhEIAAICBCYUAAAADEwoBAAAGJhQCAAAMTCgEAAAYmFAIAAAwMKEQAABgYEIhAADAwIRCAACAgQmFAAAAAxMKAQAABiYUAgAADEwoBAAAGJhQCAAAMDChEAAAYGBCIQAAwMCEQgAAgIEJhQAAAAMTCgEAAAYmFAIAAAxMKAQAABiYUAgAADAwoRAAAGBgW1ZdAAAAwKKc/rxfXXUJC/OTT37ZHvkcI4UAAAADEwoBAAAGJhQCAAAMTCgEAAAYmFAIAAAwMKEQAABgYEIhAADAwIRCAACAga0kFFbVb1fVJ6rq41V1UlVdrapuUlUfqKrPVNXfVNVVpr5XndbPmrYftoqaAQAA9kVLD4VVdXCSxyc5ortvleRKSR6W5LlJXtTdhye5MMkx01uOSXJhd/9IkhdN/QAAANgDVjV9dEuSq1fVliTXSHJuknskOXnafkKSB03LR07rmbbfs6pqibUCAADss5YeCrv7S0men+QLmYXBryc5PcnXuvuSqdu2JAdPywcn+eL03kum/vvv+LlVdWxVnVZVp11wwQWLPQgAAIB9xCqmj14vs9G/myS5YZJrJrnvGl17+1vW2fb9hu7ju/uI7j5i69ate6pcAACAfdoqpo/+bJL/290XdPd3k7whyU8n2W+aTpokhyQ5Z1reluTQJJm2XzfJV5dbMgAAwL5pFaHwC0nuVFXXmK4NvGeSTyZ5d5KHTH2OSvLmafmUaT3T9nd1904jhQAAAPzgVnFN4Qcyu2HMGUk+NtVwfJKnJHliVZ2V2TWDL5/e8vIk+0/tT0xy3LJrBgAA2Fdt2X2XPa+7n57k6Ts0n53kDmv0/VaShy6jLgAAgNGs6pEUAAAAbAJCIQAAwMCEQgAAgIEJhQAAAAMTCgEAAAYmFAIAAAxMKAQAABiYUAgAADAwoRAAAGBgQiEAAMDAhEIAAICBCYUAAAADEwoBAAAGJhQCAAAMTCgEAAAYmFAIAAAwMKEQAABgYEIhAADAwIRCAACAgQmFAAAAAxMKAQAABiYUAgAADEwoBAAAGJhQCAAAMDChEAAAYGBCIQAAwMCEQgAAgIEJhQAAAAMTCgEAAAYmFAIAAAxMKAQAABiYUAgAADAwoRAAAGBgQiEAAMDAhEIAAICBCYUAAAADEwoBAAAGJhQCAAAMTCgEAAAYmFAIAAAwMKEQAABgYEIhAADAwIRCAACAgQmFAAAAAxMKAQAABiYUAgAADEwoBAAAGJhQCAAAMDChEAAAYGBCIQAAwMCEQgAAgIEJhQAAAAMTCgEAAAYmFAIAAAxMKAQAABiYUAgAADAwoRAAAGBgQiEAAMDAhEIAAICBCYUAAAADEwoBAAAGJhQCAAAMbLehsKqeV1XXqaorV9U7q+orVfUryygOAACAxdrISOG9uvuiJA9Isi3Jjyb5nYVWBQAAwFJsJBReeXq9X5KTuvurC6wHAACAJdqygT5/V1WfSvLvSX6jqrYm+dZiywIAAGAZNjJS+PQkd05yRHd/N8nFSR640KoAAABYio2Ewn/u7gu7+3tJ0t3fTPK2xZYFAADAMuxy+mhV/XCSg5Ncvapul6SmTddJco0l1AYAAMCCrXdN4b2TPDrJIUleONf+jSRPW2BNAAAALMkuQ2F3n5DkhKr6xe5+/RJrAgAAYEk2ck3hIdPD66uqXlZVZ1TVvRZeGQAAAAu3kVD4X6aH198ryQFJjk7ynIVWBQAAwFJsJBRuv8HM/ZK8srs/MtcGAADAXmwjofD0qnpHZqHwH6rq2kkuXWxZAAAALMN6dx/d7pgkt01ydndfXFX7ZzaFFAAAgL3cRkYKO8ktkzx+Wr9mkqstrCIAAACWZiOh8M+T3DnJw6f1byT5s4VVBAAAwNJsZProHbv79lX1oSTp7gur6ioLrgsAAIAl2MhI4Xer6kqZTSNNVW2NG80AAADsEzYSCl+S5I1JDqiqZyX5pyR/tNCqAAAAWIrdTh/t7hOr6vQk98zs+YQP6u4zF14ZAAAAC7duKKyqH0ry0e6+VZJPLackAAAAlmXd6aPdfWmSj1TVjZZUDwAAAEu0kbuPHpTkE1X1wSTf3N7Y3Q9cWFUAAAAsxUZC4TMXXgUAAAArsbtrCq+U5Pe6+2eXVA8AAABLtLtrCr+X5OKquu6e3GlV7VdVJ1fVp6rqzKq6c1Vdv6pOrarPTK/Xm/pWVb2kqs6qqo9W1e33ZC0AAAAj28j00W8l+VhVnZrLXlP4+Cuw3xcneXt3P6SqrpLkGkmeluSd3f2cqjouyXFJnpLkvkkOn77umOSl0ysAAABX0EZC4Vumr3l9eXdYVddJctckj06S7v5Oku9U1ZFJ7j51OyHJezILhUcmeXV3d5L3T6OMB3X3uZe3BgAAAGY28vD6E+bXq+rQJA+7Avu8aZILkryyqm6T5PQkT0hy4Pag193nVtUBU/+Dk3xx7v3bprbLhMKqOjbJsUlyoxt5ggYAAMBGrHtN4XZVdYOq+q9V9b7MRvAOvAL73JLk9kle2t23y2xK6nHr7X6Ntp1GKrv7+O4+oruP2Lp16xUoDwAAYBy7DIVVde2qelRVvT3JB5P8SJKbdvfNuvu/XYF9bkuyrbs/MK2fnFlIPK+qDpr2fVCS8+f6Hzr3/kOSnHMF9g8AAMBkvZHC85Mck+RZSW7W3U9K8p0rusPu/nKSL1bVj01N90zyySSnJDlqajsqyZun5VOSPGq6C+mdknzd9YQAAAB7xnrXFD4ts2sHX5rkNVX1N3twv7+Z5MTpzqNnJzk6s4D6uqo6JskXkjx06vvWJPdLclaSi6e+AAAA7AG7DIXd/aIkL6qqmyZ5eJI3JblhVT0lyRu7+/9c3p1294eTHLHGpnuu0beTPPby7gsAAIBd2+2NZrr77O5+Vnf/RJKfSnLdJG9beGUAAAAs3IbuPrpdd3+su5/W3TdbVEEAAAAszw8UCgEAANi3CIUAAAADW+85he+cXp+7vHIAAABYpvUeSXFQVd0tyQOr6rVJan5jd5+x0MoAAABYuPVC4e8nOS7JIUleuMO2TnKPRRUFAADAcqz3nMKTk5xcVb/X3X+4xJoAAABYkvVGCpMk3f2HVfXAJHedmt7T3X+/2LIAAABYht3efbSqnp3kCUk+OX09YWoDAABgL7fbkcIk909y2+6+NEmq6oQkH0ry1EUWBgAAwOJt9DmF+80tX3cRhQAAALB8GxkpfHaSD1XVuzN7LMVdY5QQAABgn7CRG82cVFXvSfJTmYXCp3T3lxddGAAAAIu3kZHCdPe5SU5ZcC0AAAAs2UavKQQAAGAfJBQCAAAMbN1QWFU/VFUfX1YxAAAALNe6oXB6NuFHqupGS6oHAACAJdrIjWYOSvKJqvpgkm9ub+zuBy6sKgAAAJZiI6HwmQuvAgAAgJXYyHMK31tVN05yeHf/r6q6RpIrLb40AAAAFm23dx+tql9LcnKS/zk1HZzkTYssCgAAgOXYyCMpHpvkLkkuSpLu/kySAxZZFAAAAMuxkVD47e7+zvaVqtqSpBdXEgAAAMuykVD43qp6WpKrV9XPJfnbJH+32LIAAABYho2EwuOSXJDkY0kek+StSf77IosCAABgOTZy99FLq+qEJB/IbNrop7vb9FEAAIB9wG5DYVXdP8lfJPlskkpyk6p6THe/bdHFAQAAsFgbeXj9C5L8THeflSRVdbMkb0kiFAIAAOzlNnJN4fnbA+Hk7CTnL6geAAAAlmiXI4VV9eBp8RNV9dYkr8vsmsKHJvmXJdQGAADAgq03ffTn55bPS3K3afmCJNdbWEUAAAAszS5DYXcfvcxCAAAAWL6N3H30Jkl+M8lh8/27+4GLKwsAAIBl2MjdR9+U5OVJ/i7JpYstBwAAgGXaSCj8Vne/ZOGVAAAAsHQbCYUvrqqnJ3lHkm9vb+zuMxZWFQAAAEuxkVD4E0kemeQe+f700Z7WAQAA2IttJBT+QpKbdvd3Fl0MAAAAy/VDG+jzkST7LboQAAAAlm8jI4UHJvlUVf1LLntNoUdSAAAA7OU2EgqfvvAqAAAAWIndhsLufu8yCgEAAGD5dhsKq+obmd1tNEmukuTKSb7Z3ddZZGEAAAAs3kZGCq89v15VD0pyh4VVBAAAwNJs5O6jl9Hdb4pnFAIAAOwTNjJ99MFzqz+U5Ih8fzopAAAAe7GN3H305+eWL0nyuSRHLqQaAAAAlmoj1xQevYxCAAAAWL5dhsKq+v113tfd/YcLqAcAAIAlWm+k8JtrtF0zyTFJ9k8iFAIAAOzldhkKu/sF25er6tpJnpDk6CSvTfKCXb0PAACAvce61xRW1fWTPDHJI5KckOT23X3hMgoDAABg8da7pvCPkzw4yfFJfqK7/21pVQEAALAU6z28/klJbpjkvyc5p6oumr6+UVUXLac8AAAAFmm9awrXC4wAAADsAwQ/AACAgQmFAAAAAxMKAQAABiYUAgAADEwoBAAAGJhQCAAAMDChEAAAYGBCIQAAwMCEQgAAgIEJhQAAAAMTCgEAAAYmFAIAAAxMKAQAABiYUAgAADAwoRAAAGBgQiEAAMDAhEIAAICBCYUAAAADEwoBAAAGJhQCAAAMTCgEAAAYmFAIAAAwMKEQAABgYEIhAADAwIRCAACAgQmFAAAAAxMKAQAABiYUAgAADEwoBAAAGNjKQmFVXamqPlRVfz+t36SqPlBVn6mqv6mqq0ztV53Wz5q2H7aqmgEAAPY1qxwpfEKSM+fWn5vkRd19eJILkxwztR+T5MLu/pEkL5r6AQAAsAesJBRW1SFJ7p/kZdN6JblHkpOnLickedC0fOS0nmn7Paf+AAAAXEGrGin8kyRPTnLptL5/kq919yXT+rYkB0/LByf5YpJM278+9b+Mqjq2qk6rqtMuuOCCRdYOAACwz1h6KKyqByQ5v7tPn29eo2tvYNv3G7qP7+4juvuIrVu37oFKAQAA9n1bVrDPuyR5YFXdL8nVklwns5HD/apqyzQaeEiSc6b+25IcmmRbVW1Jct0kX11+2QAAAPuepY8UdvdTu/uQ7j4sycOSvKu7H5Hk3UkeMnU7Ksmbp+VTpvVM29/V3TuNFAIAAPCD20zPKXxKkidW1VmZXTP48qn95Un2n9qfmOS4FdUHAACwz1nF9NH/0N3vSfKeafnsJHdYo8+3kjx0qYUBAAAMYjONFAIAALBkQiEAAMDAhEIAAICBCYUAAAADEwoBAAAGJhQCAAAMTCgEAAAYmFAIAAAwMKEQAABgYEIhAADAwIRCAACAgQmFAAAAAxMKAQAABiYUAgAADEwoBAAAGJhQCAAAMDChEAAAYGBCIQAAwMCEQgAAgIEJhQAAAAMTCgEAAAYmFAIAAAxMKAQAABiYUAgAADAwoRAAAGBgQiEAAMDAhEIAAICBCYUAAAADEwoBAAAGJhQCAAAMTCgEAAAYmFAIAAAwMKEQAABgYEIhAADAwIRCAACAgQmFAAAAAxMKAQAABiYUAgAADEwoBAAAGJhQCAAAMDChEAAAYGBCIQAAwMCEQgAAgIEJhQAAAAMTCgEAAAYmFAIAAAxMKAQAABiYUAgAADAwoRAAAGBgQiEAAMDAhEIAAICBCYUAAAADEwoBAAAGJhQCAAAMTCgEAAAYmFAIAAAwMKEQAABgYEIhAADAwIRCAACAgQmFAAAAAxMKAQAABiYUAgAADEwoBAAAGJg9dne5AAAMxElEQVRQCAAAMDChEAAAYGBCIQAAwMCEQgAAgIEJhQAAAAMTCgEAAAYmFAIAAAxMKAQAABiYUAgAADAwoRAAAGBgQiEAAMDAhEIAAICBCYUAAAADEwoBAAAGJhQCAAAMTCgEAAAYmFAIAAAwMKEQAABgYEIhAADAwIRCAACAgQmFAAAAAxMKAQAABiYUAgAADEwoBAAAGNjSQ2FVHVpV766qM6vqE1X1hKn9+lV1alV9Znq93tReVfWSqjqrqj5aVbdfds0AAAD7qlWMFF6S5EndfYskd0ry2Kq6ZZLjkryzuw9P8s5pPUnum+Tw6evYJC9dfskAAAD7pqWHwu4+t7vPmJa/keTMJAcnOTLJCVO3E5I8aFo+Msmre+b9SfarqoOWXDYAAMA+aaXXFFbVYUlul+QDSQ7s7nOTWXBMcsDU7eAkX5x727apbcfPOraqTquq0y644IJFlg0AALDPWFkorKprJXl9kt/q7ovW67pGW+/U0H18dx/R3Uds3bp1T5UJAACwT1tJKKyqK2cWCE/s7jdMzedtnxY6vZ4/tW9Lcujc2w9Jcs6yagUAANiXreLuo5Xk5UnO7O4Xzm06JclR0/JRSd481/6o6S6kd0ry9e3TTAEAALhitqxgn3dJ8sgkH6uqD09tT0vynCSvq6pjknwhyUOnbW9Ncr8kZyW5OMnRyy0XAABg37X0UNjd/5S1rxNMknuu0b+TPHahRQEAAAxqpXcfBQAAYLWEQgAAgIEJhQAAAAMTCgEAAAYmFAIAAAxMKAQAABiYUAgAADAwoRAAAGBgQiEAAMDAhEIAAICBCYUAAAADEwoBAAAGJhQCAAAMTCgEAAAYmFAIAAAwMKEQAABgYEIhAADAwIRCAACAgQmFAAAAAxMKAQAABiYUAgAADEwoBAAAGJhQCAAAMDChEAAAYGBCIQAAwMCEQgAAgIEJhQAAAAMTCgEAAAYmFAIAAAxMKAQAABiYUAgAADAwoRAAAGBgQiEAAMDAhEIAAICBCYUAAAADEwoBAAAGJhQCAAAMTCgEAAAYmFAIAAAwMKEQAABgYEIhAADAwIRCAACAgQmFAAAAAxMKAQAABiYUAgAADEwoBAAAGJhQCAAAMDChEAAAYGBCIQAAwMCEQgAAgIEJhQAAAAMTCgEAAAYmFAIAAAxMKAQAABiYUAgAADAwoRAAAGBgQiEAAMDAhEIAAICBCYUAAAADEwoBAAAGJhQCAAAMTCgEAAAYmFAIAAAwMKEQAABgYEIhAADAwIRCAACAgQmFAAAAAxMKAQAABiYUAgAADEwoBAAAGJhQCAAAMDChEAAAYGBCIQAAwMCEQgAAgIEJhQAAAAMTCgEAAAYmFAIAAAxMKAQAABiYUAgAADAwoRAAAGBgQiEAAMDAhEIAAICBCYUAAAADEwoBAAAGJhQCAAAMTCgEAAAYmFAIAAAwsL0mFFbVfarq01V1VlUdt+p6AAAA9gV7RSisqisl+bMk901yyyQPr6pbrrYqAACAvd9eEQqT3CHJWd19dnd/J8lrkxy54poAAAD2etXdq65ht6rqIUnu092/Oq0/Mskdu/txc32OTXLstPpjST699ELXdoMkX1l1EZuQ87I252VnzsnanJe1OS9rc1525pyszXlZm/OyNudlZ5vpnNy4u7dupOOWRVeyh9QabZdJs919fJLjl1POxlXVad19xKrr2Gycl7U5LztzTtbmvKzNeVmb87Iz52RtzsvanJe1OS8721vPyd4yfXRbkkPn1g9Jcs6KagEAANhn7C2h8F+SHF5VN6mqqyR5WJJTVlwTAADAXm+vmD7a3ZdU1eOS/EOSKyV5RXd/YsVlbdSmm9K6STgva3NeduacrM15WZvzsjbnZWfOydqcl7U5L2tzXna2V56TveJGMwAAACzG3jJ9FAAAgAUQCgEAAAYmFC5IVb2iqs6vqo+vupbNpKoOrap3V9WZVfWJqnrCqmtataq6WlV9sKo+Mp2TZ666ps2kqq5UVR+qqr9fdS2bRVV9rqo+VlUfrqrTVl3PZlFV+1XVyVX1qen/MXdedU2rVFU/Nv2MbP+6qKp+a9V1bQZV9dvT/28/XlUnVdXVVl3TZlBVT5jOySdG/llZ63e4qrp+VZ1aVZ+ZXq+3yhqXbRfn5KHTz8qlVbXXPYJhT9jFefnj6d+hj1bVG6tqv1XWuFFC4eK8Ksl9Vl3EJnRJkid19y2S3CnJY6vqliuuadW+neQe3X2bJLdNcp+qutOKa9pMnpDkzFUXsQn9THffdm98FtICvTjJ27v75kluk8F/brr709PPyG2T/GSSi5O8ccVlrVxVHZzk8UmO6O5bZXYDu4ettqrVq6pbJfm1JHfI7L+fB1TV4autamVelZ1/hzsuyTu7+/Ak75zWR/Kq7HxOPp7kwUnet/RqNo9XZefzcmqSW3X3rZP8nyRPXXZRl4dQuCDd/b4kX111HZtNd5/b3WdMy9/I7Je2g1db1Wr1zL9Nq1eevtwBKklVHZLk/kletupa2Nyq6jpJ7prk5UnS3d/p7q+ttqpN5Z5JPtvdn191IZvEliRXr6otSa4Rzz5OklskeX93X9zdlyR5b5JfWHFNK7GL3+GOTHLCtHxCkgcttagVW+ucdPeZ3f3pFZW0KezivLxj+m8oSd6f2fPVNz2hkJWpqsOS3C7JB1ZbyepNUyQ/nOT8JKd29/DnZPInSZ6c5NJVF7LJdJJ3VNXpVXXsqovZJG6a5IIkr5ymG7+sqq656qI2kYclOWnVRWwG3f2lJM9P8oUk5yb5ene/Y7VVbQofT3LXqtq/qq6R5H5JDl1xTZvJgd19bjL7A3eSA1ZcD3uH/5LkbasuYiOEQlaiqq6V5PVJfqu7L1p1PavW3d+bpngdkuQO0zSeoVXVA5Kc392nr7qWTegu3X37JPfNbAr2XVdd0CawJcntk7y0u2+X5JsZb3rXmqrqKkkemORvV13LZjBdC3ZkkpskuWGSa1bVr6y2qtXr7jOTPDezqW9vT/KRzC75AC6HqvrdzP4bOnHVtWyEUMjSVdWVMwuEJ3b3G1Zdz2YyTXd7T1yPmiR3SfLAqvpcktcmuUdV/fVqS9ocuvuc6fX8zK4Ru8NqK9oUtiXZNjfKfnJmIZHZHw/O6O7zVl3IJvGzSf5vd1/Q3d9N8oYkP73imjaF7n55d9++u++a2ZS4z6y6pk3kvKo6KEmm1/NXXA+bWFUdleQBSR7Re8lD4YVClqqqKrNrfs7s7heuup7NoKq2br8zVVVdPbNfWD612qpWr7uf2t2HdPdhmU19e1d3D//X/Kq6ZlVde/tykntlNu1raN395SRfrKofm5rumeSTKyxpM3l4TB2d94Ukd6qqa0z/Jt0zg9+UaLuqOmB6vVFmNxDxc/N9pyQ5alo+KsmbV1gLm1hV3SfJU5I8sLsvXnU9G7Vl1QXsq6rqpCR3T3KDqtqW5Ond/fLVVrUp3CXJI5N8bLqGLkme1t1vXWFNq3ZQkhOq6kqZ/aHmdd3t8QvsyoFJ3jj7XTZbkrymu9++2pI2jd9McuI0XfLsJEevuJ6Vm64N+7kkj1l1LZtFd3+gqk5OckZmU7s+lOT41Va1aby+qvZP8t0kj+3uC1dd0Cqs9TtckuckeV1VHZPZHxYeuroKl28X5+SrSf7/JFuTvKWqPtzd915dlcu3i/Py1CRXTXLq9G/1+7v711dW5AbVXjKiCQAAwAKYPgoAADAwoRAAAGBgQiEAAMDAhEIAAICBCYUAAAADEwoBYBeqqqvqr+bWt1TVBVV1uR4bU1X7VdVvzK3f/fJ+FgDsKUIhAOzaN5PcqqquPq3/XJIvXYHP2y/Jb+y2FwAskVAIAOt7W5L7T8sPT3LS9g1Vdf2qelNVfbSq3l9Vt57an1FVr6iq91TV2VX1+Oktz0lys6r6cFX98dR2rao6uao+VVUn1vS0YwBYFqEQANb32iQPq6qrJbl1kg/MbXtmkg91962TPC3Jq+e23TzJvZPcIcnTq+rKSY5L8tnuvm13/87U73ZJfivJLZPcNMldFnkwALAjoRAA1tHdH01yWGajhG/dYfN/SvJXU793Jdm/qq47bXtLd3+7u7+S5PwkB+5iFx/s7m3dfWmSD0/7AoCl2bLqAgBgL3BKkucnuXuS/efa15rq2dPrt+favpdd/5u70X4AsBBGCgFg916R5A+6+2M7tL8vySOS2Z1Ek3yluy9a53O+keTaC6kQAC4nf40EgN3o7m1JXrzGpmckeWVVfTTJxUmO2s3n/GtV/e+q+nhmN7B5y56uFQB+UNXdu+8FAADAPsn0UQAAgIEJhQAAAAMTCgEAAAYmFAIAAAxMKAQAABiYUAgAADAwoRAAAGBg/w8NrueMpHEF5AAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 1080x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(15,10))\n",
    "sns.barplot(x=per_day_crime.index, y = per_day_crime,palette='deep')\n",
    "plt.title(\"Average Arrests Per day in a month\")\n",
    "plt.xlabel(\"Month\")\n",
    "plt.ylabel(\"Number of Arrests\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "arrests = df.Year.value_counts().sort_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2006    371905\n",
       "2007    403156\n",
       "2008    401407\n",
       "2009    420452\n",
       "2010    419832\n",
       "2011    410964\n",
       "2012    395487\n",
       "2013    393756\n",
       "2014    387687\n",
       "2015    339435\n",
       "2016    314819\n",
       "2017    286216\n",
       "2018    246743\n",
       "Name: Year, dtype: int64"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arrests"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(13,)"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arrests.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2427716 2117400\n"
     ]
    }
   ],
   "source": [
    "sum1 = sum([arrests[x] for x in range(2006,2012)])\n",
    "sum2 = sum([arrests[x] for x in range(2012,2018)])\n",
    "\n",
    "print(sum1,sum2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Arrests')"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 360x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(5,6))\n",
    "sns.barplot(x=[0,1],y = [sum1,sum2])\n",
    "plt.title(\"Arrests by groups\")\n",
    "plt.xlabel(\"0 = 2006-11, 1 = 2011-17\")\n",
    "plt.ylabel(\"Arrests\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "7"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grouped_sums = []\n",
    "for i in range(2006,2019,2):\n",
    "    grouped_sums.append(arrests.get(i)+arrests.get(i,0))\n",
    "    \n",
    "len(grouped_sums)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Arrests')"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,10))\n",
    "sns.barplot(x=[0,1,2,3,4,5],y = grouped_sums[:-1])\n",
    "plt.title(\"Arrests by 2 year groups\")\n",
    "plt.xlabel(\"0 = 2006-07, 1 = 2008-09, 2 = 2010-11, 3 = 2012-13, 4 = 2012-13, 5 = 2014-15, 6 = 2016-17 \")\n",
    "plt.ylabel(\"Arrests\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_brooklyn = df[df.city=='Brooklyn']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1333369, 23)"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_brooklyn.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "arrests_brooklyn = df_brooklyn.Year.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "grouped_sums_brooklyn = []\n",
    "for i in range(2006,2019,2):\n",
    "    grouped_sums_brooklyn.append(arrests_brooklyn.get(i)+arrests_brooklyn.get(i,0))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Arrests')"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,10))\n",
    "sns.barplot(x=[0,1,2,3,4,5],y = grouped_sums_brooklyn[:-1])\n",
    "plt.title(\"Arrests in Brooklyn by 2 year groups\")\n",
    "plt.xlabel(\"0 = 2006-07, 1 = 2008-09, 2 = 2010-11, 3 = 2012-13, 4 = 2012-13, 5 = 2014-15, 6 = 2016-17 \")\n",
    "plt.ylabel(\"Arrests\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['ARREST_KEY', 'ARREST_DATE', 'PD_CD', 'PD_DESC', 'KY_CD', 'OFNS_DESC',\n",
       "       'LAW_CODE', 'LAW_CAT_CD', 'ARREST_BORO', 'ARREST_PRECINCT',\n",
       "       'JURISDICTION_CODE', 'AGE_GROUP', 'PERP_SEX', 'PERP_RACE', 'X_COORD_CD',\n",
       "       'Y_COORD_CD', 'Latitude', 'Longitude', 'zipcode', 'city', 'Year',\n",
       "       'Month', 'Day'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "arrest_by_race = df.PERP_RACE.value_counts().sort_values()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "OTHER                                1363\n",
       "AMERICAN INDIAN/ALASKAN NATIVE      10434\n",
       "UNKNOWN                             48822\n",
       "ASIAN / PACIFIC ISLANDER           192823\n",
       "BLACK HISPANIC                     383715\n",
       "WHITE                              578492\n",
       "WHITE HISPANIC                    1247861\n",
       "BLACK                             2328349\n",
       "Name: PERP_RACE, dtype: int64"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arrest_by_race "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [],
   "source": [
    "arrest_by_race = arrest_by_race[~arrest_by_race.index.isin([\"OTHER\",\"UNKNOWN\"])]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "AMERICAN INDIAN/ALASKAN NATIVE      10434\n",
       "ASIAN / PACIFIC ISLANDER           192823\n",
       "BLACK HISPANIC                     383715\n",
       "WHITE                              578492\n",
       "WHITE HISPANIC                    1247861\n",
       "BLACK                             2328349\n",
       "Name: PERP_RACE, dtype: int64"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arrest_by_race"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f79df20c7d0>"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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oumVr+mu0M4PQVFWXAz9P8pC+6eVJrpp6Mf5rtT3w4XQ/rulC4GbglGHH/loeTncNnzuH8afar+3HvdV1n8Hb6MPoHM9xLj5F9+T8+wb3sk4Z3pP5mXkcU5Ik9XLbf1+1kCR5K/DWqnI2bkI222jz2v3ue7H8B1+ZdCmSJK1xSc6vqqWz9Ru7VKm1X1U9c9I1SJIkjfLXSkqSJKk5Q6YkSZKaM2RKkiSpOUOmJEmSmjNkSpIkqTlDpiRJkpozZEqSJKk5Q6YkSZKaM2RKkiSpOUOmJEmSmjNkSpIkqTlDpiRJkpozZEqSJKk5Q6YkSZKaM2RKkiSpOUOmJEmSmjNkSpIkqTlDpiRJkpozZEqSJKk5Q6YkSZKaM2RKkiSpOUOmJEmSmjNkSpIkqTlDpnQ77b7X/Vn+g69MugxJktYqhkxJkiQ1Z8iUJElSc4ZMSZIkNWfIlCRJUnOGTEmSJDVnyJQkSVJzhkxJkiQ1Z8iUJElSc4ZMSZIkNWfIlCRJUnOGTEmSJDVnyJQkSVJzhkxJkiQ1Z8iUJElSc4smXYC00H3/ou/y7AccOekyAPjXi8+YdAmSJAHOZEqSJGkNMGRKkiSpOUOmJEmSmjNkSpIkqTlDpiRJkpozZEqSJKk5Q6YkSZKaM2RKkiSpOUOmJEmSmjNkSpIkqTlDpiRJkpozZEqSJKk5Q6YkSZKaM2RKkiSpOUOmJEmSmjNkSpIkqTlDpiRJkpozZEqSJKk5Q6YkSZKaM2RKkiSpOUOmJEmSmjNkSpIkqTlDpiRJkpozZEqSJKk5Q+YcJDkiSSW5/6DtTklOTrIyyTeTfD3JvfptVyTZdpb9F/dtzxu0nZLkaTPUsEOST03TvirJir6O9ye587hx+/b7JflYksuSXJLk9CR3S/KIJB/p+zwtyTX9sVckeWff/vYkR/bvN0xyYpLv9OOfl+SQaWo8O8nS/v3T++t1Yb/P4aPHnWb/v0zy6yR3HbQ9oj+3wwZtH0nyiMGYl/bjfKu/tltOc92mXi8Z2e+C/mu6ZLqaJEnSeIbMuVkGfAk4etB2FLAjsFdV7QkcAfxsHvsD/Bj4iyQbzaGGg4FPTtN+Y1Utqao9gN8Azxk3bpJNgI8C/1xVu1bV7sA/A9tNc+z39cdeUlVPmWb7q4AdgD368Q8DtpjpBJLcA3gZsH9V7QXsC1w44xnf+jy+TneNh67qjzeTY/px9gJuAs4abLtxcG5LqurEkf0eBLwZeN0c6pMkSSMMmbNIsjnw+8AzuHVI3AH4UVXdAlBVV1XVT+exP8A1wGeBp86hlIOBj8/S5xxg11nG/RPg3Kr68FRDVX2+qlbOoYb/1c+YPgt4XlXd1B/n6qo6fcxu2wM3AL/o+/+iqi6fZZz7AJsDL6cLm0MXANcnefS4Y1TVb4AXAzsnedC4viPOBXaaR39JktQzZM7uccAnqurbwHVJ9u7bTwcO65daT0ry4HnuP+VE4K+TbDBTAf223arq4jF9FgGHAN+cZdw9gPNnPNtbO2qwnPynI9t2BX5QVT+f47GgC4VXA5cn+ffhUvcYy4D30AXo3ZJsP7L91XQBdKyqWtWPP3XrwKYjy+VHTbPbwcAH51CjJEkasWjSBSwAy4B/6t+/t//8jaq6KsluwCP712eTPKGqPjuX/ac2VtXlSc6jm2GcyUOAr82wbdMkK/r35wBvm8u4c/S+qjpunvvMqKpWJTkY+D3gQOANSfapquPH7HY0cERV3ZLkP4EnAG8aHPOcJCR52BxKyOD9jVU10/2WpyXZDNgAGP2fgu5AybHAsQCbL9p0DkNLkrR+MWSOkWQbugC5R5KiCx2V5MXVuYluCfvjSa6mmz387Fz2HxnqH4AzgC/OUMohwCdm2HabsDTLuBcBfzC3KzDWZXTLz1tU1Q1z3amqCjgPOC/Jp4F/B46frm+SvYD7Ap9OArAR8D0GIbP3Grp7M2+eadx+NnhP4JI5lHkM3aznif1Yj5/mPE4FTgXYbtOtag7HlCRpveJy+XhHAu+sql2qanFV3RO4HNg/yd5JdoTuSXO6h0u+P9f9h52q6lvAxcChM9RxIIPwenvqBt4N7JfkD6c6Jzk4yZ7zOD5V9Su6WdOTpx5c6p+Af9JM+yTZceR2gSXc9poNLQOO789hcVXtCOyUZJeRWj4FbAVMe79lkg2BE4Arq2ouDxpRVb+lW4bfN8nuc9lHkiT9jiFzvGXAB0bazqRb2t4e+HCSlXRPSN8MnDKP/Ue9BrjHaGOS7YBfz/PexxnHraob6cLs8/ofPXQx8DS6J93n6+V0Dy9d3F+HD/afZ7Ih8Pr+RwqtoHtC/y8G2/81yVX961y6pfLR8/gAt32ACqa/fqcluRBYCWwGHD7YNnpP5okj+9Jfq5OAF445J0mSNI10q5daW/Uzg/cY+RE7Wotst+lW9fh7HTjpMgD414vPmHQJkqR1XJLzq2rpbP28J3MtV1X/MekaJEmS5svlckmSJDVnyJQkSVJzhkxJkiQ1Z8iUJElSc4ZMSZIkNWfIlCRJUnOGTEmSJDVnyJQkSVJzhkxJkiQ1Z8iUJElSc4ZMSZIkNWfIlCRJUnOGTEmSJDVnyJQkSVJzhkxJkiQ1Z8iUJElSc4ZMSZIkNWfIlCRJUnOGTEmSJDVnyJQkSVJzhkxJkiQ1Z8iUJElSc4ZMSZIkNbdo0gVIC90uD7wP/7r8jEmXIUnSWsWZTEmSJDVnyJQkSVJzhkxJkiQ1Z8iUJElSc4ZMSZIkNWfIlCRJUnOGTEmSJDVnyJQkSVJzhkxJkiQ1Z8iUJElSc4ZMSZIkNWfIlCRJUnOGTEmSJDW3aNIFSAvdtRddzlse+ORJl7HWedZF75p0CZKkCXImU5IkSc0ZMiVJktScIVOSJEnNGTIlSZLUnCFTkiRJzRkyJUmS1JwhU5IkSc0ZMiVJktScIVOSJEnNGTIlSZLUnCFTkiRJzRkyJUmS1JwhU5IkSc0ZMiVJktScIVOSJEnNGTIlSZLUnCFTkiRJzRkyJUmS1JwhU5IkSc0ZMiVJktScIVOSJEnNGTIlSZLUnCFTkiRJzRkyJUmS1Jwhcx2XZFWSFUkuSPKNJPv17YuTrJxhn0VJrk1ywkj7hklOTPKdJCuTnJfkkH7bFUm27d/vk+TyJA8e2f8RST4y0vb2JEf2789OsrR///Qk30xyYT/W4YP+l/fn9I0kD51D3WcnWT74vDTJ2dPVlOSQJMuTXJLkW0leP8dLLUmSBgyZ674bq2pJVT0IeClwwmw7AAcBlwJPTJJB+6uAHYA9qmoP4DBgi+GOSfYCzgCOqqr/Wp2Ck9wDeBmwf1XtBewLXDjo8qKqWgK8BPjXOdQNsP1UIB4z7h7AKcCTqmp3YA/ge6tzDpIkre8MmeuXuwA/nUO/ZcAbgR/QBTyS3Bl4FvC8qroJoKqurqrTB/vtDnwQeHJVnXc76tweuAH4RT/OL6rq8mn6fRHYdVzdA68DXj7LuC8GXlNV3+rHvbmq3jz/8iVJ0qJJF6A1btMkK4BN6GYhHzmuc5JNgQOBZwNb0gW3c+nC3A+q6udjdj+LbhbwS2P6PKyvZ8rOwEdG+lwAXA1cnuSzwH9W1YenOdZhwDdnqXvKucARSQ6gC7DT2QM4aUztkiRpjpzJXPdNLZffHzgYeOc0S8lDhwKfr6pfAWfSBbMN5jjWZ4BnztL/nL6eJf2S94dGO1TVqr7WI4FvA29Icvygy+v6oHos8Ix51P1qZp/NnJMkx/b3bi6/YdVNLQ4pSdI6xZC5Hqmqc4Ftge3GdFsGPCrJFcD5wDbAAcBlwM5Jthiz73H9n7d7ibk651XVCcDRwB8PNr+oD6mPrqqph5dmqnt4zM/RzeiOLqVPuQjYZ471nVpVS6tq6RYbbDzn85IkaX1hyFyPJLk/sAHwkxm23wXYH9i5qhZX1WLgucCyfobwbcDJSTbq+++Q5EmDQ9xCF/Z2S/LK21Hnjkn2HjQtAb4/pv+MdU/T/TV0915O53XA3ya5X3/cOyX5q9U4BUmS1nvek7nu23RwD2SAp1bVqn7FfLckVw36vhH43NSDPb2zgNcm2ZhuqfnVwMVJfg38EnjFcLCquqn/cUNfSHJ1Vb1pNWreEHh9kh2BXwPXAM8Z0//xs9Q9rO9jSa6Z7iBVdWGSFwDv6R90KuCjq1G/JEnrvVTVpGuQFrTFm25TL7v3YyddxlrnWRe9a9IlSJLWgCTnV9XS2fq5XC5JkqTmDJmSJElqzpApSZKk5gyZkiRJas6QKUmSpOYMmZIkSWrOkClJkqTmDJmSJElqzpApSZKk5gyZkiRJas6QKUmSpOYMmZIkSWrOkClJkqTmDJmSJElqzpApSZKk5gyZkiRJas6QKUmSpOYMmZIkSWrOkClJkqTmDJmSJElqzpApSZKk5uYVMpNstqYKkSRJ0rpjTiEzyX5JLgYu6T8/KMmb12hlkiRJWrDmOpP5BuAxwE8AquoC4OFrqihJkiQtbIvm2rGqrkwybFrVvhxp4dn2gffiWcvfNekyJElaq8w1ZF6ZZD+gkmwEPJ9+6VySJEkaNdfl8ucAzwV2Aq4ClvSfJUmSpNuY00xmVV0LHLOGa5EkSdI6Yq5Pl78jyZaDz1sl+bc1V5YkSZIWsrkul+9VVT+b+lBVPwUevGZKkiRJ0kI315B5pyRbTX1IsjXzeDJdkiRJ65e5BsWTgK8kOaP//ATgNWumJEmSJC10c33w551JzgcOAAI8vqouXqOVSZIkacGazw9jvyjJNcAmAEl2rqofrLHKJEmStGDN9enyP0ryHeBy4AvAFcDH12BdkiRJWsDm+uDPq4B9gW9X1b2AA4Evr7GqJEmStKDNNWT+tqp+QveU+Z2q6vN0v/VHkiRJuo253pP5sySbA18ETkvyY+DmNVeWJEmSFrK5zmQeDvwK+EvgE8B3gcPWVFGSJEla2Ob6I4x+2b+9BXhHkg2Ao4HT1lRh0kLxq+98j+WHPGHSZWiMpR9//6RLkKT1ztiZzCR3SfLSJKckOSid44DvAU+8Y0qUJEnSQjPbTOa7gJ8C5wLPBF4EbAQcXlUr1nBtkiRJWqBmC5n3rqo9AZK8FbgW2LmqbljjlUmSJGnBmu3Bn99OvamqVcDlBkxJkiTNZraZzAcl+Xn/PsCm/ecAVVV3WaPVSZIkaUEaGzKraoM7qhBJkiStO+b6czIlSZKkOTNkSpIkqTlDpiRJkpozZEqSJKk5Q6YkSZKaM2RKkiSpOUOmJEmSmjNkSpIkqTlDpiRJkpozZEqSJKk5Q6YkSZKaM2RKkiSpOUOmJEmSmjNkSpIkqTlDpiRJkpozZEqSJKk5Q6YkSZKaM2RKkiSpuUWTLkCaiyRvAL5fVf/Uf/4kcGVVPbP/fBLw38DTq2qPwX7HA7+oqtcneTvwEeAY4F7A5sB2wOV99z8H/gHYAbixb7usqo5cs2cnSdK6x5lMLRRfAfYDSHInYFvggYPt+wFfnsuBquqIqloCPBM4p6qW9K+v9F2OGbQZMCVJWg2GTC0UX6YPmXThciVwQ5KtkmwM7A78dFLFSZKkW3O5XAtCVf0wyc1JdqYLm+cCOwEPBa4HLgR+A9wnyYrBrncHXj/P4U5LMrVc/umqetHtq16SpPWPIVMLydRs5n7AP9KFzP3oQubUUvd3+6Vw4H/vyZyvY6pq+bgOSY4FjgW4+yZ3Xo0hJElat7lcroVk6r7MPemWy79KN5M55/sxW6mqU6tqaVUt3Wqjje/IoSVJWhAMmVpIvgwcClxXVauq6jpgS7qgee5EK5MkSbficrkWkm/SPVX+7pG2zavq2iSbNxpneE/mtVX1qEbHlSRpvZGqmnQN0oL2gLtuXe/c78BJl6Exln78/ZMuQZLWGUnOr6qls/VzuVySJEnNGTIlSZLUnCFTkiRJzRkyJUmS1JwhU5IkSc0ZMiVJktScIVOSJEnNGTIlSZLUnCFTkiRJzRkyJUmS1JwhU5IkSc0ZMiVJktScIVOSJEnNGTIlSZLUnCFTkiRJzRkyJUmS1JwhU5IkSc0ZMiVJktScIVOSJEnNGTIlSZLUnCFTkiRJzRkyJUmS1JwhU5IkSc0ZMiVJktTcokkXIC10d77vvVn68fdPugxJktYqzmRKkiSpOUOa4EdIAAAQNUlEQVSmJEmSmjNkSpIkqTlDpiRJkpozZEqSJKk5Q6YkSZKaM2RKkiSpOUOmJEmSmjNkSpIkqTlDpiRJkpozZEqSJKk5Q6YkSZKaM2RKkiSpuUWTLkBa6H7zo+9zxSuPnXQZkoDFrzh10iVI6jmTKUmSpOYMmZIkSWrOkClJkqTmDJmSJElqzpApSZKk5gyZkiRJas6QKUmSpOYMmZIkSWrOkClJkqTmDJmSJElqzpApSZKk5gyZkiRJas6QKUmSpOYMmZIkSWrOkClJkqTmDJmSJElqzpApSZKk5gyZkiRJas6QKUmSpOYMmZIkSWrOkClJkqTmDJmSJElqzpApSZKk5gyZkiRJas6QuQ5L8oYkLxh8/mSStw4+n5Tkr5IsTrJyZN/jk7ywf//2JEcm+UCSFUkuS3J9/35Fkv2SnJ3k0kHbGdPU87Qkp4y0nZ1kaf/+iiTb9u9fluSiJBf2x3vIoP+lSS5I8uUkuw2OtV2S3yZ59sgYVyQ5c/D5yCRvn66mJE9JsrIf++KpayBJkubHkLlu+wqwH0CSOwHbAg8cbN8P+PJcD1ZVR1TVEuCZwDlVtaR/faXvcsyg7cjVLTrJQ4FDgb2rai/gUcCVgy7HVNWDgHcArxu0PwH4KrBsmsMuTfLAadqH4x4CvAA4qKoeCOwNXL+65yFJ0vrMkLlu+zJ9yKQLlyuBG5JslWRjYHfgvyZV3Bg7ANdW1U0AVXVtVf1wmn5fBHYdfF4G/DVwjyQ7jfR9PfC3s4z7UuCFU2NV1a+r6i2rcwKSJK3vDJnrsD4s3ZxkZ7qweS7wNeChwFLgwqr6Td/9PoOl7hXAc1ZjyNMGx3jdDH2OGhln6TR9PgXcM8m3k7w5yR/McKzDgG8CJLkncPeqOg84HThqpO/pwN5JdmVmewDnj9kuSZLmaNGkC9AaNzWbuR/wj8BO/fvr6ZbTp3y3XwoHunsyV2OsY6pq+Sx93ldVxw3GOXu0Q1X9Isk+wMOAA4D3JXlJVb2973JakhuBK4Dn9W1H0wVJgPcCb6M73ymr6JbWXwp8fB7nNK0kxwLHAux4181v7+EkSVrnOJO57pu6L3NPuuXyr9LNZM7rfsw7WlWtqqqzq+rvgOOAPx5snrr383FVNXWv5jLgaUmuAD4EPCjJfUcO+y7g4cDOMwx7EbDPHOs7taqWVtXSbTbbZI5nJUnS+sOQue77Mt1DNNf1we06YEu6oHnuRCubQZLdRgLiEuD74/oDm1XVTlW1uKoWAyfQzW7+r6r6LfAGuod7pnMC8Nokd++Pu3GS56/+mUiStP4yZK77vkn3VPlXR9qur6prG481vCfzM7fjOJsD7+h/hNCFwAOA48f0XwZ8YKTtTKZ/yvxtzHCbSFV9DHgT8JkkF9Hdn+ktJZIkrYZU1aRrkBa0vXbarj707CMmXYYkYPErTp10CdI6L8n5VTXdg7u34kymJEmSmjNkSpIkqTlDpiRJkpozZEqSJKk5Q6YkSZKaM2RKkiSpOUOmJEmSmjNkSpIkqTlDpiRJkpozZEqSJKk5Q6YkSZKaM2RKkiSpOUOmJEmSmjNkSpIkqTlDpiRJkpozZEqSJKk5Q6YkSZKaM2RKkiSpOUOmJEmSmjNkSpIkqTlDpiRJkpozZEqSJKk5Q6YkSZKaM2RKkiSpuUWTLkBa6DbaYRcWv+LUSZchSdJaxZlMSZIkNWfIlCRJUnOGTEmSJDVnyJQkSVJzhkxJkiQ1Z8iUJElSc4ZMSZIkNWfIlCRJUnOGTEmSJDVnyJQkSVJzhkxJkiQ1Z8iUJElSc4ZMSZIkNWfIlCRJUnOLJl2AtNDd/PNr+fEn3zbpMiRJuo3tH/OMiY3tTKYkSZKaM2RKkiSpOUOmJEmSmjNkSpIkqTlDpiRJkpozZEqSJKk5Q6YkSZKaM2RKkiSpOUOmJEmSmjNkSpIkqTlDpiRJkpozZEqSJKk5Q6YkSZKaM2RKkiSpOUOmJEmSmjNkSpIkqTlDpiRJkpozZEqSJKk5Q6YkSZKaM2RKkiSpOUOmJEmSmjNkSpIkqTlDpiRJkpozZEqSJKk5Q6YWhCSrkqxIckGSbyTZr29fnGTlDPssSnJtkhNG2jdMcmKS7yRZmeS8JIf0265Ism3/fp8klyd58Jo+P0mS1jWGTC0UN1bVkqp6EPBS4ITZdgAOAi4Fnpgkg/ZXATsAe1TVHsBhwBbDHZPsBZwBHFVV/9XiBCRJWp8YMrUQ3QX46Rz6LQPeCPwA2BcgyZ2BZwHPq6qbAKrq6qo6fbDf7sAHgSdX1XktC5ckaX2xaNIFSHO0aZIVwCZ0s5CPHNc5yabAgcCzgS3pAue5wK7AD6rq52N2Pwt4UlV9qUXhkiStj5zJ1EIxtVx+f+Bg4J0jS+CjDgU+X1W/As4EjkiywRzH+gzwzHH9kxybZHmS5T+5/oa5noMkSesNQ6YWnKo6F9gW2G5Mt2XAo5JcAZwPbAMcAFwG7JxkizH7Htf/+eYxNZxaVUurauk2dx13KEmS1k+GTC04Se4PbAD8ZIbtdwH2B3auqsVVtRh4LrCsn9l8G3Byko36/jskedLgELfQhdTdkrxyzZ2JJEnrLu/J1EIxdU8mQICnVtWqfsV8tyRXDfq+Efjc1IM9vbOA1ybZGHg58Grg4iS/Bn4JvGI4WFXdlORw4AtJrq6qN62Z05Ikad1kyNSCUFXT3h9ZVVcAG85h/+u49fL6i/vXaL/Fg/fXA0vmWaokScLlckmSJK0BhkxJkiQ1Z8iUJElSc4ZMSZIkNWfIlCRJUnOGTEmSJDVnyJQkSVJzhkxJkiQ1Z8iUJElSc4ZMSZIkNWfIlCRJUnOGTEmSJDVnyJQkSVJzhkxJkiQ1Z8iUJElSc4ZMSZIkNWfIlCRJUnOGTEmSJDVnyJQkSVJzhkxJkiQ1Z8iUJElSc4ZMSZIkNWfIlCRJUnOGTEmSJDW3aNIFSAvdortsy/aPecaky5Akaa3iTKYkSZKaM2RKkiSpOUOmJEmSmjNkSpIkqTlDpiRJkpozZEqSJKm5VNWka5AWtCQ3AJdOuo71xLbAtZMuYj3htb5jeb3vOF7r22+Xqtputk7+nEzp9ru0qpZOuoj1QZLlXus7htf6juX1vuN4re84LpdLkiSpOUOmJEmSmjNkSrffqZMuYD3itb7jeK3vWF7vO47X+g7igz+SJElqzplMSZIkNWfIlFZTkoOTXJrksiQvmXQ9a7skVyT5ZpIVSZb3bVsn+XSS7/R/btW3J8nJ/bW9MMneg+M8te//nSRPHbTv0x//sn7fjBtjXZLk35L8OMnKQdvEru24MdYFM1zv45P8d//9vSLJYwfbXtpfi0uTPGbQPu3fIUnuleRr/XV9X5KN+vaN+8+X9dsXzzbGQpfknkk+n+SSJBcl+Yu+3e/vhaCqfPnyNc8XsAHwXeDewEbABcADJl3X2vwCrgC2HWl7LfCS/v1LgP/Xv38s8HEgwL7A1/r2rYHv9X9u1b/fqt92HvDQfp+PA4eMG2NdegEPB/YGVq4N13amMdaV1wzX+3jghdP0fUD/98PGwL36vzc2GPd3CHA6cHT//l+AP+vf/znwL/37o4H3jRtj0tep0bXeAdi7f78F8O3+fP3+XgAvZzKl1fN/gMuq6ntV9RvgvcDhE65pIToceEf//h3A4wbt76zOV4Etk+wAPAb4dFVdV1U/BT4NHNxvu0tVnVvdvwLvHDnWdGOsM6rqi8B1I82TvLYzjbFOmOF6z+Rw4L1VdVNVXQ5cRvf3x7R/h/SzaI8Ezuj3H72uU9f7DODAvv9MYyx4VfWjqvpG//4G4BJgJ/z+XhAMmdLq2Qm4cvD5qr5NMyvgU0nOT3Js33a3qvoRdP+YANv37TNd33HtV03TPm6Mdd0kr+36+t/Hcf3y6b8NbsuY7/XeBvhZVd080n6rY/Xbr+/7rxfXu7894MHA1/D7e0EwZEqrJ9O0+aMaxvv9qtobOAR4bpKHj+k70/Wdb7tu6464tuvj1+OfgfsAS4AfASf17S2v93r7/Z9kc+BM4AVV9fNxXadp8/t7QgyZ0uq5Crjn4PM9gB9OqJYFoap+2P/5Y+ADdMt5V08tM/V//rjvPtP1Hdd+j2naGTPGum6S13a9+++jqq6uqlVVdQvwFn63XD3f630t3fLropH2Wx2r335XumX7dfp6J9mQLmCeVlX/2Tf7/b0AGDKl1fN14L79U6Ab0d2E/6EJ17TWSrJZki2m3gMHASvprtnUU55PBc7q338IeEr/FOe+wPX9ctUngYOSbNUvRx4EfLLfdkOSfft71J4ycqzpxljXTfLazjTGOmvknrwj6L6/obsWR/dPht8LuC/dgybT/h3S3xf4eeDIfv/R6zp1vY8EPtf3n2mMBa//nnsbcElV/eNgk9/fC8Gknzzy5WuhvuieMPw23ZOcL5t0PWvzi+4J2gv610VT14vufrLPAt/p/9y6bw/wpv7afhNYOjjW0+kebLgM+NNB+1K6f9i/C5zC737ZxLRjrEsv4D10S7S/pZtlecYkr+24MdaF1wzX+139uV5IF0J2GPR/WX8tLqV/crlvn/bvkP6/l/P6r8P7gY379k36z5f12+892xgL/QXsT7cUfSGwon891u/vhfHyN/5IkiSpOZfLJUmS1JwhU5IkSc0ZMiVJktScIVOSJEnNGTIlSZLUnCFTkiRJzRkyJUlNJFmVZEWSlUnen+TOI+1Tr5f07WcnuTTJBUm+nmTJ4FhXJPlmv+1TSe4+Ztypvhcm+UKSXUa2H5Gkktx/pP1+ST6W5LIklyQ5PcndkjwiyfUjNT+q7dWS1n2GTElSKzdW1ZKq2gP4DfCckfap14mDfY6pqgcBbwZeN3K8A/pty4G/nWXsA6pqL+Bs4OUj25YBX6L7rToAJNkE+Cjwz1W1a1XtTvf7x7fru5wzUvNn5nD+kgYMmZKkNeEcYNd59D8X2GmGbV+cx7FudZwkmwO/T/dbeY4e9PsT4Nyq+vBUQ1V9vqpWIqkJQ6Ykqakki4BD6H7lHsCmI0vPR02z28HAB2c45KGDY81m9DiPAz5RVd8Grkuyd9++B3D+mOM8bKTm+8xxfEm9RZMuQJK0ztg0yYr+/TnA2/r3N1bVkhn2OS3JZsAGwN4j2z6fZBXd760eXQIf9fkkdwN+PNJ3GfBP/fv39p+/MeuZdMvlh86hn6QZGDIlSa2MC5MzOQa4ADgReBPw+MG2A6rq2jke5wDgl8DbgVcCf5VkG+CRwB5Jii7IVpIXAxcBfzDPWiXNg8vlkqSJqqrf0s0+7ptk99txnBuBFwBPSbI1cCTwzqrapaoWV9U9gcuB/YF3A/sl+cOp/ZMcnGTP23Mukn7HkClJWtNG78k8cbRDHxBPAl54ewaqqh8B7wGeS7c0/oGRLmcCf9KPdyjwvCTfSXIx8DS65Xa47T2ZR96euqT1Uapq0jVIkiRpHeNMpiRJkprzwR9J0oKQ5GvAxiPNT66quf54I0l3IJfLJUmS1JzL5ZIkSWrOkClJkqTmDJmSJElqzpApSZKk5gyZkiRJau7/A9ByShdqLKhuAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 576x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize = (8,8))\n",
    "\n",
    "plt.xlabel(\"Number of Arrests\")\n",
    "plt.title('Number of Arrets by Race')\n",
    "plt.ylabel('Race')\n",
    "sns.barplot(x = arrest_by_race, y = arrest_by_race.index,palette='rocket')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [],
   "source": [
    "arrests_by_sex = df.PERP_SEX.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "M    3990514\n",
       "F     801345\n",
       "Name: PERP_SEX, dtype: int64"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arrests_by_sex"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Arrests')"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 288x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize = (4,8))\n",
    "sns.barplot(y = arrests_by_sex, x = arrests_by_sex.index,palette='deep')\n",
    "plt.ylabel(\"Arrests\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [],
   "source": [
    "arrests_white = df[df.PERP_RACE == 'BLACK']\n",
    "arrests_black = df[df.PERP_RACE == \"WHITE\"]\n",
    "arrests_hispanic = df[df.PERP_SEX == \"HISPANIC\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "25-44    1010260\n",
       "18-24     615195\n",
       "45-64     466471\n",
       "<18       219733\n",
       "65+        16595\n",
       "Name: AGE_GROUP, dtype: int64"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arrests_white.AGE_GROUP.value_counts().sort_values(ascending=False)[:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['ARREST_KEY', 'ARREST_DATE', 'PD_CD', 'PD_DESC', 'KY_CD', 'OFNS_DESC',\n",
       "       'LAW_CODE', 'LAW_CAT_CD', 'ARREST_BORO', 'ARREST_PRECINCT',\n",
       "       'JURISDICTION_CODE', 'AGE_GROUP', 'PERP_SEX', 'PERP_RACE', 'X_COORD_CD',\n",
       "       'Y_COORD_CD', 'Latitude', 'Longitude', 'zipcode', 'city', 'Year',\n",
       "       'Month', 'Day'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [],
   "source": [
    "x = df[['AGE_GROUP','PERP_RACE']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "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>AGE_GROUP</th>\n",
       "      <th>PERP_RACE</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>25-44</td>\n",
       "      <td>BLACK</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>25-44</td>\n",
       "      <td>ASIAN / PACIFIC ISLANDER</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>18-24</td>\n",
       "      <td>BLACK</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>25-44</td>\n",
       "      <td>WHITE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>45-64</td>\n",
       "      <td>BLACK</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  AGE_GROUP                 PERP_RACE\n",
       "0     25-44                     BLACK\n",
       "1     25-44  ASIAN / PACIFIC ISLANDER\n",
       "2     18-24                     BLACK\n",
       "3     25-44                     WHITE\n",
       "4     45-64                     BLACK"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "\n",
    "#x.groupby(by='AGE_GROUP').agg({'PERP_RACE':sum})\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [],
   "source": [
    "#sns.barplot(data = x.AGE_GROUP)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1.2941131591796875\n"
     ]
    }
   ],
   "source": [
    "start_time = time.time()\n",
    "groupby_age = x.groupby(by='AGE_GROUP').agg({'PERP_RACE':list})\n",
    "print(time.time()-start_time)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [],
   "source": [
    "groupby_age['len'] = groupby_age.PERP_RACE.apply(lambda x:len(x))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [],
   "source": [
    "groupby_age = groupby_age.sort_values(by=['len'],ascending = False).iloc[:10,:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PERP_RACE</th>\n",
       "      <th>len</th>\n",
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       "    <tr>\n",
       "      <th>AGE_GROUP</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "    <tr>\n",
       "      <th>25-44</th>\n",
       "      <td>[BLACK, ASIAN / PACIFIC ISLANDER, WHITE, BLACK...</td>\n",
       "      <td>2199242</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18-24</th>\n",
       "      <td>[BLACK, BLACK, WHITE HISPANIC, WHITE, WHITE, B...</td>\n",
       "      <td>1272256</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45-64</th>\n",
       "      <td>[BLACK, BLACK, WHITE, WHITE, BLACK, BLACK, BLA...</td>\n",
       "      <td>879628</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>&lt;18</th>\n",
       "      <td>[BLACK, WHITE HISPANIC, WHITE HISPANIC, BLACK,...</td>\n",
       "      <td>401573</td>\n",
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       "    <tr>\n",
       "      <th>65+</th>\n",
       "      <td>[WHITE, WHITE, BLACK, WHITE HISPANIC, BLACK, W...</td>\n",
       "      <td>38965</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>895</th>\n",
       "      <td>[BLACK, BLACK, BLACK, WHITE HISPANIC, BLACK HI...</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>945</th>\n",
       "      <td>[WHITE, WHITE HISPANIC, BLACK, WHITE HISPANIC,...</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>935</th>\n",
       "      <td>[BLACK, WHITE HISPANIC, WHITE HISPANIC, BLACK,...</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>894</th>\n",
       "      <td>[BLACK, BLACK, BLACK, WHITE, BLACK, BLACK, UNK...</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>937</th>\n",
       "      <td>[WHITE HISPANIC, BLACK HISPANIC, BLACK, BLACK,...</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                   PERP_RACE      len\n",
       "AGE_GROUP                                                            \n",
       "25-44      [BLACK, ASIAN / PACIFIC ISLANDER, WHITE, BLACK...  2199242\n",
       "18-24      [BLACK, BLACK, WHITE HISPANIC, WHITE, WHITE, B...  1272256\n",
       "45-64      [BLACK, BLACK, WHITE, WHITE, BLACK, BLACK, BLA...   879628\n",
       "<18        [BLACK, WHITE HISPANIC, WHITE HISPANIC, BLACK,...   401573\n",
       "65+        [WHITE, WHITE, BLACK, WHITE HISPANIC, BLACK, W...    38965\n",
       "895        [BLACK, BLACK, BLACK, WHITE HISPANIC, BLACK HI...       13\n",
       "945        [WHITE, WHITE HISPANIC, BLACK, WHITE HISPANIC,...        7\n",
       "935        [BLACK, WHITE HISPANIC, WHITE HISPANIC, BLACK,...        7\n",
       "894        [BLACK, BLACK, BLACK, WHITE, BLACK, BLACK, UNK...        7\n",
       "937        [WHITE HISPANIC, BLACK HISPANIC, BLACK, BLACK,...        5"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "groupby_age"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1.161742925643921\n"
     ]
    }
   ],
   "source": [
    "start_time = time.time()\n",
    "groupby_race = x.groupby(by='PERP_RACE').agg({'AGE_GROUP':list})\n",
    "print(time.time()-start_time)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>AGE_GROUP</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PERP_RACE</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>AMERICAN INDIAN/ALASKAN NATIVE</th>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>ASIAN / PACIFIC ISLANDER</th>\n",
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       "    </tr>\n",
       "    <tr>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>BLACK HISPANIC</th>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>OTHER</th>\n",
       "      <td>[25-44, 25-44, 25-44, 25-44, 25-44, &lt;18, 25-44...</td>\n",
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       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                                        AGE_GROUP\n",
       "PERP_RACE                                                                        \n",
       "AMERICAN INDIAN/ALASKAN NATIVE  [25-44, 45-64, 25-44, 25-44, 25-44, 45-64, 25-...\n",
       "ASIAN / PACIFIC ISLANDER        [25-44, 18-24, 25-44, 45-64, 25-44, 25-44, 45-...\n",
       "BLACK                           [25-44, 18-24, 45-64, 18-24, 25-44, <18, 18-24...\n",
       "BLACK HISPANIC                  [25-44, 25-44, 25-44, 25-44, 18-24, 25-44, 45-...\n",
       "OTHER                           [25-44, 25-44, 25-44, 25-44, 25-44, <18, 25-44..."
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "groupby_race.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [],
   "source": [
    "age_groups = groupby_age.index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['25-44', '18-24', '45-64', '<18', '65+', '895', '945', '935', '894',\n",
       "       '937'],\n",
       "      dtype='object', name='AGE_GROUP')"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "age_groups"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "25-44\n",
      "18-24\n",
      "45-64\n",
      "<18\n",
      "65+\n",
      "895\n",
      "945\n",
      "935\n",
      "894\n",
      "937\n"
     ]
    }
   ],
   "source": [
    "def group_freq(x,group):\n",
    "    \n",
    "    count = 0\n",
    "    \n",
    "    for g in x:\n",
    "        if g == group:\n",
    "            count+=1\n",
    "            \n",
    "            \n",
    "    return count\n",
    "\n",
    "for group in age_groups:\n",
    "    print(group)\n",
    "    groupby_race[group] = groupby_race.AGE_GROUP.apply(lambda x:group_freq(x,group))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\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>AGE_GROUP</th>\n",
       "      <th>25-44</th>\n",
       "      <th>18-24</th>\n",
       "      <th>45-64</th>\n",
       "      <th>&lt;18</th>\n",
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       "      <th>937</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PERP_RACE</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>AMERICAN INDIAN/ALASKAN NATIVE</th>\n",
       "      <td>[25-44, 45-64, 25-44, 25-44, 25-44, 45-64, 25-...</td>\n",
       "      <td>4786</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ASIAN / PACIFIC ISLANDER</th>\n",
       "      <td>[25-44, 18-24, 25-44, 45-64, 25-44, 25-44, 45-...</td>\n",
       "      <td>93448</td>\n",
       "      <td>45268</td>\n",
       "      <td>39602</td>\n",
       "      <td>12136</td>\n",
       "      <td>2361</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BLACK</th>\n",
       "      <td>[25-44, 18-24, 45-64, 18-24, 25-44, &lt;18, 18-24...</td>\n",
       "      <td>1010260</td>\n",
       "      <td>615195</td>\n",
       "      <td>466471</td>\n",
       "      <td>219733</td>\n",
       "      <td>16595</td>\n",
       "      <td>8</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BLACK HISPANIC</th>\n",
       "      <td>[25-44, 25-44, 25-44, 25-44, 18-24, 25-44, 45-...</td>\n",
       "      <td>169686</td>\n",
       "      <td>116185</td>\n",
       "      <td>56214</td>\n",
       "      <td>39198</td>\n",
       "      <td>2423</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>OTHER</th>\n",
       "      <td>[25-44, 25-44, 25-44, 25-44, 25-44, &lt;18, 25-44...</td>\n",
       "      <td>678</td>\n",
       "      <td>361</td>\n",
       "      <td>200</td>\n",
       "      <td>115</td>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                                        AGE_GROUP  \\\n",
       "PERP_RACE                                                                           \n",
       "AMERICAN INDIAN/ALASKAN NATIVE  [25-44, 45-64, 25-44, 25-44, 25-44, 45-64, 25-...   \n",
       "ASIAN / PACIFIC ISLANDER        [25-44, 18-24, 25-44, 45-64, 25-44, 25-44, 45-...   \n",
       "BLACK                           [25-44, 18-24, 45-64, 18-24, 25-44, <18, 18-24...   \n",
       "BLACK HISPANIC                  [25-44, 25-44, 25-44, 25-44, 18-24, 25-44, 45-...   \n",
       "OTHER                           [25-44, 25-44, 25-44, 25-44, 25-44, <18, 25-44...   \n",
       "\n",
       "                                  25-44   18-24   45-64     <18    65+  895  \\\n",
       "PERP_RACE                                                                     \n",
       "AMERICAN INDIAN/ALASKAN NATIVE     4786    2833    1870     851     94    0   \n",
       "ASIAN / PACIFIC ISLANDER          93448   45268   39602   12136   2361    0   \n",
       "BLACK                           1010260  615195  466471  219733  16595    8   \n",
       "BLACK HISPANIC                   169686  116185   56214   39198   2423    1   \n",
       "OTHER                               678     361     200     115      9    0   \n",
       "\n",
       "                                945  935  894  937  \n",
       "PERP_RACE                                           \n",
       "AMERICAN INDIAN/ALASKAN NATIVE    0    0    0    0  \n",
       "ASIAN / PACIFIC ISLANDER          1    0    0    0  \n",
       "BLACK                             3    3    5    2  \n",
       "BLACK HISPANIC                    0    0    0    1  \n",
       "OTHER                             0    0    0    0  "
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "groupby_race.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [],
   "source": [
    "groupby_race = groupby_race.drop(columns=['AGE_GROUP'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['25-44', '18-24', '45-64', '<18', '65+', '895', '945', '935', '894',\n",
       "       '937'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "groupby_race.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>AMERICAN INDIAN/ALASKAN NATIVE</th>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ASIAN / PACIFIC ISLANDER</th>\n",
       "      <td>93448</td>\n",
       "      <td>45268</td>\n",
       "      <td>39602</td>\n",
       "      <td>12136</td>\n",
       "      <td>2361</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BLACK</th>\n",
       "      <td>1010260</td>\n",
       "      <td>615195</td>\n",
       "      <td>466471</td>\n",
       "      <td>219733</td>\n",
       "      <td>16595</td>\n",
       "      <td>8</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BLACK HISPANIC</th>\n",
       "      <td>169686</td>\n",
       "      <td>116185</td>\n",
       "      <td>56214</td>\n",
       "      <td>39198</td>\n",
       "      <td>2423</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>OTHER</th>\n",
       "      <td>678</td>\n",
       "      <td>361</td>\n",
       "      <td>200</td>\n",
       "      <td>115</td>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                  25-44   18-24   45-64     <18    65+  895  \\\n",
       "PERP_RACE                                                                     \n",
       "AMERICAN INDIAN/ALASKAN NATIVE     4786    2833    1870     851     94    0   \n",
       "ASIAN / PACIFIC ISLANDER          93448   45268   39602   12136   2361    0   \n",
       "BLACK                           1010260  615195  466471  219733  16595    8   \n",
       "BLACK HISPANIC                   169686  116185   56214   39198   2423    1   \n",
       "OTHER                               678     361     200     115      9    0   \n",
       "\n",
       "                                945  935  894  937  \n",
       "PERP_RACE                                           \n",
       "AMERICAN INDIAN/ALASKAN NATIVE    0    0    0    0  \n",
       "ASIAN / PACIFIC ISLANDER          1    0    0    0  \n",
       "BLACK                             3    3    5    2  \n",
       "BLACK HISPANIC                    0    0    0    1  \n",
       "OTHER                             0    0    0    0  "
      ]
     },
     "execution_count": 91,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "groupby_race.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [],
   "source": [
    "groupby_race = groupby_race.drop(columns=['895','945','935','894','937'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['25-44', '18-24', '45-64', '<18', '65+'], dtype='object')"
      ]
     },
     "execution_count": 94,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "groupby_race.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "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>25-44</th>\n",
       "      <th>18-24</th>\n",
       "      <th>45-64</th>\n",
       "      <th>&lt;18</th>\n",
       "      <th>65+</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PERP_RACE</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>AMERICAN INDIAN/ALASKAN NATIVE</th>\n",
       "      <td>4786</td>\n",
       "      <td>2833</td>\n",
       "      <td>1870</td>\n",
       "      <td>851</td>\n",
       "      <td>94</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ASIAN / PACIFIC ISLANDER</th>\n",
       "      <td>93448</td>\n",
       "      <td>45268</td>\n",
       "      <td>39602</td>\n",
       "      <td>12136</td>\n",
       "      <td>2361</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BLACK</th>\n",
       "      <td>1010260</td>\n",
       "      <td>615195</td>\n",
       "      <td>466471</td>\n",
       "      <td>219733</td>\n",
       "      <td>16595</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BLACK HISPANIC</th>\n",
       "      <td>169686</td>\n",
       "      <td>116185</td>\n",
       "      <td>56214</td>\n",
       "      <td>39198</td>\n",
       "      <td>2423</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>OTHER</th>\n",
       "      <td>678</td>\n",
       "      <td>361</td>\n",
       "      <td>200</td>\n",
       "      <td>115</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                  25-44   18-24   45-64     <18    65+\n",
       "PERP_RACE                                                             \n",
       "AMERICAN INDIAN/ALASKAN NATIVE     4786    2833    1870     851     94\n",
       "ASIAN / PACIFIC ISLANDER          93448   45268   39602   12136   2361\n",
       "BLACK                           1010260  615195  466471  219733  16595\n",
       "BLACK HISPANIC                   169686  116185   56214   39198   2423\n",
       "OTHER                               678     361     200     115      9"
      ]
     },
     "execution_count": 95,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "groupby_race.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f79c9bd7bd0>"
      ]
     },
     "execution_count": 99,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x1080 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "groupby_race.plot.pie(y = '25-44',figsize=(12,15))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f79c9493290>"
      ]
     },
     "execution_count": 102,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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6Hpq58LgndSf58yQnVdX/vtC5qk6uqnuv4/jp7q9kdtfwpQsvjpneAPqTy+1TVXdeNF11W278mc3bkeTM6Ry2dvedk9ylqr59US1vTnJYkiWft6uqg5M8L8m/dPdaXvSS7v6PzKaB3r+q7rWWfQAAYH8g5K1sR5LXL2o7J7OplUcm+cuquiSzN0R+PcnL1rH/Ys9NctfFjVV1RJKvrvPZt2XH7e5rMwuTT5m++uCDSZ6Q2Zs+1+s5mb085oPT5/CGaX05Byd54fSVBhdm9obSX5zb/j+r6qrp54LMpmouPo/X58YvsEmW/vxeVVUXJbkkya2TPHpu2+Jn8p6/aN9Mn9WLkjxthXMCAID9Ss1mz7G/mu6M3XXRK/7Zz5xwl1v2uT93zGaXkSQ5+tcuXr0TAAAHnKra1d3bV+vnmbz9XHf/2WbXAAAAHDhM1wQAABiIkAcAADAQIQ8AAGAgQh4AAMBAhDwAAICBCHkAAAADEfIAAAAGIuQBAAAMRMgDAAAYiJAHAAAwECEPAABgIEIeAADAQIQ8AACAgQh5AAAAAxHyAAAABiLkAQAADETIAwAAGIiQBwAAMBAhDwAAYCBCHgAAwEC2bHYBMIKbH3Vcjv61nZtdBgAAuJMHAAAwEiEPAABgIEIeAADAQIQ8AACAgQh5AAAAAxHyAAAABiLkAQAADETIAwAAGIiQBwAAMBAhDwAAYCBCHgAAwECEPAAAgIEIeQAAAAPZstkFwAg+/JkP5wG/+4DNLgMAgA10/lPO3+wS1sSdPAAAgIEIeQAAAAMR8gAAAAYi5AEAAAxEyAMAABiIkAcAADAQIQ8AAGAgQh4AAMBAhDwAAICBCHkAAAADEfIAAAAGIuQBAAAMRMgDAAAYiJAHAAAwECEPAABgIEIeAADAQIQ8AACAgQh5AAAAAxHyAAAABiLkAQAADETIAwAAGIiQBwAAMBAhDwAAYCBCHgeMqrq+qi6sqg9U1fur6qSpfWtVXbLMPluq6uqqet6i9oOr6vlV9dGquqSq3ldVj5i2XVlVh0/L96uqK6rqvht9fgAAsDcIeRxIru3ubd19nyTPSvK81XZI8vAklyV5bFXVXPtvJDkqyfHdfXySRyW5zfyOVXVCkrOTnNrd/7g3TgAAADaakMeB6rZJvrCGfjuSvCTJx5PcP0mq6lZJfjbJU7r7uiTp7k9392vn9rtXkjck+anuft/eLBwAADbSls0uANbhllV1YZJbZHYX7iErda6qWyZ5aJKfS3L7zALfBUmOSfLx7v7SCru/MclPdvd7Vjj+GUnOSJKbH3bzdZwGAABsHHfyOJAsTNe8Z5KTk7xy0RTMxR6Z5B3d/ZUk5yR5TFUdtMax3prkiSv17+6Xd/f27t5+8KEHr/UcAABgQwl5HJC6+4Ikhyc5YoVuO5I8rKquTLIryR2TPDjJ5UmOrqrbrLDvk6c/f3/PqwUAgH1HyOOAVFX3THJQks8ts/22SR6Y5Oju3trdW5M8KcmO6c7eK5K8tKpuPvU/qqp+cu4Q38gsJB5bVb++cWcCAAB7l2fyOJAsPJOXJJXk8d19/TRj89iqumqu70uSvH3hxSqTNyb57ao6JMlzkvxmkg9W1VeT/HuSX5sfrLuvq6pHJ3lnVX26u39vY04LAAD2HiGPA0Z3L/l8XHdfmWTVh+K6+/O54fTOZ0w/i/ttnVv+YpJt6ywVAAA2jemaAAAAAxHyAAAABiLkAQAADETIAwAAGIiQBwAAMBAhDwAAYCBCHgAAwECEPAAAgIEIeQAAAAMR8gAAAAYi5AEAAAxEyAMAABiIkAcAADAQIQ8AAGAgQh4AAMBAhDwAAICBCHkAAAADEfIAAAAGIuQBAAAMRMgDAAAYiJAHAAAwkC2bXQCM4J5H3jPnP+X8zS4DAADcyQMAABiJkAcAADAQIQ8AAGAgQh4AAMBAhDwAAICBCHkAAAADEfIAAAAGIuQBAAAMRMgDAAAYiJAHAAAwECEPAABgIEIeAADAQIQ8AACAgWzZ7AJgBF++7LK88/t/YLPLYA/8wLveudklAADsFe7kAQAADETIAwAAGIiQBwAAMBAhDwAAYCBCHgAAwECEPAAAgIEIeQAAAAMR8gAAAAYi5AEAAAxEyAMAABiIkAcAADAQIQ8AAGAgQh4AAMBAhDwAAICBCHkAAAADEfIAAAAGIuQBAAAMRMgDAAAYiJAHAAAwECEPAABgIEIeAADAQIQ8AACAgQh5AAAAAxHybgKq6vqqurCqPlBV76+qk6b2rVV1yTL7bKmqq6vqeYvaD66q51fVR6vqkqp6X1U9Ytp2ZVUdPi3fr6quqKr7Ltr/QVV17qK2s6rqlGn5vKraPi3/dFVdXFUXTWM9eq7/FdM5vb+qvncNdZ9XVTvn1rdX1XlL1VRVj6iqnVX1oar6cFW9cI0fNQAAbDoh76bh2u7e1t33SfKsJM9bbYckD09yWZLHVlXNtf9GkqOSHN/dxyd5VJLbzO9YVSckOTvJqd39j7tTcFXdNcmzkzywu09Icv8kF811eXp3b0vyzCT/cw11J8mRC4F0hXGPT/KyJD/Z3fdKcnySj+3OOQAAwGYQ8m56bpvkC2votyPJS5J8PLOAlaq6VZKfTfKU7r4uSbr709392rn97pXkDUl+qrvftwd1Hpnky0mumca5pruvWKLfu5Ics1Ldc16Q5DmrjPuMJM/t7g9P4369u39//eUDAMDm2LLZBbBP3LKqLkxyi8zuwj1kpc5VdcskD03yc0lun1lwuiCzMPXx7v7SCru/MbO7YO9Zoc/3TfUsODrJuYv6fCDJp5NcUVVvS/K67v7LJY71qCQXr1L3gguSPKaqHpxZgFzK8UletELt31RVZyQ5I0nudMgha9kFAAA2nDt5Nw0L0zXvmeTkJK9cYirjvEcmeUd3fyXJOZkFo4PWONZbkzxxlf7vnurZNk25fNPiDt19/VTrKUk+kuTFVXXmXJcXTEHxjCQ/s466fzOr381bk+5+eXdv7+7ttzv44L1xSAAA2GNC3k1Md1+Q5PAkR6zQbUeSh1XVlUl2JbljkgcnuTzJ0VV1mxX2ffL05x5PceyZ93X385KcluTH5jY/fQqJP9jdCy+PWa7u+WO+PbM7mounci64NMn99rR2AADYLELeTUxV3TPJQUk+t8z22yZ5YJKju3trd29N8qQkO6Y7ZK9I8tKquvnU/6iq+sm5Q3wjs7B1bFX9+h7UeeeqOnGuaVuSf16h/7J1L9H9uZk9e7eUFyT51ar6rum4N6uqX96NUwAAgE3hmbybhlvOPQNXSR7f3ddPMzaPraqr5vq+JMnbF16sMnljkt+uqkMym+r4m0k+WFVfTfLvSX5tfrDuvm76uoN3VtWnu/v3dqPmg5O8sKrunOSrST6b5OdX6P+jq9Q9X99fVdVnlzpId19UVb+U5C+mF810kv9vN+oHAIBNUd292TXAAe/Y29ymX37fE1fvyH7rB971zs0uAQBgRVW1q7u3r9bPdE0AAICBCHkAAAADWTXkVdVD5pbvvmjbj25EUQAAAOyetdzJe+Hc8jmLtu2V7xsDAABg71hLyKtllpdaBwAAYBOtJeT1MstLrQMAALCJ1vI9ed9RVW/K7K7dwnKm9bsvvxsAAAD72lpC3qPnll+4aNvidQAAADbRWkLeB5Mc0d0fnG+squOSfGZDqgIAAGC3rOWZvN9NcsQS7XdN8pK9Ww4AAAB7Yi0h797d/c7Fjd39t0lO2PslAQAAsLvWEvIO3s1tAAAA7GNrCXkfraofXtxYVY9I8rG9XxIAAAC7ay0vXnlqknOr6rFJdk1t25N8b5JHblRhAAAArN+qd/K6+yNJ7p3knUm2Tj/vTHLCtA0AAID9xFru5KW7r0vyJ/NtVfWAqvqJ7n7ShlQGAADAuq0p5C2oqm1JdiQ5NckVSV63EUUBAACwe1YNeVX1XUlOyyzcfS7Ja5JUdz94g2sDAABgndZyJ+/DSd6d5FHdfXmSVNVTN7QqAAAAdstavkLhx5J8Ksk7quoPq+qhSWpjywIAAGB3rOXtmq/v7lOT3DPJeZl9pcKdquoPqurhG1wfAAAA61Ddvf6dqu6Q5MeTnNrdD9nrVcEBZvv27b1z587NLgMAgIFV1a7u3r5av3W9XXNBd3++ql6T5Ijd2R8AAICNsep0zaq6W1W9vKrOraonVtWtqupFST4SIQ8AAGC/spY7ea9M8s4k5yQ5Ocl7k1ya5ITu/tQG1gYAAMA6rSXk3aG7z5yW/7aqPp3kf+vu6zauLAAAAHbHmp7Jq6rD8q2vTfhUkltV1a2T2fN5G1QbAAAA67SWkHe7JLtyw+/Ge//0Zyf5jr1dFAAAALtn1ZDX3VvXcqCqOq67L93jigAAANhtq75dcx3+1148FgAAALthb4a8Wr0LAAAAG2lvhrzei8cCAABgN+zNkAcAAMAm25sh72t78VgAAADshlVDXlUdWVX/o6rOrarnVdVtl+rX3fff++UBAACwHmu5k/fKJP+e5HeTHJrkpRtaEQAAALttLV+G/m3d/exp+W+r6v0r9gYAAGDTrCXkVVUdlm99RcJB8+vd/fmNKg4AAID1WUvIu12SXbnh9+At3M3rJN+xt4sCAABg96wa8rp76z6oAwAAgL1gt79CoaqOrao/3JvFAAAAsGfW8hUKJ1TVm6vqkqr6zaq6U1Wdk+RtST648SUCAACwVmu5k/eHSf48yY8l+Wxmz+N9LMkx3f3iDawNAACAdVrLi1cO6e6zpuXLquppSZ7Z3ddvXFkAAADsjrWEvFtU1X3zrbdrXpPkhKpa+AoF35sHAACwn1hLyPtUkt9ZZr2TPGRvFwUAAMDuWctXKDxoH9QBAADAXrCWt2s+Y275xxdt+62NKAoAAIDds5a3a542t/ysRdtO3ou1AAAAsIfWEvJqmeWl1gEAANhEawl5vczyUusAAABsorW8XfM+VfWlzO7a3XJazrR+iw2rDAAAgHVby9s1D9oXhQAAALDnVg15VXWLJD+f5JgkFyX54+7++kYXBgAAwPqt5Zm8P02yPcnFSX44yYs2tCIAAAB221qeyfvu7r53klTVK5K8b2NLAgAAYHet5U7efywsmKYJAACwf1vP2zWTG75hs5J0d992w6oDAABgXVa9k9fdB3X3baef23T3lrllAY99oqruWlVvrKqPVtU/VdVLquqHqurC6eeaqrpsWn5lVT2oqs5ddIyzquqUafm8uf4XVtXZU/uZVfWvU9sHq2rHZpwvAADsrrVM14RNVVWV5HVJ3tDd90jyXUkOTfKw7t7W3duS7Exy+rT+uDUeeqH/tu4+Za79xdMxH53kf1bVwXvxdAAAYEMJeRwIHpLkq939J0nS3dcneWqSn66qW23UoN390SRfSXLYRo0BAAB721qeyYPNdlySXfMN3f2lqvp4vvX9jUv5vqq6cG796CTzUzhfVVXXTstv6e6nz+9cVScm+Wh3f2aPqgcAgH1IyONAUEl6He0L3t3dj/xm56qzFm0/vbt3LrHfU6vqZ5N8R5KTly2q6owkZyTJ0UcfvUIZAACw75iuyYHg0iTb5xuq6rZJ7pbknzZgvBd397FJTk3yyqq6xVKduvvl3b29u7cfccQRG1AGAACsn5DHgeBtSW5VVY9Lkqo6KMmLkpzV3V/ZqEG7+3WZvdDl8Rs1BgAA7G1CHvu97u4kj0ny41X10SQfSfLVJL+6h4d+1dxXKLx1mT6/nuSXq8r/VgAAOCDU7L+fgT2xffv23rlzqcf7AABg76iqXd29fbV+7k4AAAAMRMgDAAAYiJAHAAAwECEPAABgIEIeAADAQIQ8AACAgQh5AAAAAxHyAAAABiLkAQAADETIAwAAGIiQBwAAMBAhDwAAYCBCHgAAwECEPAAAgIEIeQAAAAMR8gAAAAYi5AEAAAxEyAMAABiIkAcAADAQIQ8AAGAgQh4AAMBAhDwAAICBCHkAAAADEfIAAAAGIuQBAAAMRMgDAAAYiJAHAAAwECEPAABgIEIeAADAQIQ8AACAgQh5AAAAAxHyAAAABiLkAQAADETIAwAAGIiQBwAAMBAhDwAAYCBbNrsAGMEnr/inPPcnT1l2+7P/7Ox9WA0AADdl7uQBAAAMRMjGC1ajAAATJklEQVQDAAAYiJAHAAAwECEPAABgIEIeAADAQIQ8AACAgQh5AAAAAxHyAAAABiLkAQAADETIAwAAGIiQBwAAMBAhDwAAYCBCHgAAwECEPAAAgIEIeQAAAAMR8gAAAAYi5AEAAAxEyAMAABiIkAcAADAQIQ8AAGAgQh4AAMBAhDwAAICBCHnsdVW1taouWdR2ZlU9rarOqqp/rapDpvbDq+rKpfarqp+tqvdX1WEr7TetH1dVb6+qj1TVR6vq/6pvubqqDpv6HVVVXVUPnNv3s1V1x6nGr1TVkXPbrtmYTwkAADaGkMdmuD7JT6/Uoap+KslTkjy8u7+w0n5Vdcskb0ry/O7+riT3SXJSkl/o7k7y90m+d+p+UpJ/nP5MVR2b5Oru/ty0/eokv7L7pwYAAJtLyGMz/I8kT62qLUttrKrHJnlmZgHv6jXs9xNJzu/uNydJd38lyZOnYyTJ+ZlC3fTn7+SGoe/v5o71x0lOrao77M6JAQDAZhPy2AwfT/KeJD+1xLZvT/KyzALep9a433FJds03dPc/JTm0qm6bWYhbCHn/KckbktxtWj8psxC44JrMgt4vruN8AABgvyHksRF6De2/leTpufE1+NnMwtxjlznGUvvVKmO+L8l9q+rWSQ7u7muSfKyqjsmN7+QlyUuTPH4KiMuqqjOqamdV7fz3r163UlcAANhnhDw2wueSHLao7Q6ZPe+WJOnuy5NcmBuHua8keUSSn6+q0xcfeJn9Lk2yfb5fVX1Hkmu6+8vT9M3LM3ue7/1Tl/cm+eEkRya5bNEY/5bkz5P8wkon2d0v7+7t3b391rc4ZKWuAACwzwh57HXTnbJPVtVDk2R6vu3kzKZazntukqctsf9np/6/VVU/tMQQi/d7VZIHVtXDpvFumdnduN+e63N+kl9KcsG0fkFmUzLfO72cZbHfSfJzSZZ8bhAAAPZXQh4b5XFJnlNVFyZ5e5L/Pj0n903dfWm+dWcti7ZdkeQ/J/njqvqelfbr7muTPHoa77IkFyf5h8ye7VtwfpLvyLdC3vuT3DU3nqq5cMyrk7w+iVt0AAAcUGrpmxjAetzljof1Lzzioctuf/afnb0PqwEAYERVtau7t6/Wz508AACAgQh5AAAAAxHyAAAABiLkAQAADETIAwAAGIiQBwAAMBAhDwAAYCBCHgAAwECEPAAAgIEIeQAAAAMR8gAAAAYi5AEAAAxEyAMAABiIkAcAADAQIQ8AAGAgQh4AAMBAhDwAAICBCHkAAAADEfIAAAAGIuQBAAAMRMgDAAAYiJAHAAAwkC2bXQCM4Ki7f2ee/Wdnb3YZAADgTh4AAMBIhDwAAICBCHkAAAADEfIAAAAGIuQBAAAMRMgDAAAYiJAHAAAwECEPAABgIEIeAADAQIQ8AACAgQh5AAAAAxHyAAAABiLkAQAADGTLZhcAI/jqJ7+cDz337ZtdxgHjXs9+yGaXAAAwLHfyAAAABiLkAQAADETIAwAAGIiQBwAAMBAhDwAAYCBCHgAAwECEPAAAgIEIeQAAAAMR8gAAAAYi5AEAAAxEyAMAABiIkAcAADAQIQ8AAGAgQh4AAMBAhDwAAICBCHkAAAADEfIAAAAGIuQBAAAMRMgDAAAYiJAHAAAwECEPAABgIEIeAADAQLZsdgGwFlX14iT/3N3/Y1r/2yT/0t1PnNZflORfk/x0dx8/t9+ZSa7p7hdW1VlJzk1yepK7Jzk0yRFJrpi6/0KS30pyVJJrp7bLu/uUjT07AADYe9zJ40Dxd0lOSpKqulmSw5McN7f9pCTnr+VA3f2Y7t6W5IlJ3t3d26afv5u6nD7XJuABAHBAEfI4UJyfKeRlFu4uSfLlqjqsqg5Jcq8kX9is4gAAYH9huiYHhO7+RFV9vaqOzizsXZDkLkm+N8kXk1yU5GtJvrOqLpzb9duSvHCdw72qqhama76lu5++Z9UDAMC+I+RxIFm4m3dSkt/JLOSdlFnIW5hq+U/TVMwk33wmb71O7+6dq3WqqjOSnJEkR93uyN0YBgAA9j7TNTmQLDyXd+/Mpmu+N7M7eWt+Hm9v6u6Xd/f27t5+h1vffl8PDwAASxLyOJCcn+SRST7f3dd39+eT3D6zoHfBplYGAAD7CdM1OZBcnNlbNf98Uduh3X11VR26l8aZfybv6u5+2F46LgAAbDghjwNGd1+f5LaL2p4wt3xlkuMXbT9zqb7T+nlJzlvU9qC9USsAAGwW0zUBAAAGIuQBAAAMRMgDAAAYiJAHAAAwECEPAABgIEIeAADAQIQ8AACAgQh5AAAAAxHyAAAABiLkAQAADETIAwAAGIiQBwAAMBAhDwAAYCBCHgAAwECEPAAAgIEIeQAAAAMR8gAAAAYi5AEAAAxEyAMAABiIkAcAADAQIQ8AAGAgQh4AAMBAtmx2ATCCWxx1m9zr2Q/Z7DIAAMCdPAAAgJEIeQAAAAMR8gAAAAYi5AEAAAxEyAMAABiIkAcAADAQIQ8AAGAgQh4AAMBAhDwAAICBCHkAAAADEfIAAAAGIuQBAAAMRMgDAAAYyJbNLgBG8IlPfCJnnnnmZpcB3IT5OwiABe7kAQAADETIAwAAGIiQBwAAMBAhDwAAYCBCHgAAwECEPAAAgIEIeQAAAAMR8gAAAAYi5AEAAAxEyAMAABiIkAcAADAQIQ8AAGAgQh4AAMBAhDwAAICBCHkAAAADEfIAAAAGIuQBAAAMRMgDAAAYiJAHAAAwECEPAABgIEIeAADAQIQ8AACAgQh5g6uqF1fVL82t/21V/dHc+ouq6peramtVXbJo3zOr6mnT8llVdUpVvb6qLqyqy6vqi9PyhVV1UlWdV1WXzbWdvUQ9T6iqly1qO6+qtk/LV1bV4dPys6vq0qq6aDre98z1v6yqPlBV51fVsXPHOqKq/qOqfm7RGFdW1Tlz66dU1VlL1VRVj6uqS6axP7jwGQAAwIFAyBvf3yU5KUmq6mZJDk9y3Nz2k5Kcv9aDdfdjuntbkicmeXd3b5t+/m7qcvpc2ym7W3RVfW+SRyY5sbtPSPKwJP8y1+X07r5Pkj9N8oK59h9P8t4kO5Y47PaqOm6J9vlxH5Hkl5I8vLuPS3Jiki/u7nkAAMC+JuSN7/xMIS+zcHdJki9X1WFVdUiSeyX5x80qbgVHJbm6u69Lku6+urs/sUS/dyU5Zm59R5JfSXLXqrrLor4vTPKrq4z7rCRPWxiru7/a3X+4OycAAACbQcgb3BRWvl5VR2cW9i5I8vdJvjfJ9iQXdffXpu7fOTfV8sIkP78bQ75q7hgvWKbPqYvG2b5EnzcnuVtVfaSqfr+qfmCZYz0qycVJUlV3S/Jt3f2+JK9Ncuqivq9NcmJVHZPlHZ9k1wrbAQBgv7Zlswtgn1i4m3dSkt9Jcpdp+YuZTedc8E/TVMwks2fydmOs07t75yp9XtPdT54b57zFHbr7mqq6X5LvS/LgJK+pqmd291lTl1dV1bVJrkzylKnttMyCXJK8OskrMjvfBddnNrXzWUn+eh3ntKSqOiPJGUlyu9vdbk8PBwAAe4U7eTcNC8/l3Tuz6ZrvzexO3rqex9vXuvv67j6vu/9bkicn+bG5zQvP/v1Idy88q7cjyROq6sokb0pyn6q6x6LD/q8k35/k6GWGvTTJ/dZY38u7e3t3b7/VrW61xrMCAICNJeTdNJyf2UtMPj8Fp88nuX1mQe+CTa1sGVV17KKAti3JP6/UP8mtu/su3b21u7cmeV5md/e+qbv/I8mLM3u5ylKel+S3q+rbpuMeUlX/ZffPBAAA9i0h76bh4szeqvneRW1f7O6r9/JY88/kvXUPjnNokj+dvsLgoiTfneTMFfrvSPL6RW3nZOm3bL4iy0xV7u6/SvJ7Sd5aVZdm9nyeac0AABwwqrs3uwY44N35znfuM844Y7PLAG7CzjzzzM0uAYANVlW7unuplxbegDt5AAAAAxHyAAAABiLkAQAADETIAwAAGIiQBwAAMBAhDwAAYCBCHgAAwECEPAAAgIEIeQAAAAMR8gAAAAYi5AEAAAxEyAMAABiIkAcAADAQIQ8AAGAgQh4AAMBAhDwAAICBCHkAAAADEfIAAAAGIuQBAAAMRMgDAAAYiJAHAAAwECEPAABgINXdm10DHPC2b9/eO3fu3OwyAAAYWFXt6u7tq/VzJw8AAGAgQh4AAMBAhDwAAICBCHkAAAADEfIAAAAGIuQBAAAMxFcowF5QVV9Octlm18EB7fAkV292ERzQXEPsKdcQe8o1tPG+vbuPWK3Tln1RCdwEXLaW7yyB5VTVTtcQe8I1xJ5yDbGnXEP7D9M1AQAABiLkAQAADETIg73j5ZtdAAc81xB7yjXEnnINsadcQ/sJL14BAAAYiDt5AAAAAxHyYA9U1clVdVlVXV5Vz9zseth8VXVlVV1cVRdW1c6p7Q5V9Zaq+uj052FTe1XVS6fr56KqOnHuOI+f+n+0qh4/136/6fiXT/vWvj9L9qaq+uOq+kxVXTLXtuHXzHJjcOBZ5ho6s6r+dfq76MKq+uG5bc+arofLquqH5tqX/Detqu5eVX8/XSuvqaqbT+2HTOuXT9u37pszZm+rqrtV1Tuq6kNVdWlV/eLU7u+iA5SQB7upqg5K8ntJHpHku5PsqKrv3tyq2E88uLu3zb1G+plJ3tbd90jytmk9mV0795h+zkjyB8nsH7wk/y3J9yT5T0n+29w/en8w9V3Y7+SNPx022Fm58e9xX1wzy43BgeesLP13wYunv4u2dfdfJcn079RpSY6b9vn9qjpolX/T/u/pWPdI8oUkPzO1/0ySL3T3MUlePPXjwPT1JL/S3fdKcv8kT5p+//4uOkAJebD7/lOSy7v7Y939tSSvTvLoTa6J/dOjk/zptPynSX5krv2VPfPeJLevqqOS/FCSt3T357v7C0nekuTkadttu/uCnj1Q/cq5Y3GA6u53Jfn8ouZ9cc0sNwYHmGWuoeU8Osmru/u67r4iyeWZ/Xu25L9p092WhyQ5e9p/8fW4cA2dneShZhccmLr7k939/mn5y0k+lOQu8XfRAUvIg913lyT/Mrd+1dTGTVsneXNV7aqqM6a2O3X3J5PZP6RJjpzal7uGVmq/aol2xrMvrpnlxmAcT56m0v3x3N2U9V5Dd0zyb9399UXtNzjWtP2LU38OYNO02/sm+fv4u+iAJeTB7lvq/630uloe0N0nZjaV5UlV9f0r9F3uGlpvOzcdrhnW6g+SfGeSbUk+meRFU/vevIZcX4OpqkOTnJPkl7r7Syt1XaLN30X7ESEPdt9VSe42t37XJJ/YpFrYT3T3J6Y/P5Pk9ZlNgfr0NFUl05+fmbovdw2t1H7XJdoZz764ZpYbgwF096e7+/ru/kaSP8zs76Jk/dfQ1ZlNxduyqP0Gx5q23y5rnzbKfqaqDs4s4L2qu183Nfu76AAl5MHu+4ck95jeOnbzzB5kf9Mm18QmqqpbV9VtFpaTPDzJJZldFwtvGHt8kjdOy29K8v+3dz8hVlZhHMe/P5AKTCIJ1DaWQdnGNhIuXESKkrtgyFbCpLuiZRuhdRtduY0EBReBkoLg3xbRxk06YX90pr+U0CJQl0VPi/cMXMTBtLlzuYfvBx6Ge9733HvmzsN73+fOec+7r61Stg243aaqnAN2JXm6TbHaBZxr2+4m2daue9k38lzqy0rkzFKvoQ4snjQ3bzIci2D4u7/dVsZ8nmEBjCss8ZnWrp/6HJhp/e/Nx8UcmgEulzdgnkrt+PAx8G1VHR7Z5LFoWlWVYRiPGMAe4AawAByc9HiMiefDJuBai+uLOcFwjcol4Gb7uba1h2E1uwXga2DryHO9w7AgwjwwO9K+leFkbQE4AmTSv7fxv/PmBMN0ur8Yvu3evxI5s9RrGNMXS+TQsZYjcwwn0RtG9j/Y8uF74I2R9vt+prVj25WWW58Cj7f2J9rj+bZ906TfC+ORc2g7w/TJOeBqiz0ei6Y3Ft9cSZIkSVIHnK4pSZIkSR2xyJMkSZKkjljkSZIkSVJHLPIkSZIkqSMWeZIkSZLUEYs8SZK0opK8l2Q+SSV5ZqT9qSRnklxLcj3J7CTHKUnTyiJPkiSNXZLHkqxuD78EdgI/37Pbu8A3VfUK8BpwqN2YW5L0ECzyJEnS2CR5OckhhhtvvwhQVV9V1U/32b2ANUkCPAn8Cfy9UmOVpF6smvQAJElSX9p/7N4C9gMBPgG2VNXdB3Q9ApwGfgfWAHur6p9xjlWSemSRJ0mSltstYA44UFXfPUS/3cBV4HXgBeBCki+q6s4YxihJ3XK6piRJWm4zwG/AqSQfJtn4H/vNAidrMA/8CGwe1yAlqVcWeZIkaVlV1fmq2gtsB24DnyW5mOS5B3T9BdgBkGQd8BLwwxiHKkldSlVNegySJKlzSV4FblXVr0neBz4A1gN/AGer6kCSZ4GjwAaGa/k+qqrjkxqzJE0rizxJkiRJ6ojTNSVJkiSpIxZ5kiRJktQRizxJkiRJ6ohFniRJkiR1xCJPkiRJkjpikSdJkiRJHbHIkyRJkqSOWORJkiRJUkf+BSgX07o8xhYbAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 864x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize = (12,10))\n",
    "sns.barplot(y = groupby_race.index, x = groupby_race['<18'] )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "8.328898491395648\n"
     ]
    }
   ],
   "source": [
    "black_arrest_juvenile = groupby_race[(groupby_race.index == \"BLACK\")]['<18'].values[0]\n",
    "\n",
    "white_arrest_juvenile = groupby_race[(groupby_race.index == \"WHITE\")][\"<18\"].values[0]\n",
    "\n",
    "black_arrest_wrt_white_arrests_juvenile = black_arrest_juvenile / white_arrest_juvenile\n",
    "\n",
    "print(black_arrest_wrt_white_arrests_juvenile)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Juvenile Crime in black population is 8.32 times more common than in white population"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "219733"
      ]
     },
     "execution_count": 119,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "groupby_race[(groupby_race.index == \"BLACK\")]['<18'].values[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "groupby_race[groupby_race['<18']==\"BLACK\"]"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "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": "3.7.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}