us-arrests-data-exploration.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 288,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Imports the required packages\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 289,
   "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>City</th>\n",
       "      <th>Murder</th>\n",
       "      <th>Assault</th>\n",
       "      <th>UrbanPop</th>\n",
       "      <th>Rape</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Alabama</td>\n",
       "      <td>13.20</td>\n",
       "      <td>236</td>\n",
       "      <td>58</td>\n",
       "      <td>21.20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Alaska</td>\n",
       "      <td>10.00</td>\n",
       "      <td>263</td>\n",
       "      <td>48</td>\n",
       "      <td>44.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Arizona</td>\n",
       "      <td>8.10</td>\n",
       "      <td>294</td>\n",
       "      <td>80</td>\n",
       "      <td>31.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Arkansas</td>\n",
       "      <td>8.80</td>\n",
       "      <td>190</td>\n",
       "      <td>50</td>\n",
       "      <td>19.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>California</td>\n",
       "      <td>9.00</td>\n",
       "      <td>276</td>\n",
       "      <td>91</td>\n",
       "      <td>40.60</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         City  Murder  Assault  UrbanPop  Rape\n",
       "0     Alabama   13.20      236        58 21.20\n",
       "1      Alaska   10.00      263        48 44.50\n",
       "2     Arizona    8.10      294        80 31.00\n",
       "3    Arkansas    8.80      190        50 19.50\n",
       "4  California    9.00      276        91 40.60"
      ]
     },
     "execution_count": 289,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# load the UsArrests dataset and create dataframes\n",
    "df = pd.read_csv('UsArrests.csv')\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 290,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of states: 50\n"
     ]
    }
   ],
   "source": [
    "print('Number of states: {}'.format(df.shape[0]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 291,
   "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>missing</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>City</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Murder</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Assault</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>UrbanPop</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Rape</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          missing\n",
       "City            0\n",
       "Murder          0\n",
       "Assault         0\n",
       "UrbanPop        0\n",
       "Rape            0"
      ]
     },
     "execution_count": 291,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Count missing values\n",
    "missing = df.isnull().sum()\n",
    "relevant_missing = pd.DataFrame(missing, columns=[\"missing\"])\n",
    "relevant_missing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 292,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "City         object\n",
       "Murder      float64\n",
       "Assault       int64\n",
       "UrbanPop      int64\n",
       "Rape        float64\n",
       "dtype: object"
      ]
     },
     "execution_count": 292,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#checks type of data in columns\n",
    "df.dtypes"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## EDA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 293,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\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>Murder</th>\n",
       "      <th>Assault</th>\n",
       "      <th>UrbanPop</th>\n",
       "      <th>Rape</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>50.00</td>\n",
       "      <td>50.00</td>\n",
       "      <td>50.00</td>\n",
       "      <td>50.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>7.79</td>\n",
       "      <td>170.76</td>\n",
       "      <td>65.54</td>\n",
       "      <td>21.23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>4.36</td>\n",
       "      <td>83.34</td>\n",
       "      <td>14.47</td>\n",
       "      <td>9.37</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.80</td>\n",
       "      <td>45.00</td>\n",
       "      <td>32.00</td>\n",
       "      <td>7.30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>4.08</td>\n",
       "      <td>109.00</td>\n",
       "      <td>54.50</td>\n",
       "      <td>15.07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>7.25</td>\n",
       "      <td>159.00</td>\n",
       "      <td>66.00</td>\n",
       "      <td>20.10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>11.25</td>\n",
       "      <td>249.00</td>\n",
       "      <td>77.75</td>\n",
       "      <td>26.18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>17.40</td>\n",
       "      <td>337.00</td>\n",
       "      <td>91.00</td>\n",
       "      <td>46.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Murder  Assault  UrbanPop  Rape\n",
       "count   50.00    50.00     50.00 50.00\n",
       "mean     7.79   170.76     65.54 21.23\n",
       "std      4.36    83.34     14.47  9.37\n",
       "min      0.80    45.00     32.00  7.30\n",
       "25%      4.08   109.00     54.50 15.07\n",
       "50%      7.25   159.00     66.00 20.10\n",
       "75%     11.25   249.00     77.75 26.18\n",
       "max     17.40   337.00     91.00 46.00"
      ]
     },
     "execution_count": 293,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#To set the decimal precision at 2 points in float format\n",
    "pd.set_option('display.float_format', lambda x: '%.2f' % x) \n",
    "# gives general info about data\n",
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 295,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Highest murders:\n",
      "               City  Murder  Assault  UrbanPop  Rape\n",
      "9          Georgia   17.40      211        60 25.80\n",
      "23     Mississippi   16.10      259        44 17.10\n",
      "8          Florida   15.40      335        80 31.90\n",
      "17       Louisiana   15.40      249        66 22.20\n",
      "39  South Carolina   14.40      279        48 22.50\n",
      " Highest assaults:\n",
      "               City  Murder  Assault  UrbanPop  Rape\n",
      "32  North Carolina   13.00      337        45 16.10\n",
      "8          Florida   15.40      335        80 31.90\n",
      "19        Maryland   11.30      300        67 27.80\n",
      "2          Arizona    8.10      294        80 31.00\n",
      "30      New Mexico   11.40      285        70 32.10\n",
      " Highest rapes:\n",
      "           City  Murder  Assault  UrbanPop  Rape\n",
      "27      Nevada   12.20      252        81 46.00\n",
      "1       Alaska   10.00      263        48 44.50\n",
      "4   California    9.00      276        91 40.60\n",
      "5     Colorado    7.90      204        78 38.70\n",
      "21    Michigan   12.10      255        74 35.10\n",
      " Highest urban population:\n",
      "              City  Murder  Assault  UrbanPop  Rape\n",
      "4      California    9.00      276        91 40.60\n",
      "29     New Jersey    7.40      159        89 18.80\n",
      "38   Rhode Island    3.40      174        87  8.30\n",
      "31       New York   11.10      254        86 26.10\n",
      "20  Massachusetts    4.40      149        85 16.30\n"
     ]
    }
   ],
   "source": [
    "# sorting data into groups\n",
    "states = df.City #states\n",
    "homicide = df.sort_values(by=['Murder'], ascending=False) # max to min murders per 100,000 population\n",
    "violent = df.sort_values(by=['Assault'], ascending=False) # max to min assaults per 100,000 population\n",
    "sexual_abuse = df.sort_values(by=['Rape'], ascending=False) # max to min rapes per 100,000 population\n",
    "pop_urban = df.sort_values(by=['UrbanPop'], ascending=False) # max to min urban population %\n",
    "print(f\"\"\"Highest murders:\n",
    " {homicide.head()}\n",
    " Highest assaults:\n",
    " {violent.head()}\n",
    " Highest rapes:\n",
    " {sexual_abuse.head()}\n",
    " Highest urban population:\n",
    " {pop_urban.head()}\"\"\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 294,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1500x1000 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Histograms will give a general idea on the distribution of crimes/arrests committed throughout the USA\n",
    "# Plot histograms\n",
    "histograms = df.hist(grid=False, figsize=(15, 10)) #This can also be done for each column individually by calling hist on that column\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 296,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'# Plot six different scatter plots with the different combinations of the variables\\nsns.pairplot(df, hue = \"City\")\\nplt.show()\\nplt.close()'"
      ]
     },
     "execution_count": 296,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Plot six different scatter plots with the different combinations of the variables\n",
    "sns.pairplot(df, hue = \"City\")\n",
    "plt.show()\n",
    "plt.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 297,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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fFwDExx9/bDA96/PMyckpz/meO3dOABDvvvtuntfRaDT60SQPDw8xf/78fOfOUqhdJ9u0aQNPT09UqFABffv2hZOTE77//nv4+flhy5YtyMzMRO/evfHo0SP9Px8fHwQHB2Pfvn0G89JoNBg6dKjBtM2bN6Ns2bJ44403cty3SqUCAGzatAkuLi546aWXDO4nPDwcTk5OOe4nLCxM/w0GADw9PVGlShVcv349X8v8bON78uQJHj16hGbNmiEpKQmXLl0CABw7dgzR0dEYMWKEwQZp/fv3h5ubm8H8Nm3ahKpVqyI0NNQgf6tWrQAgR/5nHTt2DA8ePMBrr71msE6zY8eOCA0Nxfbt2/O1TMaWMT09HdHR0ahcuTJcXV1x4sSJHNcfMWJEruuBc3tO87u8Wd9Ktm3bhszMzALlzxo52LlzJ5KSkgp029zs2bMHaWlpeOutt/TfJgHdcjs7O+d4nHNb7txERUXh1KlTGDx4sMFox0svvYSwsLAi537WqVOncOXKFfTr1w/R0dH6xz0xMRGtW7fG77//nuNxHj16tMHvBX1POzk5YcCAAfrfbW1t0aBBg3y/17LLzMzE1q1b0blzZ/22Us/K+puQJa/XZW7CwsLQqFEj/e9ZW8+3atUKFStWzDE9axliYmKwd+9e9O7dW//34NGjR4iOjka7du1w5coV3LlzB4DuNX3+/HlcuXKlAEudN1M/vs/K/tw3a9YM0dHRePz4MYCCvxaKytvbG6dPn8bo0aMRGxuLJUuWoF+/fvDy8sLHH3+sH/nKEhERAW9vb7z44osAdK+NPn36YP369dBqtYXOMXToUIPRq6zPElM85gBQt25dNGzYELNmzcLKlSsRGRmJHTt2YNSoUbCxsUFycnKet42IiACg+5zJy44dO/Dzzz9j9uzZqFix4nNH3HJTqNUQCxcuREhICKytreHt7Y0qVaro/5heuXIFQggEBwfnettnh20BwM/PL8dGJdeuXUOVKlUMPnCzu3LlCuLj4+Hl5ZXr5Vkbe2V59o2fxc3NDbGxsXnex7POnz+PDz/8EHv37tW/cbLEx8cDAG7evAkAObZUtba2zrF3xZUrV3Dx4kV4enrmK/+zsu6nSpUqOS4LDQ3FgQMHjC9MHpKTkzFz5kysXLkSd+7cMXgjZi3jswIDA3OdT27PaX6Xt0+fPli2bBleffVVvP/++2jdujW6d++Onj17Gnxg5yYwMBDjx4/HnDlzEBERgWbNmqFLly4YMGBAnqsgjMnrcba1tUWlSpX0l2fJbbmNzTe390iVKlVyLWaFlfUBZWwINz4+3qDMZn9eC/qeLl++fI4PcDc3N5w5c6ZA2bM8fPgQjx8/RvXq1fN1/bxel7nJ/nch63VSoUKFXKdn/b24evUqhBD46KOP8NFHH+U67wcPHsDPzw/Tpk1D165dERISgurVq6N9+/YYOHBgjo3U8svUj++zsj8eWa+L2NhYODs7F/i1UBjZl83X1xeLFy/GokWLcOXKFezcuROzZs3CpEmT4Ovrq98jSKvVYv369XjxxRdx48YN/e0bNmyI2bNn49dff0Xbtm0LlcnY42IqmzdvRp8+fTBs2DAAgFqtxvjx4/Hbb7/h8uXLud5GCIF169ahevXqRl9PWeWpQ4cO6Nq1K6pXrw4nJyeMHTs23/kKVRYaNGiQa8MHdN8CVCoVduzYkWu7z76bS/Z1NPmVmZkJLy8vfavKLvuHUl7fNLI309zExcWhRYsWcHZ2xrRp0xAUFAQ7OzucOHEC7733XoG/AWflr1GjBubMmZPr5dn/WJWEN954AytXrsRbb72FRo0awcXFBSqVCn379s11GfN67nKbnt/ltbe3x++//459+/Zh+/bt+OWXX7Bhwwa0atUKu3bteu43xtmzZ2PIkCHYtm0bdu3ahTfffBMzZ87En3/++dztAYqqsK/l4pT1vH3++ed57qL4vPdkQd/TRXmvmUJBnoe8sj5vGbIe1wkTJqBdu3a5XjfrS0Pz5s1x7do1/Wty2bJlmDt3LpYsWWKw62tRM5vi8c3PchfktZBd1khoXt+Uk5KS8twDRKVSISQkBCEhIejYsSOCg4MRERGhfwz37t2LqKgorF+/HuvXr89x+4iIiEKXhZJ4Tfv5+eHAgQO4cuUK7t27h+DgYPj4+KBcuXIICQnJ9TYHDx7EzZs3MXPmzHzfT1BQEOrUqYOIiIjiLwvPCyKEQGBgYJ4LmJ95/PXXX0hPT8+zqQYFBWHPnj1o0qSJyf5IZ2+0Wfbv34/o6Ghs2bLFYAOSZ9sroNsYB9B968hqcgCQkZGByMhIg+YXFBSE06dPo3Xr1nneb16y7ufy5cv6Yfwsly9f1l9eUN999x0GDx6M2bNn66elpKSY5CiABVleKysrtG7dGq1bt8acOXMwY8YM/O9//8O+ffvQpk2b596+Ro0aqFGjBj788EMcOnQITZo0wZIlSzB9+vQCZX72ca5UqZJ+elpaGm7cuFHoYxlkzTe3Yem8vkEUVtYGbM7OzoXOa4r3dHYFec17enrC2dkZ586dM8l9m0LW68HGxiZfj6u7uzuGDh2KoUOHIiEhAc2bN8eUKVMKVRaKoqB/a7Ir6mvh2fdU9i9ESUlJ+Oeff/L1gV6pUiW4ubkhKipKPy0iIgJeXl5YuHBhjutv2bIF33//PZYsWVIqS/2zgoOD9SM3Fy5cQFRUFIYMGZLrdSMiIqBSqdCvX78C3UdycrJ+o8r8Mvnhnrt37w61Wo2pU6fmaF1CCERHRz93Hj169MCjR4+wYMGCHJdlzbN3797QarX4+OOPc1wnIyOjUB9wWfuTZ79tVqt8dnnS0tJy7MNar149eHh4YOnSpcjIyNBPj4iIyDFc1bt3b9y5cwdLly7NkSM5OdnoOqV69erBy8sLS5YsMXjCd+zYgYsXL6Jjx47PWdLcqdXqHM/ZV199VaR1fVnyu7wxMTE5Ls/6Rpy1rHk9T48fPzZ43AFdcbCysirwGwPQbZtja2uL+fPnGzwuy5cvR3x8fKEfZ19fX9SuXRurV682WL2ze/duXLhwoVDzzEt4eDiCgoLwxRdf5Hq0u4cPHz53HqZ4T2eX13OYGysrK3Tr1g0//vgjjh07luPykhqxeJaXlxdatmyJr7/+2uADK8uzj2v2x8fJyQmVK1cu1GuyqBwdHYtU/ov6WmjdujVsbW2xePHiHKOV33zzDTIyMgz26Pjrr79y/Vt45MgRREdH61cRJicnY8uWLejUqRN69uyZ49/YsWPx5MkT/PDDD4Vd9BKXmZmJd999Fw4ODjm2JQF025Vt2rQJTZs2zXU1e0ZGRq6rSY4cOYKzZ8/muXYgL8UysjB9+nRMnDhRv9tgmTJlcOPGDXz//fcYOXIkJkyYYHQegwYNwpo1azB+/HgcOXIEzZo1Q2JiIvbs2YPXXnsNXbt2RYsWLTBq1CjMnDkTp06dQtu2bWFjY4MrV65g06ZNmDdvHnr27Fmg7LVr14ZarcasWbMQHx8PjUaDVq1aoXHjxnBzc8PgwYPx5ptvQqVSYe3atTneLLa2tpgyZQreeOMNtGrVCr1790ZkZCRWrVqFoKAgg1Y/cOBAbNy4EaNHj8a+ffvQpEkTaLVaXLp0CRs3bsTOnTvzfDJtbGwwa9YsDB06FC1atMArr7yi33UyICAAb7/9doGWO0unTp2wdu1auLi4ICwsDIcPH8aePXv0u8QWRX6Xd9q0afj999/RsWNH+Pv748GDB1i0aBHKly+Ppk2bAtC9xlxdXbFkyRKUKVMGjo6OaNiwIU6fPo2xY8eiV69eCAkJQUZGBtauXQu1Wo0ePXoUOLOnpycmTpyIqVOnon379ujSpQsuX76MRYsWoX79+gYbmRXUzJkz0bFjRzRt2hTDhg1DTEwMvvrqK1SrVi3PQ9gWhpWVFZYtW4YOHTqgWrVqGDp0KPz8/HDnzh3s27cPzs7O+PHHH43OwxTv6dzmmdtzmNf2BjNmzMCuXbvQokUL/a63UVFR2LRpEw4cOCDlXB4LFy5E06ZNUaNGDYwYMQKVKlXC/fv3cfjwYdy+fRunT58GoNuIsmXLlggPD4e7uzuOHTuG7777rkBDwKYSHh6OPXv2YM6cOShXrhwCAwMLdEjkor4WvLy8MGnSJHz44Ydo3rw5unTpAgcHBxw6dAjffvst2rZtqz/4EKDb3TMiIgIvv/wywsPDYWtri4sXL2LFihWws7PDBx98AAD44Ycf8OTJE3Tp0iXX+33hhRfg6emJiIgI9OnTJ9/LawoLFixAXFwc7t69CwD48ccfcfv2bQC6Vb9Z28OMGzcOKSkpqF27NtLT07Fu3TocOXIEq1evzrUM7Ny5E9HR0Xlu2JiQkIAKFSqgT58+qFatGhwdHXH27FmsXLkSLi4ueW5rk6eC7DpRkN3wNm/eLJo2bSocHR2Fo6OjCA0NFa+//rq4fPmy/jpZBxjJTVJSkvjf//4nAgMDhY2NjfDx8RE9e/YU165dM7jeN998I8LDw4W9vb0oU6aMqFGjhnj33XfF3bt39dfJOvhKdrkdKGnp0qWiUqVKQq1WG+y2dPDgQfHCCy/oD4Ly7rvv6nezyr6r5fz584W/v7/QaDSiQYMG4uDBgyI8PFy0b9/e4HppaWli1qxZ+gPzuLm5ifDwcDF16lQRHx//vIdYbNiwQdSpU0d/oJfsB2USomDPWWxsrBg6dKgoW7ascHJyEu3atROXLl0S/v7+BrtbGZunsec0P8v766+/iq5du4py5coJW1tbUa5cOfHKK6+Iv//+22Be27ZtE2FhYcLa2lq/O9L169fFsGHDRFBQkP6gUi+++KLYs2fPc5fd2C5dCxYsEKGhocLGxkZ4e3uLMWPG5HlQpoLYvHmzqFq1qtBoNCIsLCxfB2USomC7TmY5efKk6N69u/Dw8BAajUb4+/uL3r17i19//TVfj0FW3sK+p3NbrtyeQ2Nu3rwpBg0aJDw9PYVGoxGVKlUSr7/+eo6DMuW2/MYOypQdAPH6668bTLtx44YAID7//HOD6deuXRODBg0SPj4+wsbGRvj5+YlOnTqJ7777Tn+d6dOniwYNGghXV1dhb28vQkNDxSeffCLS0tKMLq+xgzJll9vjm5tLly6J5s2bC3t7+1wPypT9uc/tcRMif68FY/7v//5PvPDCC8LR0VFoNBoRGhoqpk6dmuMAbWfOnBHvvPOOqFu3rnB3dxfW1tbC19dX9OrVy+AgWZ07dxZ2dna5HkAqy5AhQ4SNjY149OhRntfJa9fJ7O+1rNdDfnb9ze2gb1n/nn1cV65cKWrVqiUcHR1FmTJlROvWrcXevXvznG/fvn2FjY2NiI6OzvXy1NRUMW7cOFGzZk3h7OwsbGxshL+/vxg+fHiO5zM/VEJIGMOzMJmZmfD09ET37t1zHYYnIiIqzXiKahNLSUnJsXpizZo1iImJ4alPiYjILHFkwcT279+Pt99+G7169YKHhwdOnDiB5cuXo2rVqjh+/LjJT1RCRERU3Ey+gaOlCwgIQIUKFTB//nzExMTA3d0dgwYNwqeffsqiQEREZokjC0RERGQUt1kgIiIio1gWiIiIyCiWBSIiIjKKZYGIiIiMYlkgIiIio1gWiIiIyCiWBSIiIjKKZYGIiIiMYlkgIiIio1gWiIiIyCiWBSIiIjKKZYGIiIiMYlkgIiIio1gWiIiIyCiWBSIiIjKKZYGIiIiMYlkgIiIio1gWiIiIyCiWBSIiIjKKZYGIiIiMYlkgIiIio1gWiIiIyCiWBSIiIjKKZYGIiIiMYlkgIiIio1gWiIiIyCiWBSIiIjKKZYGIiIiMYlkgIiIio1gWiIiIyCiWBSIiIjKKZYGIiIiMYlkgIiIio1gWiIiIyCiWBSIiIjKKZYGIiIiMYlkgIiIio1gWiIiIyCiWBSIiIjLKWnYAIipGQgBJ0cCTKODJPd2/lDggIwXISP33Z5ruZ2aG7voQT28LAVhZA3YugJ0rYO/69Ke9m+E0a1sZS0hEJYBlgchcZWqBmOtA3K1/i0BUtp/3gIT7QGZ6yeSxcdAVCHs3wNUfKFsZ8AgGygbrfjp6lEwOIjI5lRBCyA5BRM+RmgDcPwfcOwvcOwPcOwc8uAhkJMtOln/2bs+Uh8pPS4R7JY5KEJVyLAtEpU38nX9Lwb/F4P45IOYG9KsHlEalBryqAv6NgYqNdD/L+MhORUTPYFkgku3xXeDqr8C1vcCN34GkR7ITyedeCajYGPBvpCsQHkGyExFZNJYFopKWngxEHtSVg2u/Ag8vyU5U+jn5/Fsc/i0Q3tUBlUp2KiKLwbJAVBLundMVg2t7gZuHAW2q7ETmzckbqPIfILQTENic2zwQFTOWBaLioM0Aru8Hzm/RrWJIuCc7kXJpnIHgl3TFIaQdYOsoOxGR4rAsEJnSrb+As5uAC1uBxIey01geGwddYajeU1cgrDWyExEpAssCUVHFXAdORuhKQtxN2Wkoi8YFqNoJqN4DqPQiYMUD1hIVFssCUWGkJQEXtgEn1wI3D0GxuzUqhas/0GAEUGeA7ngPRFQgLAtEBRF1Gji6HDj/PZD6WHYaKigbB6Bmb6DBKMA7THYaIrPBskCUH9f3Awe+BK7vk52ETCWgGdBgJBDaEbBSy05DVKqxLBDlJTMTuPgDcPBL4O5J2WmouLhUAOoNA8KHAA7ustMQlUosC0TZZaQCp78FDn0FRF+VnYZKirWdbi+KxmN1h58mIj2WBaIsKY+BYyuAPxfzuAiWTGUF1OgFvPgB4BYgOw1RqcCyQJTwAPhzEXB0BZAaLzsNlRZqW6DuYKDFu4CTl+w0RFKxLJDlSk/RrWo4MBdIT5SdhkorG0fghdFAk3GAnYvsNERSsCyQZTq3Gdg9BYi/JTsJmQt7N6DJW0DDUYCNvew0RCWKZYEsy92TwC8TgVuHZSchc1XGV7dqos4gQG0tOw1RiWBZIMvw5B7w6zTg1DrwaItkEu5BQNvpQOh/ZCchKnYsC6RsGanA4QXAH3OAtATZaUiJwroC//mCG0GSorEskHKd3wrsnsSTO1Hxs3MF2n4M1B0kOwlRsWBZIOV5HAX8MBa4ukd2ErI0gc2BzvMA90qykxCZFMsCKcu5zcD2/wLJsbKTkKWytgdavg80foPnnCDFYFkgZUiOBbZPAM59JzsJkY5vLaDLV7qfRGaOZYHM37W9wNbXgSd3ZSchMmRlDTR6HWg5kcdmILPGskDmKy1JtwHj0WXg7pBUqnlUBnquBHxryk5CVCgsC2Sebh8Hvh/Js0KS+VBrgHafAA1GyE5CVGAsC2RetBnAb7OAP2YDQis7DVHBhXXVbcvA80yQGWFZIPPxOArYOBC4fVR2EqKicQvQrZbwqys7CVG+sCyQebj1J7BxEJBwX3YSItNQa4AOs4B6Q2UnIXoulgUq/Y4s1Z38KTNddhIi06s7SHe4aGuN7CREeWJZoNIrIxXYPh44+X+ykxAVr3J1gT5rAZfyspMQ5YplgUqnhIfA+n7A7SOykxCVDIeyQK9VQGAz2UmIcmBZoNLnwUVgXW8g7pbsJEQlS20LvPw1UL277CREBlgWqHS5ugfYNBRIfSw7CZEcKiugw2c8HgOVKiwLVHocWQrseI/HTyACgBbvAS9+IDsFEQCWBSot9s3QHWyJiJ4KHwp0nANYWclOQhaOZYHk2z0ZOPil7BREpVPVLkCPZdy1kqRiWSC5fpkI/LlIdgqi0i2gGdB3HWDnLDsJWSiWBZJDCODnCf+eMZKInsunBjBgC+DkJTsJWSCWBSp5mZnAT+OAE2tkJyEyL26BwMAtgHsl2UnIwrAsUMnK1ALbXgdOfys7CZF5KuMLDPtFdzIqohLCTWyp5GgzgC0jWBSIiuJJFLCmG/CEJ1WjksOyQCVDmw58NwQ4t1l2EiLzF3sDWPsykBwrOwlZCJYFKn7aDN3ppS/+KDsJkXI8OA9E9AbSkmQnIQvAskDF78dxwOWfZacgUp7bR4AN/YGMNNlJSOFYFqh4/fYZcIqnmCYqNtf26rYFysyUnYQUjGWBis+ZjcC+T2SnIFK+C1uBn96SnYIUjGWBiseNP3S7SBJRyTixWnfodKJiwLJApvfwsm49qpbrUYlK1MEvgQNfyk5BCsSyQKaV8ACI6AmkxMtOQmSZ9kwGzn4nOwUpDMsCmU5aErCuDxB3S3YSIsv2w5vA/QuyU5CCsCyQaWRmAptfBe6ekJ2EiNITgQ0DOMJHJsOyQKaxcyJwebvsFESUJeYa8P1o3RleiYqIZYGK7tS3wF9LZKcgouwu/wz8MVt2ClIAnnWSiib6GvB1cyAtQXYSIsqNygoYsBkIaiU7CZkxjixQ4WWkAd8NZVEgKs1EJvDdcG54TEXCskCFt2cyEHVadgoiep7kGGDDQCAjVXYSMlMsC1Q4f+8E/lwkOwUR5VfUKWD7f2WnIDPFskAF9zgK2DpGdgoiKqiTa4Hjq2SnIDPEskAFk5mpO8NdUrTsJERUGDveAx5dkZ2CzAzLAhXMgdlA5B+yUxBRYWWk6I6/kKmVnYTMCMsC5d+tv4D9n8pOQURFdeeY7qRTRPnEskD5kxynO5xzZobsJERkCvs/Be6fl52CzATLAuXPLxOBeO6nTaQY2jTd6ghtuuwkZAZYFuj5bvwOnF4nOwURmdq9M8CBL2WnIDPAskDGZaQCP70tOwURFZffP+feEfRcLAtk3O9fANFXZacgouKiTQV+HMezU5JRLAuUt4eXucU0kSW4eRA4sVp2CirFWBYobz+9rdsIioiUb/ck4Ml92SmolGJZoNyd/U73bYOILENKPLDrf7JTUCnFskA5pSUCuz6SnYKIStrZ74C7J2WnoFKIZYFy+v1z4Mld2SmIqMQJYPdk2SGoFGJZIEPR14DDC2WnICJZbvwGXN0jOwWVMiwLZOiXidyokcjS7Z6iO8Ms0b9YFuipf44AV3bKTkFEst0/C5zdKDsFlSIsC/TU/pmyExBRabH3E90RXInAskBZ/jkCXNsrOwURlRbxt4AjS2WnoFKCZYF0OKpARNn98YXu9PRk8VgWCPjnKEcViCin5FjgwFzZKagUYFkgjioQUd7++hqIvyM7BUnGsmDp/jkKXPtVdgoiKq0ykoE/ZstOQZKxLFg6jioQ0fOc/la3SoIsFsuCJbt9jKMKRPR86UnA8VWyU5BELAuWjKMKRJRfR5YB2gzZKUgSlgVLdfs4j/9ORPn3+DZwcZvsFCQJy4KlOvK17AREZG7+XCw7AUnCsmCJkmOBC/yGQEQFdPuoblsnsjgsC5bo9HogI0V2CiIyR38ukp2AJGBZsETHV8tOQETm6sI2HqTJArEsWJpbfwIPL8pOQUTmKjMDOPKN7BRUwlgWLA33lSaiojqxGkhLkp2CShDLgiVJjgXOfy87BRGZu+RY4MwG2SmoBLEsWJLTG7hhIxGZxtlNshNQCWJZsCQnuGEjEZnIrcPA47uyU1AJYVmwFLf+Ah5ckJ2CiJRCZALnt8pOQSWEZcFScMNGIjK1c5tlJ6ASwrJgCdKTgQtbZacgIqW5cwyIvSk7BZUAlgVLcG2f7hSzRESmxj2sLALLgiW4/LPsBESkVFwVYRFYFpROCODKLtkpiEip7p0Boq/JTkHFjGVB6e4cBxLuy05BRErG0QXFY1lQOq6CIKLidm6L7ARUzFgWlO7yDtkJiEjpHl4EHvAEdUrGsqBksZE8EBMRlYwL22QnoGLEsqBkHFUgopJybZ/sBFSMWBYkGDJkCLp161b8d8TtFYiopNw5BqQ+kZ2Ciomiy8Lhw4ehVqvRsWNH2VGMatmyJd566y3TzjQ5Drh5yLTzJCLKS2YG/+YomKLLwvLly/HGG2/g999/x927FnZ2tKt7dG9eIqKScn2/7ARUTBRbFhISErBhwwaMGTMGHTt2xKpVq/SXxcbGon///vD09IS9vT2Cg4OxcuVKAEBaWhrGjh0LX19f2NnZwd/fHzNnztTfds6cOahRowYcHR1RoUIFvPbaa0hISNBfPmXKFNSuXdsgy5dffomAgIBccw4ZMgS//fYb5s2bB5VKBZVKhcjIyKI/ANf2Fn0eREQFwbKgWIotCxs3bkRoaCiqVKmCAQMGYMWKFRBCAAA++ugjXLhwATt27MDFixexePFilC1bFgAwf/58/PDDD9i4cSMuX76MiIgIgw96KysrzJ8/H+fPn8fq1auxd+9evPvuu4XOOW/ePDRq1AgjRoxAVFQUoqKiUKFChSItOwDg1p9FnwcRUT4IGwfE+TTGH5rmiH6SIjsOFQNr2QGKy/LlyzFgwAAAQPv27REfH4/ffvsNLVu2xK1bt1CnTh3Uq1cPAAzKwK1btxAcHIymTZtCpVLB39/fYL7PblsQEBCA6dOnY/To0Vi0aFGhcrq4uMDW1hYODg7w8fEp1DxySHwExPDwq0RUPISNA+I9auOcTQ38klAZ3z/wReIT3XfPxZGx6FDDV3JCMjVFloXLly/jyJEj+P573dnQrK2t0adPHyxfvhwtW7bEmDFj0KNHD5w4cQJt27ZFt27d0LhxYwC61QIvvfQSqlSpgvbt26NTp05o27atft579uzBzJkzcenSJTx+/BgZGRlISUlBUlISHBwcpCxvDv/8JTsBESmIsXKQ3ZHIGJYFBVJkWVi+fDkyMjJQrlw5/TQhBDQaDRYsWIAOHTrg5s2b+Pnnn7F79260bt0ar7/+Or744gvUrVsXN27cwI4dO7Bnzx707t0bbdq0wXfffYfIyEh06tQJY8aMwSeffAJ3d3ccOHAAw4cPR1paGhwcHGBlZaVf3ZElPT29ZB8AroIgoiIoSDnI7mhkTDGnIxkUVxYyMjKwZs0azJ4922BEAAC6deuGb7/9FqNHj4anpycGDx6MwYMHo1mzZnjnnXfwxRdfAACcnZ3Rp08f9OnTBz179kT79u0RExOD48ePIzMzE7Nnz4aVle6Ns3HjRoP78PT0xL179yCEgEqlAgCcOnXKaGZbW1totVoTPQLgyAIRFUhRykF2F6OeICE1A04axX28WDTFPZs//fQTYmNjMXz4cLi4uBhc1qNHDyxfvhx3795FeHg4qlWrhtTUVPz000+oWrUqAN3eDr6+vqhTpw6srKywadMm+Pj4wNXVFZUrV0Z6ejq++uordO7cGQcPHsSSJUsM7qNly5Z4+PAhPvvsM/Ts2RO//PILduzYAWdn5zwzBwQE4K+//kJkZCScnJzg7u6uLyMFpk0Hok4X7rZEZBFMWQ6y02YKnL8Tj4aVPEwyPyodFLc3xPLly9GmTZscRQHQlYVjx47B2toaEydORM2aNdG8eXOo1WqsX78eAFCmTBl89tlnqFevHurXr4/IyEj8/PPPsLKyQq1atTBnzhzMmjUL1atXR0REhMFulQBQtWpVLFq0CAsXLkStWrVw5MgRTJgwwWjmCRMmQK1WIywsDJ6enrh161bhH4AHF4EMbo1MRE9l7a1woMIofOj2OaonfYPakWMx4EoL/F+UHxK1pv0ouBj12KTzI/lUIvsKdjJvJ9YAP7whOwURSSRsHBDnUQfnbGpgR0JlfP/AG8ladYndf+965fFZz1oldn9U/BS3GsLi3T0lOwERlbBcy8GTkisH2V2M4jkilIZlQWmiTslOQETFrLSVg+z+vv8EGdpMWKsVt6bbYrEsKIk2Hbh/XnYKIjKx0l4OskvNyMT1R4kI8S4jO0qhTJkyBVu3bn3unmyWhLVPSaKvcuNGIgUQNg6I9WmCPyqMxgduXyAs6WvUiXwdA680x7qociW6/UFhXbhb9I0chwwZApVKhdGjR+e47PXXX4dKpcKQIUOKfD/0fBxZUJLYm7ITEFEhmNvIQX5cjHqMbnX8ijyfChUqYP369Zg7dy7s7e0BACkpKVi3bh0qVqxYpHmnp6fDxsamyBlLet4ycGRBSeLMsyxoMwU+2puCwHlPYP/JYwTNf4KPf0s1OBLmkK3JUE19bPCv/f8l6i9PzRAY+H0ynGc+RshXCdhz3fD03J8fTMUbPyeX2DIRGaOEkYPnuWCi3Sfr1q2LChUqYMuWLfppW7ZsQcWKFVGnTh39tICAAHz55ZcGt61duzamTJmi/12lUmHx4sXo0qULHB0d8cknnwAAPv30U3h7e6NMmTIYPnw4UlJyjtAuW7YMVatWhZ2dHUJDQw3OBxQZGQmVSoUNGzagRYsWsLOzQ0REhEmWv7TgyIKSmOnIwqyDaVh8LB2ru9mhmpcax+5qMXRbMlzsgDcbavTXa19ZjZVd7fW/a9Qq/f+/OZ6O43e1ODzcETuuZqDf5mTcn+AElUqFG7GZWHoiHcdGOpbochFlUeLIwfNERic+/0r5NGzYMKxcuRL9+/cHAKxYsQJDhw7F/v37CzyvKVOm4NNPP8WXX34Ja2trbNy4EVOmTMHChQvRtGlTrF27FvPnz0elSpX0t4mIiMCkSZOwYMEC1KlTBydPnsSIESPg6OiIwYMH66/3/vvvY/bs2ahTpw7s7OyKvNylCcuCkpjpyMKhf7ToWsUaHUN0Q3YBrlb49lw6jtzJNLieRq2Cj1Pug2EXH2nRpYo1qnmpUcnNCu/sTsWjJAFPRxXGbE/GrDYaOGtUud6WyNQssRxkdzcuxWR7RAwYMAATJ07EzZu6v3EHDx7E+vXrC1UW+vXrh6FDh+p/79u3L4YPH47hw4cDAKZPn449e/YYjC5MnjwZs2fPRvfu3QEAgYGBuHDhAr7++muDsvDWW2/pr6M0LAtKYqYjC40rqPHN8TT8Ha1FiIcap+9pceCWFnPaGjbz/ZEZ8Pr8CdzsVWgVoMb0Vhp4OOj+ENXyVmPtmXQkpwvsvJYBXycVyjqoEHEmHXbWKrxcVTnrDqn0YTnISZspcCcuGf4eRR/R8/T0RMeOHbFq1SoIIdCxY0eULVu2UPOqV6+ewe8XL17MsQFlo0aNsG/fPgBAYmIirl27huHDh2PEiBH662RkZOQ4UnD2eSsJy4KSxBXhMNESvd/UFo9TBUIXJEJtBWgzgU9aadC/5tMP+PaVrdG9qjUCXa1wLTYTH/yaig4RSTg83BFqKxWG1bHBmftahC1KQFkHFTb2skdsCjBpfwr2D3bEh3tTsP5cOoLcrbCiiz38nLm5DhUey0H+3IpJMklZAHSrIsaOHQsAWLhwYY7L83vGX0fHguVJSEgAACxduhQNGzY0uEytNnzOCzpvc8KyoBTJsUBqvOwUhbLxfAYizqZjXQ97VPO0wql7Wry1MxXlyqgwuLYtAKBv9afFoYa3GjW91Qian4D9kVq0rmQNG7UKCzvaG8x36LZkvNnAFifvabH1UgZOj3bCZwdT8eYvKdjc26FEl5HMG8tB4dyONd1Gxe3bt0daWhpUKhXatWuX43JPT09ERUXpf3/8+DFu3Ljx3PlWrVoVf/31FwYNGqSf9ueff+r/7+3tjXLlyuH69ev6bSYsEcuCUpjpKggAeGd3Ct5votEXghreatyMF5h5IE1fFrKr5GaFsg4qXI3JROtKOS/fdyMD5x9osayzHd7ZnYr/BFvD0VaF3tVssGBVUnEuDikAy4FpRMWZriyo1WpcvHhR///sWrVqhVWrVqFz585wdXXFpEmTcr1eduPGjcOQIUNQr149NGnSBBERETh//rzBBo5Tp07Fm2++CRcXF7Rv3x6pqak4duwYYmNjMX78eJMtY2nGsqAUZrpxIwAkpQNW2bY9VKuATCOnOLv9OBPRSQK+ZXJutJiSIfD6zymI6G4PtZUK2kwga3QyPVO3LpXoWcVRDuL/3IS431ajTHgXuLcZCQCI+XUpEs/9CpWNHVxbDIZTtRf110+8dACJ536FV8/JRbrf0uRuvGkPEufs7JznZRMnTsSNGzfQqVMnuLi44OOPP87XyEKfPn1w7do1vPvuu0hJSUGPHj0wZswY7Ny5U3+dV199FQ4ODvj888/xzjvvwNHRETVq1MBbb71lisUyCzzrpFIcnA/s/kh2ikIZsjUZe65n4OtOul0nT0ZpMfKnFAyrbYNZL9khIU1g6v5U9Aizho+TFa7FZOLdPSl4kgqcHeMIjbVhYfjfrylI1QJf/LuB5Mbz6Xhndwp+fMUB8/9KQ1SCwPZ+XA1hyYr7rIypUX/j4bZZsLK1h13FmnBvMxJJV/9C9C9fwavHZGTE3kX0jnnwG7MSagcXZKYmImr12/DuOx3Wzl4myyFbk8oeiHj1BdkxyAQ4sqAUZjyy8FUHO3y0LxWv/ZyCB4kC5cqoMCrcBpNa6I6xoFYBZx5osfp0OuJSdJe3DbLGxy9qchSFcw+02HghA6dGPd3QqGeYNfZHWqPZykRU8bDCuh4sCpamJFcrZKYl49GPX8Cj/RuIP7RePz09+h/YVagBjW8wNL7BiPl1KTLi70Pt4ILYfStRps5/FFUUAOCeiUcWSB6WBaWIvy07QaGV0ajwZXs7fNk+94OY2NuosHNA/rYyru6lxpU3nAymWalUWNTRHouybQBJyiVzm4OY3YthH1Qf9gG1DcqCrWcgEk7thDYlARlx9yAyUmHtVg4pt88j7f41uLcdUyL5SlJcUs69Ecg8sSwoRYppDq1KZI5KywaJiRd+Q9q9a/AdPDfHZfaVwuFYrSXurX4bKmtblO34NqxsNIjZuQgeHd/Gk5M/48mJn6C2d4Z7u7Gw9fQv8fym9iQl4/lXIrPAsqAUaQmyExCVmNJSDp6V8fghYn5dCu8+H0NlnftePK5N+8O16dPd7+IOrINdQG2orNSIP7wB5YYtRPLVI4jePge+Q+aVVPRik6bNREq6FnY23JPE3LEsKEU6dwck5SqN5SC7tHtXkZkUh6hV455OFJlI/ec8npz4CRUnfA+V1dPM6dH/IPHCPvgOmY+EM7thV7461A4ucAhthugd85CZmgQrjflvX/M4OZ1lQQFYFpQijWWBlMMcykF2dv614DtsgcG06J/nwcajPJwb9jAoCkIIRO9cCLdWr8LK1h4QmRCZ/w7ZZ/0UhudGMVePU9Lh5ayskypZIpYFpUg33RneiEqaOZaD7Kw0DrD1DDCYprLRwMquTI7pCad3Qm3vDIfKusMHa/yqIu7AOqTeuYTk68dh41ERVnaGG+qaq/hkbregBCwLSsGRBTIjSigHhaVNjEX84Y3wGfC5fpqmXBU4N3gZD76bCisHF5Tt+LbEhKb1JIV7RCgBD8qkBNp04OPCnYGNqCQU90GQqPSa/0oddKlVTnYMKiKOLChBGldBUOliySMHZIgjC8rAsqAELAskGcsB5SVDy8FrJWBZUALuNkkljOWA8osnblMGlgUl4MgCFTOWAyqsTG4WpwgsC0rAkQUyMZYDMhWWBWVgWSAiA08c3PBB1ZcQmR4D4CrgdhWVK8hORebK0b0fgCDZMaiIWBaUwMb8DwlLpUeZpFh8em4HPqzWFHtiz8uOQ2YuQ8Xz1iiBlewAZAIsC2RijqlPMPfEDrxVphrUKq5+oMKztuJ3UiVgWVACG3vZCUihhp/ZgcXCE662LrKjkJmyUvFjRgn4LCqBraPsBKRgjW4cwYYH8QgrEyA7CpkhjkwpA8uCEnBkgYpZudhbWHPhCLq51ZAdhcwMV0MoA8uCEtjYA1DJTkEKp8lIwccntuMjx1DYWNnIjkNmgq8VZWBZUApu5EglpPe5XViRVgZedh6yo5AZcLNzkx2BTIBlQSm4KoJKUO1/TmHDP3cQ7hIsOwqVciwLysCyoBQcWaASVjbhAZad+Q0D3GrKjkKlmAdHoBSBZUEpbFkWqORZZ2bgvRM/4VNNEOzVdrLjUCnEkQVlYFlQCq6GIIk6XtqHtQlqlHfwkR2FShFHG0do1BrZMcgEWBaUQuMsOwFZuCr3LmL99b/R1DVUdhQqJdw0HFVQCpYFpSjjKzsBEVyS47Dw1B6MdqkBFXfntXju9u6yI5CJsCwohYuf7AREAAArkYnXT23HfHV5lLFxkh2HJHLXsCwoBcuCUjiXk52AyEDLqwfxbUwKKjvx/NaWiiMLysGyoBTOHFmg0sf/0XVEXD6F9m7VZEchCbjNgnKwLCgFywKVUg5pifj8xA5MKBPGkwpZGHc7jiwoBcuCUriUl52AyKjBZ37BN5ll4a5xlR2FSghXQygHy4JSOLgDGhfZKYiMahB5FBvuRaOGcyXZUagEBDgHyI5AJsKyoCTuAbITED2XT9wdrDp3GD14umtFU0GFSi4shUrBsqAk7nxjknmw1aZiyontmOwQAlsrW9lxqBiUcyoHB56zRjFYFpTELVB2AqIC6Xl+D1alOsDbvqzsKGRilV0ry45AJsSyoCQcWSAzVOP2GWy8eQsNXEJkRyETCnINkh2BTMhadgAyIQ++Ock8uSc+wjen92Fu7fZYHXdWdpw8JV5OxKOfHyH5ZjIy4jJQ8Y2KcA5/el6W+GPxiNkXg5TIFGgTtQiaGgR7f8OTvEV9G4W4A3FQaVTw6ekD18auT29/JB5xB+Pg/7Z/SS1SseHIgrJwZEFJvKsDKj6lZJ7UQosJJ7fjc9tA2FuXzrOoZqZmwq6iHcoNzP2IqZmpmXAMcYR3b+9cL3988jHiD8cjYEIAfHr74M7KO8h4kgEA0CZpcX/zffgOUsZ5XjiyoCz8ZFESO2fAI1h2CqIiaX/5N6x7DPg7lr5DmJepWQbePbwNRhOe5dbEDV5dveAUlvs5MVKjUuEY6gj7QHu4vuAKK3srpD1MAwDc23gP7q3cYeth/ht8WqmsuCeEwrAsKI1fuOwEREVW+f5lfHv1Ilq6VZUdxaTsKtghOTIZ2kQtkiOTIdIENN4aJP6diJSbKfB4yUN2RJPwc/KDnbWd7BhkQiwLSuNXV3YCIpMokxKP+Sd24XXn6rBSyOq1MjXKwKWRC65NvYbby26j/IjyUGlUuLvmLsoNLoeYvTH4+/2/cX36daTcSZEdt9C4vYLycANHpeHIAimICgKjT/+MakGN8L7NYzxOeyI7UpF5v+wN75efbtPwYOsDOIU5QaVW4eEPD1F5emU8Of0Et7+5jcpTzfNDl2VBeZRR1+kpnxqAWiM7BZFJNbt2GOsfJSDEqaLsKCaVejcVcYfj4NXdC4mXEuFQxQHWztZwaeCClJsp0CZrZUcsFG7cqDwsC0qjtgF8a8pOQWRyFaJv4v8un0BHt+qyo5iEEAJ3Vt+BT18fqO3UEJkCQit0l2XofiJTYsAiqO1VW3YEMjGWBSXiqghSKPu0JHx64me851QV1qqSX4uqTdEi+WYykm8mAwDSHqUh+WYy0qJ1ezRkJGQg+WYyUu+m6i6/p7s8PS49x7xif4uFdRlrONfR7VnhEOyAxIuJSLqahEe7HkFTTgO1o/md0tvPyQ9+Tn6yY5CJcZsFJWJZIIUbcHYnQv3DMcE+A9GpsSV2v8k3khE5K1L/+71v7wEAXJu4ovyI8nhy8gnuLL+jv/yfxf8AADy7ehpsp5ARn4GHPz5EpQ+f7l7oUMkBZduXxc25N2HtbA2/Eeb5gdvAp4HsCFQMVEIIITsEmVj0NeAr7hVByvfAxRfjA0Jx+vE12VHoXzOazkDnoM6yY5CJcTWEEnkEAXauslMQFTuv+CisPHcQfXi661KDIwvKxLKgVFwVQRbCRpuGD09sx8f2IdBwTyCpApwD4O2Y+6GuybyxLChV0IuyExCVqG4X9mBNki3K2XvJjmKx6vvUlx2BignLglKFdJCdgKjEhd09jw2R1/GCaxXZUSxSA1+uglAqlgWlKlsZ8OBR1MjyuCbFYMmpvRjmwu0YSpIKKtT35siCUrEsKFlIe9kJiKRQCy3ePrUdc2384WjtIDuORQhyDYKHvTJOhEU5sSwoWRWuiiDL1ubvP7AuXosAR/M8ZoE54V4QysayoGQVXuAulGTxKj24gvVXzqG1W5jsKIrG7RWUjWVBydTWQPBLslMQSeeY+gRzT+zEOOdqijnddWlip7ZDI99GsmNQMeK7Rum43QIRAN3prl89vQOL4QNXWxfZcRSlWflmcLDhtiFKxrKgdJXbAFY8BQhRlsbX/8T6h/GoWsZfdhTFaOvfVnYEKmYsC0pn7wpU5PAg0bP8Ym5hzcVj6MLDRBeZndoOzcs3lx2DihnLgiXgXhFEOdilJ+OTE9vxP8dQWHP0rdCa+DXhKggLwLJgCVgWiPLU99wurExzgaedu+woZqmoqyCGDBkClUoFlUoFGxsbBAYG4t1330VKSoqJEpIpsCxYAvdKXBVBZETtf05i4+0o1HXhUU8LwsHaAS9WLPp5aNq3b4+oqChcv34dc+fOxddff43JkyebICGZCsuCpag7WHYColKt7JP7WHbmD/Rzqyk7itlo498G9tb2RZ6PRqOBj48PKlSogG7duqFNmzbYvXs3ACA6OhqvvPIK/Pz84ODggBo1auDbb781uH3Lli0xduxYjB07Fi4uLihbtiw++ugjCCH010lNTcWECRPg5+cHR0dHNGzYEPv37y9ydkvBsmApqnUD7Li7GJExNpnpmHjiJ8ywqww7nu76uTpV6mTyeZ47dw6HDh2Cra0tACAlJQXh4eHYvn07zp07h5EjR2LgwIE4cuSIwe1Wr14Na2trHDlyBPPmzcOcOXOwbNky/eVjx47F4cOHsX79epw5cwa9evVC+/btceXKFZMvgxKpxLPVi5Rt+wTg6FLZKYjMwiXfMLzl7og7SfdlRymVvBy8sLvn7iIf5GrIkCH4v//7P9jZ2SEjIwOpqamwsrLCxo0b0aNHj1xv06lTJ4SGhuKLL74AoBtZePDgAc6fPw+VSgUAeP/99/HDDz/gwoULuHXrFipVqoRbt26hXLly+vm0adMGDRo0wIwZM4q0DJaAmwBbkvDBLAtE+RQadQEb4lzxXmgDHIy7JDtOqdMxsKPJjob54osvYvHixUhMTMTcuXNhbW2tLwparRYzZszAxo0bcefOHaSlpSE1NRUODoZ7YLzwwgv6ogAAjRo1wuzZs6HVanH27FlotVqEhIQY3CY1NRUeHjz5VX6wLFgSnxpAubrA3ROykxCZBZfkOCw6tQcLanXAsvhzEOBAbJYuQV1MNi9HR0dUrqzbuHTFihWoVasWli9fjuHDh+Pzzz/HvHnz8OWXX6JGjRpwdHTEW2+9hbS0tHzPPyEhAWq1GsePH4darTa4zMnJyWTLoWQsC5YmfDDLAlEBWIlMvHlqO2oEN8UHqmgkpCfKjiTdC74voLJb8ew5YmVlhQ8++ADjx49Hv379cPDgQXTt2hUDBgwAAGRmZuLvv/9GWJjhicH++usvg9///PNPBAcHQ61Wo06dOtBqtXjw4AGaNWtWLLmVjhs4WprqPQFbNmmignrxygF8G5uGIKfysqNINzBsYLHOv1evXlCr1Vi4cCGCg4Oxe/duHDp0CBcvXsSoUaNw/37O7Uhu3bqF8ePH4/Lly/j222/x1VdfYdy4cQCAkJAQ9O/fH4MGDcKWLVtw48YNHDlyBDNnzsT27duLdVmUgiMLlkbjBFTvDpxYIzsJkdkJeHgN6x7fx0fVmmFX7HnZcaSo5FIJzfyK99u5tbU1xo4di88++wwnT57E9evX0a5dOzg4OGDkyJHo1q0b4uPjDW4zaNAgJCcno0GDBlCr1Rg3bhxGjhypv3zlypWYPn06/vvf/+LOnTsoW7YsXnjhBXTqZPo9OpSIe0NYotvHgWWtZKcgMmsra7bHvITL0Aqt7CglalKjSegV0kt2DAMtW7ZE7dq18eWXX8qOolhcDWGJyocD3tVlpyAya0PP/IKvRVm4WdDprt00buhcqbPsGCQBy4KlajBCdgIis9fwxlFseBCHas6BsqOUiF5VesHO2k52DJKAqyEsVUYaMK8m8CRKdhIis5em1mB6rTb4Pvas7CjFxsbKBrt67kJZ+7Kyo5AEHFmwVNa2QKOxslMQKYKtNhXTTmzHRw5VYGNlIztOsegQ2IFFwYKxLFiyekMBe56Wl8hUep/fjVWpTvBW4IfqoLBBsiOQRCwLlszWEXhhjOwURIpS8/ZpbLj1D+q5BMuOYjINfRqiinsV2TFIIpYFS9dgJKBxlp2CSFE8Eh5i6ZnfMNC1huwoJjGoGkcVLB3LgqWzdwUajpadgkhxrDMz8O7J7fhMUwn2avPdg6C2Z200L99cdgySjGWBgMZjATtX2SmIFKnDpf34vydWqOjgKztKofy33n9lR6BSgGWBADsXXWEgomIRcv8Svr1+Cc1dq8qOUiBtKrZBba/asmNQKcCyQDoNxwAOPK87UXFxTo7HgpO78JpLDaigkh3nuaxV1ngr/C3ZMaiUYFkgHY0T0GSc7BREiqaCwJhT27HAyg9lbEr32V97hPSAv7O/7BhUSrAs0FP1RwDOPP0uUXFrfu0QNkQnIdipouwouXK0ccSYWtytmp5iWaCnbB2A9jNkpyCyCBWiIxFx+QQ6uJW+k7oNqTYEHvZcLUlPsSyQobCuQFBr2SmILIJ9WhI+O/Ez3nEKg7XKWnYcAICnvScGVxssOwaVMiwLlNN/PgfUGtkpiCzGoLO/4ButO9w1brKj4LXar8He2l52DCplWBYoJ48goMmbslMQWZT6N49hY9RD1HSuJC1DkEsQXq78srT7p9KLZYFy1+y/gGvp3PiKSKm84+9i1blD6OUm5zDR/633X6it1FLum0o3lgXKnY090OEz2SmILI6NNg2TTmzHNPsQ2FrZltj9dqzUEc3KNyux+yPzohJCCNkhqBRb1xf4e4fsFEQW6bxfDbztYouo5IfFej8edh7Y1m0bXDQuxXo/ZL44skDGdZgFcGMnIimq3TmLDTcj0dA1pFjv58MXPmRRIKNYFsg4N3/d9gtEJIVbYjS+PrUPQ4vpdNftAtqhjX+bYpk3KQdXQ9DzZaQCixoBMddkJyGyaLuqNMdHmQ+QlJFkkvm527lja9etcLOTv8smlW4cWaDns9YA3RYDKm4lTSRT28u/Y118JgIcy5lkfhMbTmRRoHxhWaD8qdgQaPm+7BREFi/owd/49uoFvOgWVqT5vOT/EtoHtDdRKlI6roag/MvMBFZ3Bm4ekJ2EyOIJqLC0VgcsfHIBmSKzQLd11bhia9etPP8D5RtHFij/rKyAHksBe3fZSYgsngoCI0//jEXwgYutc4Fu+36D91kUqEBYFqhgnMsBXRfITkFE/2py/U+sf/gYoWX883X9Fyu8iI6VOhZzKlIalgUquNCOQL3hslMQ0b/Kx9zC2ovH0Ok5p7v2cfTBtMbTSigVKQm3WaDCSU8Blr4IPLggOwkRPSOiRjt8kXQFGZkZBtOtrayxuv1q1PSsKSkZmTOOLFDh2NgBPVfw6I5EpUz/szuxIs0FnnaG2xZNqDeBRYEKjWWBCs+rKtDuE9kpiCibOv+cxIY791DHpTIA3VEa+1ftLzkVmTOuhqCiW98fuPST7BRElE26lQ2Wv/AKBr74KRxtHGXHITPGskBFlxIPLG8HPLwoOwkRPUvjAoz4FSgbLDsJmTmuhqCis3MB+m8CnLxlJyGiLKp/j4vCokAmwLJApuFaAXhlPWDjIDsJEQFAqw+BkHayU5BCsCyQ6fjVBXos132jISJ5qvfgqeXJpPhXnUwr9D9AuxmyUxBZLt/aQBceZZVMi2WBTO+FMUCDUbJTEFmeslWAAVsAW64OJNNiWaDi0X4mEMLT3xKVGJeKwMDvAUeeIIpMj2WBioeVWneER99aspMQKZ+jFzBoK+DiJzsJKRTLAhUfW0eg30bAubzsJETKZeeiG1HwCJKdhBSMZYGKVxkf3TEY7N2ff10iKhgbB6DfJsDH+NkmiYqKZYGKn3cYMGgbYO8mOwmRcqhtgT7/B1RsKDsJWQCWBSoZvjV1hcHOVXYSIvOnUgM9lgGVW8tOQhaCZYFKjm+tfwuDi+wkRGZMBXSeB4R1lR2ELAjLApWscrWBgVtZGIgKq90MoO5A2SnIwvCskyRH1Glg7ctAUrTsJETmwcoa6DwfqNNfdhKyQCwLJM/Dy8CarsCTKNlJiEo3Gweg1yqeGIqkYVkguWJuAGu6AHG3ZCchKp3s3XS7R1aoLzsJWTCWBZIv/o6uMERflZ2EqHRxLg8M3AJ4VpGdhCwcywKVDonRwIYBwK1DspMQlQ6eVXVFwbmc7CRELAtUimSkAdvHAyfXyk5CJFfFRsAr3/JAZlRqsCxQ6XN4EbDrQ0BoZSchKnlVOupOwmZjJzsJkR7LApVOV/cAm4YBqfGykxCVnLqDgU5zdWdtJSpFWBao9Hp0BVjXB4i5JjsJUfFS2+oOttRghOwkRLliWaDSLTkW2DQEuL5fdhKi4uFSEei9CvALl52EKE8sC1T6aTOAnROBI9/ITkJkWsHtgJeXAA48hTuVbiwLZD6OrQB+fhfITJedhKhoVGqg1YdA07cBlUp2GqLnYlkg83LnOLBlJA/gRObLyVu3t0NAU9lJiPKNZYHMT1oSsPsj4Ogy2UmICiagGdBjOVDGW3YSogJhWSDzdWUPsO11IOGe7CREz6ECmo0HXvwfd4sks8SyQOYtKQb46S3gwjbZSYhy5+QDdPkKCGkrOwlRobEskDKcXq/b+JEHcaLSpFY/oP0MHraZzB7LAilH3D/A1jFA5B+yk5Clcy4PdJ4HBLeRnYTIJFgWSFmEAA4vAH79GNCmyk5DFkcFhA8B2n4MaMrIDkNkMiwLpEyPruoO5HRll+wkZCm8woCOcwD/RrKTEJkcywIp25XdwM4PgEd/y05CSmXjALR4F2g0FlDbyE5DVCxYFkj5tBm6Q0X/9imQwg0gyYRC2gP/+RxwrSg7CVGxYlkgy5EYDeybDhxfDQit7DRkznxqAK0mcXdIshgsC2R57p0Dfnmfe01QwbkHAa3+B1TrznM6kEVhWSDLdWEbsOsjIO6m7CRU2jmX122XULs/oLaWnYaoxLEskGXLSNWdY+LgfB42mnJyKKs7THP9VwFrjew0RNKwLBABQHoKcHItcOBL4PFt2WlINo0z0PgN4IXXAI2T7DRE0rEsED1Lmw6cWgccmAPERspOQyXN2h5oMAJo+jbg4C47DVGpwbJAlJtMLXBhK3DoK+DuSdlpqLi5+gP1hwN1BrIkEOWCZYHoeSIP6ErD3zsB8O2iHCog6EWgwUgguB1gZSU7EFGpxbJAlF8P/wb+Wgyc28yDO5kzjTNQux9QfwRQtrLsNERmgWWBqKDSU4DL24FT3wLX9vIAT+bCM1S3PULNvtxokaiAWBaIiuLJPeDMRuD0t8CDC7LTUHbWdkBwW92uj5VayE5DZLZYFohM5e4pXWk4uwlIipadxnLZOADBLwFVu+jO3cBRBKIiY1kgMjVtuu7U2KfW6c56qU2VnUj5bMsAIe2AsC5A5ZcAWwfZiYgUhWWBqDilJ+v2prj6K3DtV54q25TsXIAq/wHCugJBrXiERaJixLJAVJLibj0tDtd/B1K5V0WBuAcBgc2B0I5ApZaA2kZ2IiKLwLJAJIs2A7h9FLi6R1ce7p4Cj+OQjXslIKApENBM99O5nOxERBaJZYGotEiMBm4fAe6dBaJOA/fO6EYiLIW1HeBbGyhfD6jQACjfAHD2lZ2KiMCyQFS6JcfpysO9M0DUGd3/H10GMjNkJys8lZXulM8eQYBHZaBsCFA+HPCpydUKRKUUywKRuUlP0R3T4d5Z4OFlIP6ff//dBhIfyk73lIOHrgx4VH5aDDwq67Y7sLGTnY6ICoBlgUhJ0lOAx3eeFoeEB0DiAyDhoe5n4iNAm6bbvTMzXbfdhDbt6f8z059epqcCNGUAO1fdHgh2LoD9M//PPt3RU1cO7N2kPAREZHosC0SUu6zyoNbwJEtEFo5lgYiIiIzi1wUiIiIyimWBiIiIjGJZICIiIqNYFoiIiMgolgUiIiIyimWBiIiIjGJZICIiIqNYFoiIiMgolgUiIiIyimWBiIiIjGJZICIiIqNYFoiIiMgolgUiIiIyimWBiIiIjGJZICIiIqNYFoiIiMgolgUiIiIyimWBiIiIjGJZICIiIqNYFoiIiMgolgUiIiIyimWBiIiIjGJZICIiIqNYFoiIiMgolgUiIiIyimWBiIiIjGJZICIiIqNYFoiIiMgolgUiIiIyimWBiIiIjGJZICIiIqNYFoiIiMgolgUiIiIyimWBiIiIjGJZICIiIqNYFoiIiMgolgUiIiIyimWBiIiIjGJZICIiIqNYFoiIiMgolgUiIiIyimWBiIiIjGJZICIiIqNYFoiIiMgolgUiIiIy6v8BMN5cfNgWJEUAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "Murder     389.40\n",
       "Assault   8538.00\n",
       "Rape      1061.60\n",
       "dtype: float64"
      ]
     },
     "execution_count": 297,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# number of crimes committed/arrests for those crimes in all 50 states in 1973\n",
    "no_crimes=df.drop(['City','UrbanPop'],axis=1).sum()\n",
    "# pie chart for % arrests for specific crimes\n",
    "plt.pie(no_crimes, labels = df.drop(['City','UrbanPop'],axis=1).columns, autopct='%.0f%%')\n",
    "plt.title(\"Percentage of arrests for different crimes in the USA in 1973\")\n",
    "plt.show()\n",
    "plt.close()\n",
    "no_crimes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 298,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\super\\AppData\\Local\\Temp\\ipykernel_6420\\2920169007.py:1: FutureWarning: The default value of numeric_only in DataFrame.corr is deprecated. In a future version, it will default to False. Select only valid columns or specify the value of numeric_only to silence this warning.\n",
      "  corr_df = df.corr()\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot: >"
      ]
     },
     "execution_count": 298,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "corr_df = df.corr()\n",
    "labels =corr_df.columns\n",
    "\n",
    "correlations = corr_df.corr()\n",
    "mask_ut=np.triu(np.ones(corr_df.shape)).astype(np.bool_)\n",
    "sns.heatmap(corr_df, vmin=-1, vmax=1, annot=True,fmt='.2g', mask=mask_ut, cmap=\"coolwarm\")"
   ]
  }
 ],
 "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": "3.11.0"
  },
  "orig_nbformat": 4,
  "vscode": {
   "interpreter": {
    "hash": "fd9e38af040559ad764514e0db59d6a267742561531a8adad5281cf94d911280"
   }
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}