DataScienceEP2.ipynb

{
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   "cell_type": "markdown",
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   "source": [
    "## Indentification of all the members of the group.\n",
    "Name: Kaique Kazuyoshi Komata - 10297741\n",
    "\n",
    "Name: Ricardo Hideki Hangai Kojo - 10295429\n",
    "\n",
    "Name: Yurick Yussuke Honda - 10258984"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Name of the dataset\n",
    "Dataset: Chicago Crime"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Abstract\n",
    "Abstract of the dataset (describe the dataset with your own words): O dataset apresenta dados reais sobre os crimes reportados em Chicago. Ele oferece diversas informações sobre a localização onde o crime ocorreu e descrições da ocorrência. O tipo de crime é definido primariamente pelos identificadores Illinois Uniform Crime Reporting (IUCR) code e National Incident-Based Reporting System (NIBRS). O fato do dataset ser construído pelo Departamento de Polícia de Chicago, não garante a precisão dos dados. De qualquer forma, o objetivo do dataset é estudar potencias causas de crimes em Chicago, principalmente porque aproximadamente 10 pessoas, em média, são baleadas em Chicago por dia, de acordo com o próprio Departamento de Polícia de Chicago."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Questions to the dataset\n",
    "Question 1: Qual o percentual de crimes que resultaram em prisão?\n",
    "\n",
    "Question 2: Em época de Natal a média de crimes reportados em Chicago é diferente do que em outras épocas do ano?\n",
    "\n",
    "Question 3: Qual é o crime mais reportado?\n",
    "\n",
    "Question 4: Qual o ano com maior número de crimes reportados?\n",
    "\n",
    "Question 5: Os crimes que recebem atualização possuem alguma relação com o local do incidente?\n",
    "\n",
    "Question 6: Os crimes com agressão doméstica possuem alguma relação com o local do incidente?\n",
    "\n",
    "Question 7: Qual o local que teve o maior número de crimes reportados?\n",
    "\n",
    "Question 8: Qual o distrito policial com o maior número de incidentes?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## EDA\n",
    "Present your EDA strategy: O foco é responder as perguntas trabalhadas por cada integrante da equipe. Quando integrantes analisam uma mesma pergunta, é possível comparar as respostas, com seus diversos pontos de vista, e o modo como a análise exploratória foi programada. Quando integrantes analisam perguntas diferentes, é possível obter cada vez mais informações sobre o dataset. A análise exploratória de dados será feita por meio da análise de dados estatísticos e geração de gráficos para auxiliar na visualização da informação. Todas as questões serão divididas pela resolução de cada integrante, quando mais de um integrante a resolveu, e então serão feitas as conclusões.\n",
    "\n",
    "É possível fazer o download do arquivo *csv* usado na análise em https://www.kaggle.com/currie32/crimes-in-chicago. Nessa análise foi utilizada a partição entre os anos de 2012 e 2017."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "from statistics import mean, median\n",
    "import seaborn as sns\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import csv\n",
    "import math"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
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     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 1456714 entries, 0 to 1456713\n",
      "Data columns (total 23 columns):\n",
      "Unnamed: 0              1456714 non-null int64\n",
      "ID                      1456714 non-null int64\n",
      "Case Number             1456713 non-null object\n",
      "Date                    1456714 non-null object\n",
      "Block                   1456714 non-null object\n",
      "IUCR                    1456714 non-null object\n",
      "Primary Type            1456714 non-null object\n",
      "Description             1456714 non-null object\n",
      "Location Description    1455056 non-null object\n",
      "Arrest                  1456714 non-null bool\n",
      "Domestic                1456714 non-null bool\n",
      "Beat                    1456714 non-null int64\n",
      "District                1456713 non-null float64\n",
      "Ward                    1456700 non-null float64\n",
      "Community Area          1456674 non-null float64\n",
      "FBI Code                1456714 non-null object\n",
      "X Coordinate            1419631 non-null float64\n",
      "Y Coordinate            1419631 non-null float64\n",
      "Year                    1456714 non-null int64\n",
      "Updated On              1456714 non-null object\n",
      "Latitude                1419631 non-null float64\n",
      "Longitude               1419631 non-null float64\n",
      "Location                1419631 non-null object\n",
      "dtypes: bool(2), float64(7), int64(4), object(10)\n",
      "memory usage: 236.2+ MB\n"
     ]
    }
   ],
   "source": [
    "crimes = pd.read_csv('Chicago_Crimes_2012_to_2017.csv')\n",
    "crimes.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
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       "      <td>20.0</td>\n",
       "      <td>42.0</td>\n",
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       "      <td>053XX W CHICAGO AVE</td>\n",
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       "      <td>PUBLIC PEACE VIOLATION</td>\n",
       "      <td>RECKLESS CONDUCT</td>\n",
       "      <td>STREET</td>\n",
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       "      <td>...</td>\n",
       "      <td>37.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>24</td>\n",
       "      <td>1140789.0</td>\n",
       "      <td>1904819.0</td>\n",
       "      <td>2016</td>\n",
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       "      <td>41.894908</td>\n",
       "      <td>-87.758372</td>\n",
       "      <td>(41.894908283, -87.758371958)</td>\n",
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       "      <td>049XX W FULTON ST</td>\n",
       "      <td>0460</td>\n",
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       "      <td>SIMPLE</td>\n",
       "      <td>SIDEWALK</td>\n",
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       "      <td>...</td>\n",
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       "      <td>25.0</td>\n",
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       "      <td>1143223.0</td>\n",
       "      <td>1901475.0</td>\n",
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       "      <td>05/10/2016 03:56:50 PM</td>\n",
       "      <td>41.885687</td>\n",
       "      <td>-87.749516</td>\n",
       "      <td>(41.885686845, -87.749515983)</td>\n",
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       "      <td>4</td>\n",
       "      <td>911</td>\n",
       "      <td>10508699</td>\n",
       "      <td>HZ250455</td>\n",
       "      <td>05/03/2016 10:00:00 PM</td>\n",
       "      <td>003XX N LOTUS AVE</td>\n",
       "      <td>0820</td>\n",
       "      <td>THEFT</td>\n",
       "      <td>$500 AND UNDER</td>\n",
       "      <td>RESIDENCE</td>\n",
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       "      <td>06</td>\n",
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       "      <td>1901675.0</td>\n",
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       "      <td>05/10/2016 03:56:50 PM</td>\n",
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       "   Unnamed: 0        ID Case Number                    Date  \\\n",
       "0           3  10508693    HZ250496  05/03/2016 11:40:00 PM   \n",
       "1          89  10508695    HZ250409  05/03/2016 09:40:00 PM   \n",
       "2         197  10508697    HZ250503  05/03/2016 11:31:00 PM   \n",
       "3         673  10508698    HZ250424  05/03/2016 10:10:00 PM   \n",
       "4         911  10508699    HZ250455  05/03/2016 10:00:00 PM   \n",
       "\n",
       "                 Block  IUCR            Primary Type              Description  \\\n",
       "0   013XX S SAWYER AVE  0486                 BATTERY  DOMESTIC BATTERY SIMPLE   \n",
       "1   061XX S DREXEL AVE  0486                 BATTERY  DOMESTIC BATTERY SIMPLE   \n",
       "2  053XX W CHICAGO AVE  0470  PUBLIC PEACE VIOLATION         RECKLESS CONDUCT   \n",
       "3    049XX W FULTON ST  0460                 BATTERY                   SIMPLE   \n",
       "4    003XX N LOTUS AVE  0820                   THEFT           $500 AND UNDER   \n",
       "\n",
       "  Location Description  Arrest  ...  Ward  Community Area  FBI Code  \\\n",
       "0            APARTMENT    True  ...  24.0            29.0       08B   \n",
       "1            RESIDENCE   False  ...  20.0            42.0       08B   \n",
       "2               STREET   False  ...  37.0            25.0        24   \n",
       "3             SIDEWALK   False  ...  28.0            25.0       08B   \n",
       "4            RESIDENCE   False  ...  28.0            25.0        06   \n",
       "\n",
       "   X Coordinate  Y Coordinate  Year              Updated On   Latitude  \\\n",
       "0     1154907.0     1893681.0  2016  05/10/2016 03:56:50 PM  41.864073   \n",
       "1     1183066.0     1864330.0  2016  05/10/2016 03:56:50 PM  41.782922   \n",
       "2     1140789.0     1904819.0  2016  05/10/2016 03:56:50 PM  41.894908   \n",
       "3     1143223.0     1901475.0  2016  05/10/2016 03:56:50 PM  41.885687   \n",
       "4     1139890.0     1901675.0  2016  05/10/2016 03:56:50 PM  41.886297   \n",
       "\n",
       "   Longitude                       Location  \n",
       "0 -87.706819  (41.864073157, -87.706818608)  \n",
       "1 -87.604363   (41.782921527, -87.60436317)  \n",
       "2 -87.758372  (41.894908283, -87.758371958)  \n",
       "3 -87.749516  (41.885686845, -87.749515983)  \n",
       "4 -87.761751  (41.886297242, -87.761750709)  \n",
       "\n",
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     "execution_count": 6,
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   "source": [
    "crimes.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
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       "      <td>PARKING LOT/GARAGE(NON.RESID.)</td>\n",
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       "      <td>05/10/2016 03:56:50 PM</td>\n",
       "      <td>41.758309</td>\n",
       "      <td>-87.620658</td>\n",
       "      <td>(41.75830866, -87.620658418)</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 23 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         Unnamed: 0        ID Case Number                    Date  \\\n",
       "1456709     6250330  10508679    HZ250507  05/03/2016 11:33:00 PM   \n",
       "1456710     6251089  10508680    HZ250491  05/03/2016 11:30:00 PM   \n",
       "1456711     6251349  10508681    HZ250479  05/03/2016 12:15:00 AM   \n",
       "1456712     6253257  10508690    HZ250370  05/03/2016 09:07:00 PM   \n",
       "1456713     6253474  10508692    HZ250517  05/03/2016 11:38:00 PM   \n",
       "\n",
       "                        Block  IUCR     Primary Type              Description  \\\n",
       "1456709       026XX W 23RD PL  0486          BATTERY  DOMESTIC BATTERY SIMPLE   \n",
       "1456710   073XX S HARVARD AVE  1310  CRIMINAL DAMAGE              TO PROPERTY   \n",
       "1456711       024XX W 63RD ST  041A          BATTERY      AGGRAVATED: HANDGUN   \n",
       "1456712  082XX S EXCHANGE AVE  0486          BATTERY  DOMESTIC BATTERY SIMPLE   \n",
       "1456713       001XX E 75TH ST  5007    OTHER OFFENSE  OTHER WEAPONS VIOLATION   \n",
       "\n",
       "                   Location Description  Arrest  ...  Ward  Community Area  \\\n",
       "1456709                       APARTMENT    True  ...  28.0            30.0   \n",
       "1456710                       APARTMENT    True  ...  17.0            69.0   \n",
       "1456711                        SIDEWALK   False  ...  15.0            66.0   \n",
       "1456712                        SIDEWALK   False  ...   7.0            46.0   \n",
       "1456713  PARKING LOT/GARAGE(NON.RESID.)    True  ...   6.0            69.0   \n",
       "\n",
       "         FBI Code  X Coordinate  Y Coordinate  Year              Updated On  \\\n",
       "1456709       08B     1159105.0     1888300.0  2016  05/10/2016 03:56:50 PM   \n",
       "1456710        14     1175230.0     1856183.0  2016  05/10/2016 03:56:50 PM   \n",
       "1456711       04B     1161027.0     1862810.0  2016  05/10/2016 03:56:50 PM   \n",
       "1456712       08B     1197261.0     1850727.0  2016  05/10/2016 03:56:50 PM   \n",
       "1456713        26     1178696.0     1855324.0  2016  05/10/2016 03:56:50 PM   \n",
       "\n",
       "          Latitude  Longitude                       Location  \n",
       "1456709  41.849222 -87.691556   (41.849222028, -87.69155551)  \n",
       "1456710  41.760744 -87.633335   (41.760743949, -87.63333531)  \n",
       "1456711  41.779235 -87.685207  (41.779234743, -87.685207125)  \n",
       "1456712  41.745252 -87.552773  (41.745251975, -87.552773464)  \n",
       "1456713  41.758309 -87.620658   (41.75830866, -87.620658418)  \n",
       "\n",
       "[5 rows x 23 columns]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "crimes.tail()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Questão 1\n",
    "\n",
    "### Resolução Yurick\n",
    "\n",
    "A resolução consistiu em agrupar os valores da coluna 'Arrest' e contá-los. Depois disso, calcular o percentual de valores 'True', uma vez que esse campo assume apenas valores 'True'/'False'. Verificando que apenas uma pequena quantidade de ocorrências, pouco mais de um quarto do dataset, resultaram em apreensão de algum indivíduo. Por fim, para uma melhor visualização da dimensão da diferença entre os valores, foi plotado um gráfico de barra e outro de setores. A partir deles, a conclusão, dessa vez de forma visual, fica mais clara."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "25.91256760077819"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "series_describe = crimes['Arrest'].value_counts()\n",
    "# Calculate the FALSE values\n",
    "false_values = series_describe.get(False)\n",
    "# Calculate the TRUE values\n",
    "true_values = series_describe.get(True)\n",
    "# Calculate the percentage\n",
    "100.0*true_values/(false_values+true_values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<BarContainer object of 2 artists>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot bar based on calc values\n",
    "plt.bar(['False', 'True'], [false_values, true_values])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "([<matplotlib.patches.Wedge at 0x7fb45e811b10>,\n",
       "  <matplotlib.patches.Wedge at 0x7fb45e81d050>],\n",
       " [Text(-0.755202,0.799794,'False'), Text(0.755202,-0.799794,'True')])"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot bar based on calc values\n",
    "plt.pie([false_values, true_values], labels=['False', 'True'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Resolução Kaique\n",
    "\n",
    "A aproximação tomada em relação à questão foi um pouco diferente da anterior, no modo em que, além de recuperar a porcentagem de crimes que resultaram em prisões, foi também analisado a porcentagem desses crimes que tinham cunho doméstico.<br>\n",
    "Primeiramente foi feito um agrupamento dos registros que contém a coluna \"Arrest\" com o valor True. Após isso, foi feita uma contagem do total de registros que foram agrupados e do total de registros do conjunto de dados para depois relacionar ambos os valores para encontrar a porcentagem. Os registros que possuem o valor da coluna \"Domestic\" como True também foram agrupados e depois mesclados com o grupo de registros anteriormente extraídos para recuperar a porcentagem dos crimes que resultaram em uma prisão que possuem cunho doméstico.<br>\n",
    "O resultado obtido mostra que um quarto dos incidentes registrados resultaram em alguma prisão e que, desses incidentes, mais de um décimo possui cunho doméstico, o que representa relativamente uma grande parte do total."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Porcentagem de crimes que resultaram em prisão: 25.91257%\n"
     ]
    }
   ],
   "source": [
    "total = crimes['ID'].count()\n",
    "arrest = crimes[crimes['Arrest'] == True]\n",
    "total_arrest = arrest['ID'].count()\n",
    "print ('Porcentagem de crimes que resultaram em prisão: {:.5%}'.format(total_arrest/total))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Porcentagem dos crimes com prisão que tem cunho doméstico: 11.31210%\n"
     ]
    }
   ],
   "source": [
    "domestic = crimes[crimes['Domestic'] == True]\n",
    "intersection = pd.merge(domestic, arrest, how='inner', on='ID')\n",
    "total_inter = intersection['ID'].count()\n",
    "print ('Porcentagem dos crimes com prisão que tem cunho doméstico: {:.5%}'.format(total_inter/total_arrest))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Resolução Ricardo\n",
    "\n",
    "De forma simples e direta, a porcentagem de crimes que resultam em prisão é de **25.9126 %**. É possível chegar a isso usando apenas a função de contagem, com a opção de normalizar.\n",
    "\n",
    "Explorando mais a questão, é possível agrupar os valores por ano e verificar como foi a evolução de crimes com/sem prisão durante o intervalo de 2012 à 2017, sendo que os dados de 2017 incluem apenas o início do ano.\n",
    "\n",
    "Por fim, foi criado um gráfico de barras mostrando a quantidade de crimes com/sem prisão por ano. Neste gráfico, é possível verificar que a quantidade absoluta de crimes com prisão diminuiu com o passar dos anos. No entanto, a porcentagem se manteve estável de 2012 a 2015, e caiu drasticamente em 2016."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False    1079242\n",
       "True      377472\n",
       "Name: Arrest, dtype: int64"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "crimes['Arrest'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False    0.740874\n",
       "True     0.259126\n",
       "Name: Arrest, dtype: float64"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "crimes['Arrest'].value_counts(normalize=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Year  Arrest\n",
       "2012  False     245159\n",
       "      True       90511\n",
       "2013  False     220484\n",
       "      True       86219\n",
       "2014  False     195470\n",
       "      True       79057\n",
       "2015  False     193598\n",
       "      True       69397\n",
       "2016  False     215076\n",
       "      True       50386\n",
       "2017  False       9455\n",
       "      True        1902\n",
       "Name: Arrest, dtype: int64"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "crimes.groupby('Year')['Arrest'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Year  Arrest\n",
       "2012  False     0.730357\n",
       "      True      0.269643\n",
       "2013  False     0.718884\n",
       "      True      0.281116\n",
       "2014  False     0.712025\n",
       "      True      0.287975\n",
       "2015  False     0.736128\n",
       "      True      0.263872\n",
       "2016  False     0.810195\n",
       "      True      0.189805\n",
       "2017  False     0.832526\n",
       "      True      0.167474\n",
       "Name: Arrest, dtype: float64"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "crimes.groupby('Year')['Arrest'].value_counts(normalize=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count        6.00000\n",
       "mean     62912.00000\n",
       "std      33126.29794\n",
       "min       1902.00000\n",
       "25%      55138.75000\n",
       "50%      74227.00000\n",
       "75%      84428.50000\n",
       "max      90511.00000\n",
       "Name: Arrest, dtype: float64"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "crimes_with_arrest = crimes.query('Arrest').groupby('Year')\n",
    "crimes_with_arrest[\"Arrest\"].value_counts().describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count         6.000000\n",
       "mean     179873.666667\n",
       "std       85585.866919\n",
       "min        9455.000000\n",
       "25%      194066.000000\n",
       "50%      205273.000000\n",
       "75%      219132.000000\n",
       "max      245159.000000\n",
       "Name: Arrest, dtype: float64"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "crimes_without_arrest = crimes.query('not Arrest').groupby('Year')\n",
    "crimes_without_arrest[\"Arrest\"].value_counts().describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f8cb3919dd0>"
      ]
     },
     "execution_count": 20,
     "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": [
    "arrest_count = crimes_with_arrest[\"Arrest\"].value_counts()\n",
    "no_arrest_count = crimes_without_arrest[\"Arrest\"].value_counts()\n",
    "arrest_df = pd.DataFrame({'Arrest': arrest_count.values, 'No arrest': no_arrest_count.values}, index=[i for i in range(2012, 2018)])\n",
    "arrest_df.plot.bar(figsize=(10,10))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Questão 2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Resolução Yurick\n",
    "\n",
    "O primeiro passo foi converter os valores do campo 'Date' para o time Datetime do Pandas. Depois disso, houve o cálculo da quantidade de ocorrências por mês, para qualquer ano. Essa primeira abordagem, considera que o período de Natal é o mês de dezembro. É possível verificar que no mês de dezembro não há aumento na quantidade de crimes, pelo contrário, possui até menor quantidade de ocorrências do que a média (121392.83333333333). Uma forma de visualizar isso é pelo gráfico de barras, onde é possível comparar melhor o mês de dezembro com os restantes. Além disso, outro recurso que facilita é o boxplot, em que é possível verificar que a quantidade de crimes do mês de dezembro se localiza entre o limite inferior e o primeiro quartil do boxplot.\n",
    "\n",
    "Outra abordagem realizada é considerar que o período de Natal se situa entre os dias 23 e 31 do mês de dezembro, fazendo então a comparação com o mesmo período de outros meses. Nesse último caso, foi plotado um gráfico de setores, onde não é possível verificar nenhum aumento na quantidade de ocorrências no período de Natal."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "121392.83333333333\n",
      "1     123809\n",
      "2      98073\n",
      "3     119010\n",
      "4     118064\n",
      "5     129735\n",
      "6     130575\n",
      "7     135762\n",
      "8     133738\n",
      "9     124067\n",
      "10    123571\n",
      "11    111834\n",
      "12    108476\n",
      "Name: Date, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "crimes[\"Date\"] = pd.to_datetime(crimes[\"Date\"])\n",
    "# Calculate the crimes occurred on each month\n",
    "crimes_by_month = crimes[\"Date\"].dt.month.value_counts()\n",
    "print(crimes_by_month.mean())\n",
    "# Sort the series\n",
    "print(crimes_by_month.sort_index())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<BarContainer object of 12 artists>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot a bar with the number of crimes on each month\n",
    "plt.bar(crimes_by_month.index, crimes_by_month.values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fb4503d5750>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot a boxplot with the number of crimes on each month\n",
    "crimes_by_month.plot.box()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "([<matplotlib.patches.Wedge at 0x7fb457ce0190>,\n",
       "  <matplotlib.patches.Wedge at 0x7fb457ef0810>,\n",
       "  <matplotlib.patches.Wedge at 0x7fb45846cdd0>,\n",
       "  <matplotlib.patches.Wedge at 0x7fb4588f6810>,\n",
       "  <matplotlib.patches.Wedge at 0x7fb458be7550>,\n",
       "  <matplotlib.patches.Wedge at 0x7fb458e03e10>,\n",
       "  <matplotlib.patches.Wedge at 0x7fb4590ed750>,\n",
       "  <matplotlib.patches.Wedge at 0x7fb4595ed550>,\n",
       "  <matplotlib.patches.Wedge at 0x7fb459ae66d0>,\n",
       "  <matplotlib.patches.Wedge at 0x7fb459fe7350>,\n",
       "  <matplotlib.patches.Wedge at 0x7fb459fe71d0>,\n",
       "  <matplotlib.patches.Wedge at 0x7fb45a21dc10>],\n",
       " [Text(-0.932509,0.583462,'Janeiro'),\n",
       "  Text(-1.08011,0.20825,'Fevereiro'),\n",
       "  Text(-1.06007,-0.293673,'Marco'),\n",
       "  Text(-0.788481,-0.767006,'Abril'),\n",
       "  Text(-0.266944,-1.06712,'Maio'),\n",
       "  Text(0.36137,-1.03895,'Junho'),\n",
       "  Text(0.884657,-0.653744,'Julho'),\n",
       "  Text(1.0999,-0.014839,'Agosto'),\n",
       "  Text(0.909201,0.619156,'Setembro'),\n",
       "  Text(0.426777,1.01384,'Outubro'),\n",
       "  Text(-0.159974,1.08831,'Novembro'),\n",
       "  Text(-0.657171,0.882114,'Dezembro')],\n",
       " [Text(-0.508641,0.318252,'4.4%'),\n",
       "  Text(-0.589149,0.113591,'7.3%'),\n",
       "  Text(-0.578222,-0.160185,'7.4%'),\n",
       "  Text(-0.430081,-0.418367,'8.6%'),\n",
       "  Text(-0.145606,-0.582064,'9.0%'),\n",
       "  Text(0.197111,-0.566699,'9.4%'),\n",
       "  Text(0.48254,-0.356588,'9.7%'),\n",
       "  Text(0.599945,-0.00809402,'10.2%'),\n",
       "  Text(0.495928,0.337722,'9.3%'),\n",
       "  Text(0.232787,0.553001,'9.0%'),\n",
       "  Text(-0.0872587,0.593621,'8.3%'),\n",
       "  Text(-0.358457,0.481153,'7.4%')])"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "periods_by_month = []\n",
    "# Calculate the crimes on the period month/23-month/31\n",
    "crimes_period = crimes[\"Date\"].dt.day.between(23, 31)\n",
    "crimes_period = crimes_period.where(crimes_period == True).dropna()\n",
    "for i in range(1, 13):\n",
    "    crimes_month = crimes[\"Date\"].dt.month.between(i-1, i)\n",
    "    crimes_month = crimes_month.where(crimes_month == True).dropna()\n",
    "    # Calculate the crimes on each month and make intersection with period\n",
    "    periods_by_month.append(len(crimes_period.index.intersection(crimes_month.index)))\n",
    "labels = ['Janeiro','Fevereiro','Marco','Abril','Maio','Junho','Julho','Agosto','Setembro','Outubro','Novembro','Dezembro']\n",
    "# Plot a pie with the number of crimes on each month in the given period\n",
    "plt.pie(periods_by_month, labels=labels, autopct='%1.1f%%', shadow=True, startangle=140)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Resolução Kaique\n",
    "\n",
    "A aproximação tomada para a questão foi um pouco diferente da anterior no modo em que foi analisada a relação da quantidade de crimes na época de Natal com a de outros feriados específicos. <br>\n",
    "Primeiramente foi feito um agrupamento dos registros em relação à coluna \"Date\" formatada para apenas o mês e o dia, já que a coluna em questão se encontra no formato completo de data-hora. Após isso, foi feita uma contagem de todos os registros que se encaixam nos dias do feriado de Natal e de outros determinados feriados nacionais dos Estados Unidos, como o Dia de Ação de Graças e o Dia da Independência. Para uma melhor visualização, o resultado de todas as contagens foi colocado em um gráfico de barras, onde é possível comparar os valores.<br>\n",
    "O resultado obtido mostra que o feriado de Natal possui mais incidentes reportados que outros feriados, com exceção do feriado de Ano Novo, mas que a diferença entre as quantidades não é tão grande. Além disso, o que mais se destaca no resultado é a enorme quantidade de incidentes reportados no feriado de Ano Novo e a também grande diferença dessa quantidade em comparação com todos os outros feriados analisados, chegando a ser quase o dobro da quantidade do feriado de Natal."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1792ab66f28>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "crimes['Month and Day'] = crimes['Date'].str[:5]\n",
    "christmas1 = crimes[(crimes['Month and Day'] == '12/24')]\n",
    "christmas2 = crimes[(crimes['Month and Day'] == '12/25')]\n",
    "fourth = crimes[(crimes['Month and Day'] == '07/04')]\n",
    "thanks = crimes[(crimes['Month and Day'] == '11/28')]\n",
    "martin = crimes[(crimes['Month and Day'] == '01/15')]\n",
    "new1 = crimes[(crimes['Month and Day'] == '01/01')]\n",
    "new2 = crimes[(crimes['Month and Day'] == '12/31')]\n",
    "veteran = crimes[(crimes['Month and Day'] == '11/11')]\n",
    "\n",
    "holidays = ('Christmas', 'Thanksgiving Day', 'Veterans Day', 'Independence Day', 'M. Luther King Day', 'New Year')\n",
    "x = np.arange(len(holidays))\n",
    "numbers = [christmas1.shape[0]+christmas2.shape[0], thanks.shape[0], veteran.shape[0], fourth.shape[0], martin.shape[0], new1.shape[0]+new2.shape[0]]\n",
    "plt.barh(x, numbers, align='center', alpha=0.5)\n",
    "plt.yticks(x, holidays)\n",
    "plt.xlabel('Número de incidentes em feriados')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Resolução Ricardo\n",
    "\n",
    "Essa pergunta surgiu da seguinte dúvida: há um aumento/diminuição de crimes nos feriados? Para verificar isso, começamos verificando qual a média/mediana de crimes por dia em Chicago. Pegando a coluna '_Date_', normalizando de forma a ignorar o horário e agrupando por dia, temos que a **média de crimes registrados por dia é de 789.5468**, enquanto a **mediana é de 782**.\n",
    "\n",
    "Depois disso, verificamos a mesma coisa para o feriado de Natal. Expandindo a pergunta, verificamos para o Dia de São Patrício, Ação de Graças, Dia de Independência e Ano Novo. Por fim, comparamos os reusltados através de boxplots.\n",
    "\n",
    "Respondendo a pergunta, foi possível verificar que o registro de crimes em Chicago é menor no dia de Natal e Ação de Graças. Fica bem próximo da média no Dia de São Patrício e aumenta consideravelmente no Dia de Independência e no Ano Novo."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "crimes[\"Date\"] = pd.to_datetime(crimes[\"Date\"])\n",
    "crimes[\"Date\"] = crimes['Date'].dt.floor('d')\n",
    "crimes_by_day = crimes['Date'].groupby([crimes['Year'], crimes['Date'].dt.month, crimes['Date'].dt.day])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Year  Date  Date  Date      \n",
       "2012  1     1     2012-01-01    1451\n",
       "            2     2012-01-02     700\n",
       "            3     2012-01-03     805\n",
       "            4     2012-01-04     799\n",
       "            5     2012-01-05     871\n",
       "                                ... \n",
       "2017  1     14    2017-01-14     649\n",
       "            15    2017-01-15     600\n",
       "            16    2017-01-16     611\n",
       "            17    2017-01-17     659\n",
       "            18    2017-01-18     655\n",
       "Name: Date, Length: 1845, dtype: int64"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "crimes_by_day.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    1845.000000\n",
       "mean      789.546883\n",
       "std       122.376604\n",
       "min       363.000000\n",
       "25%       708.000000\n",
       "50%       782.000000\n",
       "75%       868.000000\n",
       "max      1451.000000\n",
       "Name: Date, dtype: float64"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "crimes_by_day.value_counts().describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f7fede59c10>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "crimes_by_day.value_counts().plot.box()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Year  Date      \n",
       "2012  2012-12-25    522\n",
       "2013  2013-12-25    424\n",
       "2014  2014-12-25    438\n",
       "2015  2015-12-25    495\n",
       "2016  2016-12-25    468\n",
       "Name: Date, dtype: int64"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "christmas_crimes_by_year = crimes[(crimes['Date'].dt.month == 12) & (crimes['Date'].dt.day == 25)].groupby('Year')['Date']\n",
    "christmas_crimes_by_year.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count      5.000000\n",
       "mean     469.400000\n",
       "std       40.221885\n",
       "min      424.000000\n",
       "25%      438.000000\n",
       "50%      468.000000\n",
       "75%      495.000000\n",
       "max      522.000000\n",
       "Name: Date, dtype: float64"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "christmas_crimes_by_year.value_counts().describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Year  Date      \n",
       "2012  2012-03-17    1069\n",
       "2013  2013-03-17     771\n",
       "2014  2014-03-17     703\n",
       "2015  2015-03-17     690\n",
       "2016  2016-03-17     732\n",
       "Name: Date, dtype: int64"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "st_patricks_day_crimes_by_year = crimes[(crimes['Date'].dt.month == 3) & (crimes['Date'].dt.day == 17)].groupby('Year')['Date']\n",
    "st_patricks_day_crimes_by_year.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count       5.000000\n",
       "mean      793.000000\n",
       "std       157.392821\n",
       "min       690.000000\n",
       "25%       703.000000\n",
       "50%       732.000000\n",
       "75%       771.000000\n",
       "max      1069.000000\n",
       "Name: Date, dtype: float64"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "st_patricks_day_crimes_by_year.value_counts().describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Year  Date      \n",
       "2012  2012-11-28    858\n",
       "2013  2013-11-28    565\n",
       "2014  2014-11-28    599\n",
       "2015  2015-11-28    657\n",
       "2016  2016-11-28    684\n",
       "Name: Date, dtype: int64"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "thanksgiving_crimes_by_year = crimes[(crimes['Date'].dt.month == 11) & (crimes['Date'].dt.day == 28)].groupby('Year')['Date']\n",
    "thanksgiving_crimes_by_year.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count      5.00000\n",
       "mean     672.60000\n",
       "std      113.73346\n",
       "min      565.00000\n",
       "25%      599.00000\n",
       "50%      657.00000\n",
       "75%      684.00000\n",
       "max      858.00000\n",
       "Name: Date, dtype: float64"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "thanksgiving_crimes_by_year.value_counts().describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Year  Date      \n",
       "2012  2012-07-04    1045\n",
       "2013  2013-07-04     946\n",
       "2014  2014-07-04     838\n",
       "2015  2015-07-04     842\n",
       "2016  2016-07-04     844\n",
       "Name: Date, dtype: int64"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "independence_day_crimes_by_year = crimes[(crimes['Date'].dt.month == 7) & (crimes['Date'].dt.day == 4)].groupby('Year')['Date']\n",
    "independence_day_crimes_by_year.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count       5.000000\n",
       "mean      903.000000\n",
       "std        91.433036\n",
       "min       838.000000\n",
       "25%       842.000000\n",
       "50%       844.000000\n",
       "75%       946.000000\n",
       "max      1045.000000\n",
       "Name: Date, dtype: float64"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "independence_day_crimes_by_year.value_counts().describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Year  Date      \n",
       "2012  2012-01-01    1451\n",
       "2013  2013-01-01    1305\n",
       "2014  2014-01-01    1069\n",
       "2015  2015-01-01    1030\n",
       "2016  2016-01-01     913\n",
       "2017  2017-01-01     753\n",
       "Name: Date, dtype: int64"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_year_crimes_by_year = crimes[(crimes['Date'].dt.month == 1) & (crimes['Date'].dt.day == 1)].groupby('Year')['Date']\n",
    "new_year_crimes_by_year.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count       6.000000\n",
       "mean     1086.833333\n",
       "std       255.117555\n",
       "min       753.000000\n",
       "25%       942.250000\n",
       "50%      1049.500000\n",
       "75%      1246.000000\n",
       "max      1451.000000\n",
       "Name: Date, dtype: float64"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_year_crimes_by_year.value_counts().describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([1, 2, 3, 4, 5, 6]), <a list of 6 Text xticklabel objects>)"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.boxplot(\n",
    "    [crimes_by_day.value_counts(),\n",
    "    christmas_crimes_by_year.value_counts(),\n",
    "    st_patricks_day_crimes_by_year.value_counts(),\n",
    "    thanksgiving_crimes_by_year.value_counts(),\n",
    "    independence_day_crimes_by_year.value_counts(),\n",
    "    new_year_crimes_by_year.value_counts()],\n",
    "    labels=['Crimes por dia', 'Natal', 'Dia de São Patrício', 'Dia de Ação de Graças', '4 de Julho', 'Ano Novo'],\n",
    ")\n",
    "plt.xticks(rotation=90)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Questão 3"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Resolução Yurick\n",
    "\n",
    "Apesar e parecer uma questão simples, para verificar os crimes mais reportados do dataset foram utilizadas duas abordagens: IUCR code e FBI code. O [Illinois Uniform Crime Reporting](https://data.cityofchicago.org/Public-Safety/Chicago-Police-Department-Illinois-Uniform-Crime-R/c7ck-438e) (IUCR) code consiste em classificar um crime de acordo com os regimes do estado de Illinois. Para isso, encontrou-se uma base de dados, que está anexada, que relaciona os códigos IUCR a uma descrição mais clara. Com isso, foram calculados os dez códigos IUCR com maior quantidade de ocorrências que foram relacionados a sua descrição. Depois disso, foram plotados gráficos de setor e barra para melhor visualização. O código IUCR com maior quantidade de ocorrência é o de roubo de 500 ou menos dólares.\n",
    "\n",
    "Na abordagem por [FBI code](http://gis.chicagopolice.org/clearmap_crime_sums/crime_types.html), não foi possível encontrar um arquivo csv com a relação dos códigos e uma descrição detalhada. Mas o cálculo seguiu da mesma forma, inicialmente a análise dos dez códigos FBI com maior quantidade de ocorrências, e então plotá-los em gráficos de barra e setores. O código mais reportado é 06, cujo significado é furto. O que confirma, de forma genérica, o código IUCR."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "IUCR\n",
      "0910     51741\n",
      "0610     53500\n",
      "1811     60557\n",
      "0560     61942\n",
      "1310     71694\n",
      "1320     72515\n",
      "0810     74906\n",
      "0460     88069\n",
      "0486    130700\n",
      "0820    136036\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "iucr = crimes.groupby('IUCR').size().sort_values().tail(10)\n",
    "print(iucr)\n",
    "reader = csv.reader(open('Chicago_Police_Department_-_Illinois_Uniform_Crime_Reporting__IUCR__Codes.csv', 'r'))\n",
    "# convert iucr to description\n",
    "d_iucr = {}\n",
    "for row in reader:\n",
    "   d_iucr[row[0]] = [row[1], row[2]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0910: MOTOR VEHICLE THEFT - AUTOMOBILE\n",
      "0610: BURGLARY - FORCIBLE ENTRY\n",
      "1811: NARCOTICS - POSS: CANNABIS 30GMS OR LESS\n",
      "0560: ASSAULT - SIMPLE\n",
      "1310: CRIMINAL DAMAGE - TO PROPERTY\n",
      "1320: CRIMINAL DAMAGE - TO VEHICLE\n",
      "0810: THEFT - OVER $500\n",
      "0460: BATTERY - SIMPLE\n",
      "0486: BATTERY - DOMESTIC BATTERY SIMPLE\n",
      "0820: THEFT - $500 AND UNDER\n"
     ]
    }
   ],
   "source": [
    "# For each iucr show a better description\n",
    "for val in iucr.index:\n",
    "    desc = d_iucr[str(int(val))][0] + \" - \" + d_iucr[str(int(val))][1]\n",
    "    print(val + \": \" + desc)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<BarContainer object of 10 artists>"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot bar with iucr\n",
    "plt.bar(iucr.index, iucr.values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "([<matplotlib.patches.Wedge at 0x7fb4519205d0>,\n",
       "  <matplotlib.patches.Wedge at 0x7fb451920a50>,\n",
       "  <matplotlib.patches.Wedge at 0x7fb45191ce10>,\n",
       "  <matplotlib.patches.Wedge at 0x7fb451913c50>,\n",
       "  <matplotlib.patches.Wedge at 0x7fb451913710>,\n",
       "  <matplotlib.patches.Wedge at 0x7fb458294cd0>,\n",
       "  <matplotlib.patches.Wedge at 0x7fb4582aab10>,\n",
       "  <matplotlib.patches.Wedge at 0x7fb4582aa790>,\n",
       "  <matplotlib.patches.Wedge at 0x7fb44f4d0b10>,\n",
       "  <matplotlib.patches.Wedge at 0x7fb44f4c5c50>],\n",
       " [Text(-0.967774,0.522889,'0910'),\n",
       "  Text(-1.09622,0.0911309,'0610'),\n",
       "  Text(-1.02792,-0.391649,'1811'),\n",
       "  Text(-0.730854,-0.822102,'0560'),\n",
       "  Text(-0.221777,-1.07741,'1310'),\n",
       "  Text(0.389691,-1.02866,'1320'),\n",
       "  Text(0.888216,-0.6489,'0810'),\n",
       "  Text(1.09997,0.00850214,'0460'),\n",
       "  Text(0.713459,0.837243,'0486'),\n",
       "  Text(-0.366378,1.03719,'0820')],\n",
       " [Text(-0.527877,0.285212,'6.5%'),\n",
       "  Text(-0.597937,0.0497078,'6.7%'),\n",
       "  Text(-0.560681,-0.213627,'7.6%'),\n",
       "  Text(-0.398648,-0.448419,'7.7%'),\n",
       "  Text(-0.12097,-0.587679,'8.9%'),\n",
       "  Text(0.212559,-0.561087,'9.0%'),\n",
       "  Text(0.484482,-0.353946,'9.3%'),\n",
       "  Text(0.599982,0.00463753,'11.0%'),\n",
       "  Text(0.389159,0.456678,'16.3%'),\n",
       "  Text(-0.199843,0.565741,'17.0%')])"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot pie with iucr\n",
    "plt.pie(iucr, labels=iucr.index, autopct='%1.1f%%', shadow=True, startangle=140)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "FBI Code\n",
      "03      57313\n",
      "07      61138\n",
      "11      66547\n",
      "08A     68076\n",
      "05      83397\n",
      "18     129796\n",
      "26     137597\n",
      "14     155455\n",
      "08B    227082\n",
      "06     329460\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "# group by fbi code\n",
    "fbi_code = crimes.groupby('FBI Code').size().sort_values().tail(10)\n",
    "print(fbi_code)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "([<matplotlib.patches.Wedge at 0x7fb44f9b36d0>,\n",
       "  <matplotlib.patches.Wedge at 0x7fb44f9c7b90>,\n",
       "  <matplotlib.patches.Wedge at 0x7fb44f9c7050>,\n",
       "  <matplotlib.patches.Wedge at 0x7fb44f9bb850>,\n",
       "  <matplotlib.patches.Wedge at 0x7fb4503d1f90>,\n",
       "  <matplotlib.patches.Wedge at 0x7fb4503b8bd0>,\n",
       "  <matplotlib.patches.Wedge at 0x7fb4503b8650>,\n",
       "  <matplotlib.patches.Wedge at 0x7fb4503c9090>,\n",
       "  <matplotlib.patches.Wedge at 0x7fb452d67ad0>,\n",
       "  <matplotlib.patches.Wedge at 0x7fb452d67650>],\n",
       " [Text(-0.931221,0.585514,'03'),\n",
       "  Text(-1.05762,0.302404,'07'),\n",
       "  Text(-1.09962,-0.0289782,'11'),\n",
       "  Text(-1.03415,-0.374869,'08A'),\n",
       "  Text(-0.834631,-0.716513,'05'),\n",
       "  Text(-0.379673,-1.0324,'18'),\n",
       "  Text(0.310317,-1.05532,'26'),\n",
       "  Text(0.916991,-0.607558,'14'),\n",
       "  Text(1.0413,0.35452,'08B'),\n",
       "  Text(-0.0945769,1.09593,'06')],\n",
       " [Text(-0.507939,0.319371,'4.4%'),\n",
       "  Text(-0.576882,0.164947,'4.6%'),\n",
       "  Text(-0.599792,-0.0158063,'5.1%'),\n",
       "  Text(-0.564084,-0.204474,'5.2%'),\n",
       "  Text(-0.455253,-0.390825,'6.3%'),\n",
       "  Text(-0.207095,-0.563127,'9.9%'),\n",
       "  Text(0.169264,-0.57563,'10.5%'),\n",
       "  Text(0.500177,-0.331395,'11.8%'),\n",
       "  Text(0.567984,0.193374,'17.3%'),\n",
       "  Text(-0.0515874,0.597778,'25.0%')])"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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mTS6yz1iymBAmYZ4QzIwMk95gu5qVX+IPkamOmkQr9Ignl5aXVT1utBFDzYgRZLlolgzgJi4la3TK/AtPJpyQkI98EadSr01MMzMyTAAAqfVfBO5If4B38qFELfSIFxqAU8vLqt402pChZEQIslw0iwVwJRHk6alHXXYcK9oyDnqQgeiUUiVTrdWL3alEksyMjBEI1TQ6q/55/zW5r4/kGY1BALPKy6q+NdqQoWLYC7JcNIsAOAWELE4t/9lszukea7RNfaUnI6OOFmV5zIyMkQPp7lJ/Fro3OD/1h5EsxjvYBGBGeVlVn2sLkpmE8aHGkdkAljhnn3FIMokxsDMjI9/yYYNON9dtMzMyhj/Wph9D9zPXqqYY76QIwIiZ0TesV8hy0axCAL+Tx8512iYfmfTtvjRe8YbHSl0kx8zIGG5QSjF++1L/TdlPWzmGmv+5+3JDeVnV/UYbEW+GrSDLRbNSANzKOjLE1NJLziMcn5BBvIGgSJFWZYJTNTMyhgnhoH56x1/8J7k/GwkpbQNFBVBWXla1ymhD4smwFOSeIN61BHTspPKTjmhyTZ1gtE3xIOKI1KsT0y2Mw55mtC0mA0Noq438n3CXWmhr7lOr1xFOPYCJ5WVVnUYbEi+SqUF9fygHMOVvsxrdP/HcM6E2JLV9GBrne5sskNYKMzIpYYfFI6HgFbK51V1UyWyr0YvdKUSSTL9jEpG9fWX3bZ6HRIlV+93qdYSSDeA+AJccbMdkZditkBeNFfJa4Xgpy8mLLxwbmGRhwe6+vVtlgp8Fclrf0Wfhf1xphpdLScaWhfugQ1MjudhOx5o9MhIeRaFHNT/uuyj7A9NFMTDKy8uqlg/2JL0MTS5HtI0vA8CP6NDkzYSQCgCXAmgBIAKoBHAlpTSmfTeGlSAvHscLAG7WKTwPnSCdlO8k6QfaX6egm4KO5vcjk4JLmVLHj8LEpC/G0BktGB7NNGN0Vi5hWfbgR5gMJUxXi3K9fnd4mmubmV8+cKoATC4vqxrwGJieockbARyF6Di4tQDOBvAGorM6fyCEXAHgMErpRT2C7KeU3kcIYQCsBHAzpbRykPeyB8PNZXEcgFEXlfCOg4kxADAEZJzsdY+TV+MqrEZ7hPN9FBzdvpQezq8Q5mWGGSnpfj6MzkpSFfK1mlpveKzYRXLcZkZGguCq/yp4Z9r9rEsImmI8OAoB3AbgVwfb8QDsHJoMAISQFwAsQXRI8o4nFyeifuu9ERBdJcd86vywyUNePI7PAbDELqDxuEO48oGcI1VQ7Yudm/L/4Xoq+1vxYrymXl3/s9CT1dlKzZAlpVNdQ/2TV6P5lVt73ad7w8eovmcRwg2bAACh7d+j/omr0PD0tVDao1OtiC/s8N1y0yh2xZZWvaWtYUiMN9kvVNPo9NrnfQ9l3SG5hKDpL44N1y1bXjiYgRL7G5qcg6h/+l1CyHYA5wO4e/drEkLWAWgAsJFSum4Q198vQyLIhJBjCSEbCCGbCSE39ry3ihCyruernhDyxkDPv3gcTxAdbhq+bIYwyyqQQfvmBAbcobbW7Jtc7+evtt/oWI2ftt8ZunvbrPAnTYRqcfPz+D5/C3zaqF636+EAfJ+/BSFr3M73vGtfR+ZpFUgpvxS+df8BAHStfhHOOadDCIvp8pehLObTbfW619sWL7tN9g8JeNVL22/1X5/7ihlwjS0sgD/F4bzXATieUpqLaEHKA7tt+xOltARAJgArIeSsWF887oLc46t5CFF3wgQAZxNCJlBK51FKS3pu8BNEJwYMlGIAUwtcJDA7lz188FbvS7YYSj3X9U3Bi86/ur/jLww/o/yq9qzgyzUutS1m44xVbyuCW9bCNvXoXvfpXPUsHLNPA+F29ZohDAeqhkGVMAjDQelogOprhZg3Zec+QhefLa72ppIvt9XSYNAXK5tNekdq3hT6I65VS9O/M8U4PsxbtrzwxAEeu7+hyU0AplJK1/S89yKAuXsfSClVALwHYP4Ar90rQ7FC3umroZRGEJ0MvWTHRkKIA0AZos70frN4HM8BOA9Ax2UzhDKeJXHviiVzujjfXjfq7pTX8760/kJ8X/9Z829Cf9k2MfLtoFagHcv+AdfCn6I3n2+4cTM0Xyvkwpl7vO+cfTpalz6Ark9fhv3QRehc+Qxc887b53iGEGJp4UdZVrZI+K5mG1WU8GDsNemdotp3/H9P+60lW+oaFlk8Cczdy5YXDkTH1gIoIoSMJoQIAM4C8BYAJyFkR4uFowD8sPeBPQOVD0c0uBhThiJotT9fzazdXp8EYFl/BpPuxRwA2bNy2GBxOlMywHMMGIaAFMn+zCL5U1yOT9GhcP7Vgfz2pfRwtlI4IjPE2Pr0ByKw+TMwVhcsnkMQqvlmn+2U6uhY/jjST7hun22CewyyLohWlYZq14O1RZNFWt68B4RhkVJ2MVhryi6bwXLidhTo9Q2h8Gim2szIiCHhoH5qx9/8p+R+aqa0DQ0TEBXT5/pzEKVUJYRcBeC/6BmaTCn9mhByKYBXCSE6okG7n+522HWEkPMA8AC+AfBwLG5gd+Ke9kYIOQ3AsZTSS3penw9gFqX0qp7X/0E0B/DV/p578TjeCuCPAPz3H205oSiNnRhD0weNqkNbH0xt+q8yTVnKlrlq+dHO3vbtWPEUutdXAgwLqkVAw0HIY+cg/cQbAAB6uBt1j14KwkcXXFp3B1jJjoxTboYlqwhAtB9C80u3IH3xr9H+wd+RMv9CqF1NCG77CinzL+jVTo1TveFxFjMjY5Dw7XWRm/i7lLG2xpHUSD4R2IBoBV/SN98aihXy/nw1dQBACElH1KVx8gDPXQpAKk5n1MJUJuHKozkGbIm1PbsEy/AbLENDyNLxYajIuxTzLZ9YZmXqhN/5qJWy4CKkLLgIABCq+Qbez17fKcYAwFisGHX1rkVA43M3IqX04p1iDADd65dDGjMDrGQHVcIAIQAh0e8PAKtyDvk7zaFUVbcqE+wKk5GWFaufwUjBs/2j7tvdf7PInGKK8dAzDtF5fC8YbchgGQpB3umrQVSIzwJwTs+20wAspZSG+nvSxeN4CcDxAJrOnsyXM0mwtMsSwylni+tTzsZ6BLVHwl8EPC3vqjP194Ty9HY2Y7+9DDpXPQvBUwS5aNb+Nu9EV0Lwr/8A7jNuBwA4Zp6E5pcrQFgO6Sf2LV2TD/Hp/JchRBxbG9QJaQLjdJg9Mg4CVRVa3vSE7+Kc/5kuCmO5AsNAkIekUo8QcjyAP2OXr+bOnvc/BHA3pfS9/p5z8Th+AYCL0iTS+I8TxV8ORTAvXugUdFvI2vp+eGL3UlJq+0aYnB4tBjLSJgolQ6nRJ3hcRJJMsdkPjLdVuVa7JzzdtcUs9EgMJpeXVa032ojBkJSl04vH8SyivmP9ipnCpGMP4Y412qZY0qWw3Z8E89qW6nOYZfzCzCBrM6yYQIeuRXL07XRclpvwgpkx0IOrYV3w9tT72VQhYBZ6JA4PlZdVXWW0EYMhWQV5EoBfAqh+5mTpKpdIhu2jtapD+yGY0vxepCSylCt3VvNjXEbYoTNaKFzANGJMVi5h2aQrKY8VVNdxaN3LvutzXrQzie8lG2l4AWSXl1V1G23IQEnWX6xjAATmjmKzhrMYA9HA4GRrR9ZkayV+hUo0hYXOFcFDOpeSeZaPhTlujQhD4ttgdFaUtqBAq93uDRdZOkmuexRJAr99LCEBn/aT7vu7j8z91gEYf+v33tuMNZ8G4HKxePyf0bj57bc3YXutAgDw+3XYbAwe/UfuHsdFIjquu7YBikKhaRTz51tx4UXRVMm77mrG1i0RzJ4t4+JLou89+2wHRhcIOPyIhI9XOhCNTz1mtCEDJekEefE4PgPAJAA1C/LZUqPtGWrclojrDMv3rjPwPULaPyJfBdzN72oz9He5I9PbuMy4NzlnFc4hf6851C3b2iLFjjCTmZYd72smAmLL5vCt1rtpbnpHwvjTjznGjpOWOHHPPc0737v5ZvfO7//+SBus1n3/XvM8wX33Z0GSGKgqxbXX1GPmYSGIFgKLQPDY47n49a8a4PfrCId1/PhDGOedl7LPeRKU85HEgpyMzYUmItqRiRZnsMVGG9MfNJ1i2qN+LHousN/tL32nYMJDfkx82I9zXo3us6FVw/R/+DHlET8+qVUBAKpOceQz3dB1XZhjb8y93bU073PrtXIlvbTl/0IPbCuJfNkS4zat+8CFhDT5q1A2s3prg97lbY3rxQxmTO17/r+n3sjnSh0J5UOfMkWC3bH/X2FKKVas8KO0bN94IyEEkhQ9TlUpVJWCEIDlCMIRCl2nUDUKlgWeeqoDF16YNGIMAHOXLS9M2ja6SbdCBnAEAG+Jh0l3iQdvsZlIPLgmguJ0Bt79pAVvatPwh4/C+PinVqRIBM3dUUF99AsFDx4rosDF4Jr3Qnh1FIdH1io4bwoPmd/12EwIMFrqzrhE+jzjEnwOn8IGPgnmtr6jz2E+4BdmdLOOuDStF3xCFveJF0pGe61enOkkspwwK8hBEw7pJ7U/5D89d3XS3dO334aQksIiN3f/yUeaRnHF5XWoq1OwZIkDxcXRvzUuJ4PLL6vDkUfaUFengOpA0dikmnfAIpoO+6zRhgyEpBLkxeP4FABjANSWj+aOMNqe/rDdq+OdTSp+N8+CBz6J7LP9sS8VXDlTQIoUFdnMnkdNngECChBQKHgG6AxRvL1RwXvnHdg7Yec1+Wi+Ou9oVEPTX9B/CLoa/xeZGlrKlrm2CEUxDQwyhMDSyo/SV7VpkZyWajo2y02E5M7I4DrqIzdxdynjPA1JJ8YAsHy5H6WlvWfjsSzBo//Ihd+v4fe3NGHr1ghGjxZwxZW71jj/97tGXHtdOv797w5UVUUwfbqEE05Iih/HiUhSQU42l8X4nn/phAwmqdwV174Xwh+PFMH0Egva2KZjY5uOw5/oxuzHu/He5qh74srDBNy1KowL3wjhpnkW3L4ijJvmWdCfCD/LgJlk7fRcn7KiYLnj96615MKu+8O3VZeGKhs4PRKzclMGDCvWcfmWDxtBN23fRjVNjdW5hxJ33eruh23XM+NsDQkfxdofmkbx0aoAFh5AkHdgs7EoKZGwdu2ebrSPP+5G0VgBoaCO+noFt9zixsqV3QiF4usKixHHLFtemJR1CckmyEcA8LutREqXSdKU9y7dqCDTSjA9u/f+PaoObGrX8eGFMp4/VcKlbwfRGaLIczL48CIrPrnYCpkn2O7TUZzO4PzXgzjzlQA2tvVfTzMsivNU54/5T7oey/rO8hPtReW6uouCz1Znqg3+wdznDhjKitIWUiB8uD1IaxpraZLkVlJVpQvqHvc+kH2/1copSfX0uDtffBFEXh6PjIz930Jnpwa/P/q5CYf16P6jdumXqlK89moXzjzThXCEgvRklOh6dFsS4AQwz2gjBkLSfOh6GgmNB1A3O5ctTKaMq49rNLy1QcW7m3wIqYA3THHea0E8e4q0c59cB8GsHBY8SzA6hWBsGoNNbTpm5uwS8d8tD+GOUgv+siaCS6bxKHAxuGl5CP8+ZeDJFRaWCrPsTTmz8C4q8C6qg3Lr8vB4/1KyUP5CmJaBQUzoZlXOLv2g2dWt1W2RYntCZ2QwvjblauWe8MzsqqR4JgeAO+9owtdfh9DVpeGsM6tx4YUpOO54Bz6s3DeY19qq4oH7W3DXH7LQ3qbinj+2QNeiwb8FC2yYPWfXw8Cbb3px9NF2iCKDMWMEhMM6LrmkFrMOk2GzJU1TwMMBDHoQ6lCTNIUhi8fxxQBuAFD72yOEsjmjuKT8C/jhNhX3rY5g6Tl7iuh7m1U8v17B0ydJaA3omPZoN9b93Io0OfoQs2Kbijd+VPGnY0Vc914IpxRzKHAxuPq9EF4/Mz7Zbn6VCawJ5LYt1Wfjfb400886BxXdidgjDerENJ5xOhIqGOto/DZ4h+teJs3SnVTRK5MD8kZ5WdVAm5YZRtKskAGM3vHN9UVZ6bqda5MVPeJQNTVF1fR0VaMeTWM8VONyqMZnQxdzoEk2krj3eEtlCDOyWSwex+OYQhb/q1Ix4SE/WAa49yhxpxhTSnHHqjBePC0qvD+bzuPc14JQdeCRE+IXO7NxulzuqJHLUQNhYwmqAAAgAElEQVSNvqRvDDgb/6dMCS1lSh2bhPH9Ti3amZGR3l6rTzA+I4PqOqbWver7Vc7zZtXd8GOa0QYMhKRZIR86QfyzDszgge7QNWMWQGT7tJohqh7hFT0oKnrYpmiKU9W1NFXTMzSNZOkqm6XrXA7VhGyiiW7oEpdMvhADaY3w3o+CozvepvP4VcLczEg/J3Tr0LVItr6djsvKJIIgHfyIGBP0axf4Hggck/G1OV5p+JJWXlbVbrQR/SEpBNkxzUFYSp9kKXWwds6ReemoAU2VPiiUUlahQUHRQrKiR+yKpqSomp6uadStaSRL17hsqgk5VLdkQ5OcDJIykhtrIjpRvg6kN7+nTFff4ctTG7mcPouczmjhcD7TgMKh65EhtmwJ/17+g54ntw/9HwKToeTI8rKqZUYb0R+SRZDTEO3uVmubYhudVpbW+/iLIYRoVOEUPSQqWsim6BGnommpmqZnqBpx6xqTrWtcDnRLDtVED9EkfoSsvmtDUltlaJz/bbJAXCvMyOxLYFDjVF+kSOjAKE9ce2SMrv2f/5bsx2SB0ZMtw8ik/9xQXlZ1v9FG9IeE9a/uxc4Cfc7OJcwjJmUJr7Asr4is3Qeg4YA7UzAqDQqKHpIjWtiu6qpLVbV0VaOZmk6ydI3NpiqfQ3Uxm2hSKkHStnUcJQbTLhDXpV2AdehWmeBngZzWd/RZ+B9XmuHlUvbr9I5mZOh2dUt1e6TYFmLc6bHNyIiE9RPbHvGflbsqabIoTAbNqIPvklgkiyCnoydnmpGYuDfQiQuEQOeJFOIZKSRzaAdQfaD9NapyPb5vq6JFnKqmpvb4vt2axmTpGptDNSGH6JKHapLIGNzRvhesnC6VOmpHlaIWOn2Fbgo6mt6PTAouZUodPwoT9wkMcmE+lVsXRsS2tVGdmMoyLmfGYG3gOhsivyF3KRM89aYYjywyjTagvySLILsAaADAimxSVk/1G5ZwKsva/SJr94NH04H2ja6+Q7yihyRFC9sVXUlRNS1N1WimrhGPprHZVON7fN9iBkMNKWtmCMg42eseJ6/GVViN9gjn+yg4uv1NbY5tlWV+aoSVd7oqBL/g4T71QUnvqNUnZDqILPc6IPZAZNR9Grg980HBzkVGxufGZHfcB98lsUgWQU4BoAAAIybpCjmeRFffYphnxDA4dAKoPdD+OtU4RQ9aFD1kVTTFoeg7Vt/UralMFtW5bKoKOVS35ECTRYbEpRogVVDti4VN9sXYhIj+jPq5P8P3TmSqsFwo4xqEAgtDCCxtPT0ysluq+5ORQTWVzmt4xnd5zjvmqnjkYq6Q48QuQbYw5kpnsDCEVS2sTbWwtm7waD7I7kTVw0JED4qKHrGrmuJUNC1d1fRMXSMeXWOzdY3L7gleZhBdGkhOr8CAm+toSZmLDwB8gI0BOfJhuKh7GTtf+Yw/LEOs5/L1xsZwOJ+pRmFWzoEyMoi/Q70qck9wTs4mU4xHNuYKOU440SPIRCBJs0Le8MsNYCQGhBCABQ6pOGSP7eH6MLb/cztC1SG4T3Uj/bhoAZvqVVHz1xpoAQ3uU9xwTI/qSvWD1ci+IBt8ytBm21GOsYQ5xhIG0AVg+4F21qnO7lx97yrcyVA16tY1JkvXuGxd47OhWXKIZrWS/a++x8oBYaz8tfAzfA2fyqhfhrMb340cpv93c1lmW60WihwitCNv34wMe+N3oTtcfyTpVn/CBH9NDCPppgnFVJAJIU8AWASgmVI6qee90wFUACgGcBil9PMBnNoBIAIADMckVVvH0b8ZDc6+/x8za2ORdW4WfF/69ni/a00XUktT4ZjuwLYHtsEx3QHvV16IeeKQi3G/YQijWVhrwMJaAwBaDrL7zsKdiB6xhjTGEdY4Z0TnUimlblA1GxqyoFvyxKasO5nX2D/gNVoVtre8/+1k/eWq8prvJpZwjDs9h+o6Jte94ft1zr9tLEmA+UomiUDSfQ5ivUJ+CsDfADyz23vrAZwC4NGBnNAxzUEQFeQGAEiWzmF9gXNw4BwcfF/vKchgAT2ig6oUhCGgGkXb/9qQf22+MYbGEcoxQoRjhIgEeJ0HTh2kOoAIdE6FZFVr1MKWf4Zmv89rX9lK2zPGBNUluZVEUyjLJtFTlElcSbr2rzEVZErpSkJIwV7v/QBEx8YMEAYADyDaiJUiZv174w4Btt23DQCQWpqK1IV9a//gmu1C7d9r0f5hOzxneNC+vB2uuS4wloTMbIsLTERHWpMWyWzSVXerrme060ymjzBpAZZNUVhrhE8V12cfHuhypPJHdK2kJ/A/515rTFVYiygDgbAgtPhEsTUoSR2abPUyVjlkka2qzWIhZgxi5KAYbUB/SQYf8p4dsSmSokM2AIz53RjwKTxUr4pt926DJcsC67iD6wErsyi4vgAAoHVraHmnBXm/yEPdE3XQAhrSj02HfEhyLwL5kK5kNOt+d7Me9LRSxdNBaaaXsOndEFIjxGqlxMqACAArRKfyAD5G0L9In6pu8MyhjpQivqPzY4dNWwGeYZHBpZKz1QXac+oyL2e1OSKRfEskkg+vd8/rEhKMcFyrT5Rag5LYrslWL7FagxZZVq0WC2wjpJhypDCyV8jxwPuVlzqmOVREV8o6Vem+848SlB3+Xs7BwX6oHcEtwT4J8u40v9WMjEUZ6Pq0C/JYGc6ZTtT8tQYFNxTEweLYIfm1UEYz7fY06yFPK1U9HUCmD2x6EBaXQmwyZSQAKQBzwAmaKghd7yz21WbN4cSMyRLL8oITQH3DywGXpUZmGBaEihEAgoO1sRfqx9qe6nrPxztt+w3qUSoJijIqTVFGwefde2tY5flWr8XSGpDkNlWWvYzVGhRkWbWKIrXFs6TbJC6YghwnVPQ46KlKQwbb0if0sA6qU7ASCz2sw/+dH5mL+5cWGW4MQ2lXYCu2obW2FSwfXSnqEeMfEhwdWiCzmfrdLXrE00pVTyclmX7CpQUhOlXGbgERAYgDHUpTax3V+qPnCF1zT0sVBKtjx58xTVPQ1PREZ6rU7doRs2F2qzKXGAtzqbDI9qx3eY3qYPL6d1ULpyg5qYqSk+rfa3YKpYrG8y1ei9gakKR2VZa7EBVrxSqK1M6Y/TsTkaTQit1JFkEOIvrcqumKnhQ/ZLUrmroGAFSjcM52wj7Fjvbl0W6AqWWpUDoVVN1aBT2oAwRo/V8riu4qAitFhbfp1Sa4T42mUrpmu1D9l2q0vtOKzJPjm+9ONJ2mtlF/RrMeyGqhYXc71d2dlGR2EyE1RCSnRmwciAxAjuUUsA7B5f3WfXi3zzMzRbJmpLPY4ayIEoq0a962p32pEt1jSKvM2ryIltcDADjCkgv48rwXulZWB5xaTCKhhPCsqmanqP7slO59Bl2pOse1dgmWlm5ZalcluQtWa4CXZcUqSdTOJGhZ+wjggPVRiUhMu70RQp4HsBDRX44mAL8H0A7grwAyAHQCWEcpPaY/53VMc9yJaGAvkHFiRrlcKCfVxOlEg43oWlqr7vM00ZCnVY942qFndlEmI0CE1DCRbTqxsRgaEQkyYnh9xoy2Zs9su+gqsPfmFfD6Nyl64M2wyDP7TO5M5Qrrjxp12n6bEb0e+nhbmytUEFur+w6lqs5x7T6LpaVbktoUWe6CbA3wVjkiSzK1M3GqgjQBALxUXlZ1ptFG9IdYZ1mc3cum1wd5aj+igg6lQ2kd5LmGPZagHslo1v2ZzXowq5WqnnZKM71g04PEktITMCMgPY/8Q68HKoi+MWVS0zbPXAufXpzCsnz2geqhW9o+Ckj0U07YjxgDgMBIvWbenCweXvDfzs+ra5ydeUb4gAnhGE3LdAYCmc5AAGhr27WNUp3uEGtRbAvLcidkazdntUZkSaJ2liXJ8gSbqNQYbUB/SZb/8FZEW+l5Iy2RES/IVq8WzGym3e5mPeRpo6q7gxK3D1xakFhcCrFJYEQAqYk2VLzGmtfyo+dw6O5pKYJgzepLhU9948veFKHGThimVzEVGPmATvVjxBn5H3Wt3/69oyGLYZiEWZESwhBNS7cHAun2QABo3222BaU6WK7DZxFaukWxLSJbO6ksd3NWa0SSZd3OsiTBK4QSggM2VExEkkWQtwKYAwCh2tDBir+SGqrrSOmg3ZnNtNvdooc9bVTzdIJk+MGnhYjkVIlNAJEASIkmuPujTUjxfuc+PODzzHBJ1t4G0+9LNHj3z85UKeA6WMGVhZEO6nc7QpyUa/VZGj+zbU1lWTbhe00TwkDX0uzBYJo9GAQ6OvbczrAd3YLQ4u9ZWeuy7Oes1rAky7qd40jC398QYQpynGgCQAFAD+gRPax7GQuTlI1jiKrrqW26391MA54WPZLVRnV3F5j0bsKnhonsiAbMrACsySC4+2Mvv7CDEOLoz6yknuCdf+/gXW9YWKlProhpliKP7BdbP7R+L7Mcl9SJ3LqWYg0FU6yhINC5t1gzXQFeaPFFxbqDWmU/K1tDoizrdp4nI2my9iajDegvySLILegRZABQ/WqLYBESUpD5kK6mt0QF192mq1lt0N1eyqYHiJASiQbMGBAHQBxG+G/jRX/9wr3h9W+K6IE3Iw6R6XP/Y4ER++wbHmcZlS4Ghc7/WL7SOIEflg2IdN0ph0NOORw6BF2de25jGF+Q51v8otgakuQObZdYa3ZBIEnVJ+YgtAPYYLQR/SVZBLkN0eUiAUA1r9aCNBQaYYjk18Ppzbrf0xIteHB3ULh9YNMDxOJSiNVKGRmACyCu4SS4vTEQv3BvtLStCkh0Ta/Bu96wcGK/HiXyebfr5Mgs3+t0TQdn4Q9YmDLc0HW7FA7bpXB4DLq69txGiD/EC60+UWwNSVK7ZrX6WFkOWaxWzSYkX3+QT8vLqpKu701SCLL3K6/imOZoASABCERaIvXS6PgMDLZ1agF3NGDW47+lyPARLi0EMUVhbBYQCwBLsroTYsEOv7DfM9MlWtP77Bc+EH0J3vWGhRH7bYKbS7GfpRwRfEH/qJmT+KRrZB4PKLWJkbBNjIQL4O3ae2sgLAitPovYEpKkDs0qe4nVmtD9QVYbbcBASApB7mEbgIkAAt0burc6DxvARB9dpylt1J/Zogc8zbTHf0tJhp9wOwoe+DgUPOzOkVWbYWVYMATgQPByQcEe29/2duGfbe2gAKwMg1vcbowXRbSrKq6ur4NX03B1egaOtEeftq+s247fu93I5OIbdI/6hWf2+IXzHYQQRyyeb3uCdx2pUiBloN0SBdYyoJt3cTbpAr2Ue7a7soGx8lkDuviIQbZEInmWSCRvfyXnCs83e6PNnNpV2eplEqA/iCnIceYHADMBQGlT/Fq31sxa2T1WNqyia2ktut/dQgPuFl3xtEN3d1E2oydgZo8WPNgR/TLkJgDgqVGjkMLt/0efy/N4Oi8PTpbFSr8fv29qxIv5BXjH58UZTheOsttx2fZaHGm3o9LvQ7HFEjcxjpVfuDd2q7wblNtAYIUB/wBkxsJfpJdn/stXuZ3a2dzB2DFysfA7+4P49t7W0x9EbA1KYrsqW7uwQ6zj2B9EBfBZHM4bd5JJkDfv/mLsisD6+Z38GLc36r9NiRC5p0OYEyDOZPXfTpN2ueqmShKa1Gh/FB4EIUoRoRQMIVApxTMdHXg4J/YaEku/cG/sFrzrUybFgRBYflCZAwLDsxeRI3P+3VVZHXFi+DWdNpSd/UHg92GPiQVx7A/yWXlZVXcsrB9qkkmQ6xmdRsQIRnMaZOmLbutxXHqB0Ub1F0IILtleCwLgDFcKznD1rkevdnVinjXqnjvB4cCvGurxcmcnrs/IwPOdHVjscEBiYuNaiYdfuDcGGrzbLxSUY7hBp3KxhCEXWMrzX+pauc3rVAoGbZfJQelLfxCLpTUgSm2KLHthtXb3tT/IYCuDDSNpBNn7lVeflG1vksNYRABfjR7qDGboEYlhkioJ/tlReXDzPNpUFZdsr8UYQcAMed8A9ppAN17r6sKzedEFm51l8ffcUQCALk3D4+1t+EtOLm5pbIBX03FRaipKpP45FOLlFz4Qgwne7Q8WYogQEjNPyhmW+QVvd35a3ej055vdNo2EY1TV41JVj6u7O5pmtYOe/iBdFktrtyS1KZLcBVH0W1NSVNVioamEmII8JGT48DqiJdQ1AFCvKJ5Ci2WqsVb1DzcfdXemcRzKbTZ8EwruI8gbQiHc0tiIR3NHwcXu63r5e1srfp6Whne9XhwqyTjabsc1dXV4bNSog15fBdE3pE5uqnbPEfn0Yles/cK9XleL0OamJ7r6UnnXHzhGjCCafRMzThRn5y/v+qqmytmWS4jZqS3R2Ls/yB5qDbq2ouLWKoNMGzRJJcgANiJaIMIA0D8PBr5JJkEO6DooKKwMi4CuY3V3AJen7zkYt15RcHV9He7OykKBsO/if1skgkZVxWGyFRvC7bAQFgRAiB64R3KNNa9lQ9YR0DJL4uYX7o1QpF3z9aPyrj/wRIzLwIIycVqeteuH+q8d2zMZhkm235MRDHneaAsGQ1J90Cp9vkCp3f49gAIAbSu7u7ccb3e02Vk2KcZ9t/WkrgGASilOcDgwz2rDCz21r2e5UvBIWyu6NA23NTUB2Dc17sHWFlyTngEAON7uwC/q6vBYext+kZ6OvdnbL7x3f+GhwOvfGNEDbyn2flTe9QeekeI2FWKWWJwt+8Sm1bZNLpZlR1LJcbKiAUhqQY5pP+ShoNRunwbgavQ0DjnH5Zp1hNV2rLFWJQ57+YV77S88FOwI3nFs/Pz8bn5C7cLcEw/uqxkEVUp9+/vitxaO5xKxAMJkF/+tqKhIai1IqhVyD+sR7Y8sAgi96/Wtmy1byzgycjtcGeUXPhD1DS95Uyy1MQve9YbASnGfZ1XIZ6eKIaHrbfp5FyfwcVnpm8SEvxptwGBJOkGu9PmUUrv9PQCnAKjt1LVwVST8zTiLOMNo24aaGmt+y4asww3xC/dGNHj3z65UKRjT4F1vCH1ovRkLcvh05+mROd0v65+0cSKfFC6yEcaXFRUV7xhtxGBJOkHuYTWigswA0Jf5/GtHiiC3Cane9Z65gW63cX7h3giF2zRf+zP7zLyLJxZm6J4F0jin9VxtPvdcYGUTK/PueF7rzTffxMaNG2G1WnHFFVcAAL777jusWLECLS0tuPTSS5Gdvd+pVfjkk0/w1VdfAQDcbjeWLFkCjuPw2muvoampCWPHjkV5eTkAYOXKlcjMzMT48ePjeTtDwZ1GGxALkjKlp9Ln60C0NDITANaHQ83NqrLNUKPiSJARw2vd8+qXltzgWzfnNoc25jiPaE1PhAXxTrz+jZFQ15Mhuzh0YgwAFlYa0s+wnZUtF6I0jfrVunhep6SkBOedd94e72VmZuKMM85Afn7vxYRerxefffYZLr30UlxxxRXQdR3r169HU1MTOI7D5Zdfjvr6eoRCIfh8PtTV1Q0HMV6PJC4G2Z1kXSEDwDIAs3e+8PtXnu1KKTDOnNiyP79wovY/bGlbFZCwhhN4ZsiDXgLbv9absUBkLNxPSHnWv7yVNZqDyYvHNfLz89HZuWcz44yMjD4dq+s6VFUFy7JQFAV2ux0Mw0BVVVBKoWkaGIZBZWUlFi5cGAfrh5w7Kyoqkis7oReSWZCrAGwH4ALQuaq7e+tCq60qi+cN6ZMcKxLRL3wg6htf9KYI2+2ExDd41xsWVjTEY8MRjrmQL897vmvFtqBTLzDChv3hcDgwZ84c/OlPfwLP8ygsLERhYfRXQpZlPProo5gyZQra29tBKUVWVtI3udsA4CWjjYgVcRNkQsixAB5E1MX5OKX0bkJIOYB7EXWV+AFcRCndvNsx6wD8SCk962Dnr/T5aKnd/gqA6wF0AsDb3q4PLk1NK0y2ktdE9gv3Rk/wrjNVCg64bWYsEBiLYYsKhjA411Ja8Frnx9XtrlBCNCUKBoPYsGEDrrnmGoiiiJdffhnffPMNpkyZgmOP3ZUR9vzzz2PRokVYuXIlmpqaMGbMGEyfPt1AywfMXRUVFXHPtBkq4vJhJoSwAB4CcBSiq9i1hJC3ADwCYAml9AdCyBUA/g/ART3HFCOqQ/MIIVZKaV+6NX2LaBe4TABt60Khxq1K5JsxgmVKzG8qxgRYMbQ+Y2Z7s2e2XXIevI9EIOzHcyvuQ0PHNgAE5y64AWM8E3du/2bbx1i69kkQwoAhLE6bewUKsyajqbMWTy27E5qu4qx512GMZyI0XcPD796Inx9zOwS+/2vwULhN83U87UuVYPi0DYHdTznjEHOKeHj+e51rq2udXXlxaifZZ7Zs2QKXywVrT1Oq4uJi1NbWYsqUXb8SP/74I7KyshCJRNDR0YHTTz8dzz77LKZMmQKeT6ph1psBPGe0EbEkXquLwwBsppRuAQBCyAsAliBa9rxjFp4TQP1ux5wN4F8Ainv2PegPutLn00vt9hcB3IToDC36QmfnB7/OyCzmSOKNSd/pF/bMFfm0YhfLcn32C7+y+m+YMGomLjm6AqqmIKKG99g+LudQTM6fC0II6tqq8MQHt+PmM5/CR9+/jdPmXolUuwevrH4IYzwTser7tzCzqHxAYryz8s4y+LaZsUBghYSooDtWnJm/quvb7T84GrMYhjHsAcfpdKKurg6KooDjOGzdunUPt4SmaVizZg3OOecctLXtagKh6zo0TUs2Qb6soqIibpWaRhAvQc4BULvb6+0AZgG4BMC7hJAgAC92C8oBOBPRFfV4AL9A3//ybQLwJYAJABq3K4rvy2Bw1WGyXDa4W4gdg/ULB8N+VDV8i/MX/gYAwLE8OHbPXxwLvyv9K6yEsMONwDIcImoYETUElmERCPuxvvoTXHH83f2+j+bWlQGZfGZI8G5/UErBs4NvvRkr5omTc2WfpeEL27Y0hmUHtXJ/9dVXsW3bNgQCATzwwANYuHAhJEnCf/7zHwQCATz33HPweDw477zz4PP58NZbb+Hcc89Fbm4uiouL8eijj4JhGGRlZe3hili7di2mTp0KnufhdruhqioeeeQRHHLIIRDFRI9Y7ME/KyoqlhltRKyJS+k0IeQ0AMdSSi/peX0+ooKcDeAeSukaQsivAIyjlF5CCJkB4EFK6eE97o5qAFMope19uV6p3e4GcBeABgCqhRD2NrfnCjvLpsb85vrIXn7hQX3St7duxvMrH4AnJR91bVswKqMIp829cg8RBoCvt36Etz57HL5gJy479k6M8UxEu68Jz1TeA1WL4Oz512HNxv9hUv4cjM0u6ZcNu4J3ieOgZygfPn3M9QkjyDv4IVLTslL+0cZyrNEFk8MSSmkDIaS4oqJin8l/yU68Vsh1iLbJ3EEugCYAx1FK1/S89yKA93q+PxvAeELItp7XDgCnAnisLxer9PmaSu32dwAsAlATplR7uavztYtSUn/KDGH7xP76hfuKRjXUtm7C6Yf/AgXuYrzy8d/w/roXsGjmT/bYb+roIzB19BHYXP8N3vn8Kfxi0b1Itbtx7eIHAAAtXXXo9LfC48rD08v/AE1TccLMi+B29d4KYqgr7/oDR8QwgIQT5GIhL0MOWDr+I65TOYGzG23PcIMQcvlwFGMgfoUhawEUEUJGk2iPibMAvAXASQgZ27PPUQB+6Ok3ewaAyZTSAkppAaI+5LP7ec33AHQh6pvG58Fg3ZfB4MrB38qBUUH071KnNLw74bKOj+feY/GPPytbdhXEtKlPijUDLmsGCtzFAICSMfNR27qp1/0PyZ6CVm8D/ME9P7Nvf/YEFh32E3y4/nXMHX88Tpr9M/zni3/1ep5QuE3raPlbjxgnHjyR4tJ6MxbkC+6Uk8OHQQ0rHUbbMpyglL5UUVHxptF2xIu4rJAppSoh5CoA/0U0c+IJSunXhJBLAbxKCNEBdAD4KYB5AOoopbsH+FYCmEAIyaKUNvTlmj2tOf+OaIDPD0B7uqN9ZYEgFKZzXMy7gQ1lvrBDTkWKLQNNnbVwu0ZhQ91X8Lj2zLJq6apDuiMbhBDUtmyEqkVgFR07t2+q/xpOaxoynblQ1DAIISCEIKKG9nvNLt+GCA2+nTDBu/0Rz9abscDNp9jPVI4IvKh/1MJJfN+qOkx6hVLaTgj5hdF2xJOka795MErt9lMRdV1UA0ChIKRcnZ5xGR+DbnA7/cKemS5RHtrS5e2tm/HvFfdD0xWkO7Jw3sJf44uqDwEA8yaciPfXPY81G98Hy3DgWQEnz/45CrMmA4gGv/72zq/x0yNvhlV0oLGjGk8tvwu6ruHMedei0DNpj2s1t34YkMnnHJfg47Ey+fF1pblLcoy242AEtFDkX/iwjbXySV+FYTDnV1RUPGu0EfFkOAqyAOB3ANLRM+P2ZIez5Ci7fclAzreXX9jQ/sJDQSIG73ojx1JSc0T2MXEpXY41EV3RntGWN8DOxX5M+AiAUvrsrbfeer7RdsSbZC6d3i+VPl+k1G7/B4DbEA34hF/3dq0bZ7EU5QnChL6cQwXRN6RMaarOmtPvfOFkJZGDd70hMHLSrCYEhmd/Qo7Keda7vEZxkKT4I5Io6Lr+BcMwlxhtx1Aw7FbIOyi120sRrQLcCgAuhrXcmJl5iYNl95111EO1Nb9lQ9YR0DOnpgiCddj9seqNULhV87U/47eLSKrm6+Nt5dumZswoMNqO/vJieEW1z6kmRKl1oqNpWjPLslMrKioajbZlKBjOgswAuAbRgpE6ADhEEFKuTEu/1MLsaqJrpF84Eejy/RihwaWKmCDFHv1hqmNR9fi0iUkpbG+FPtnW5OwuSALPkGHouh5hGGZuRUXFF0bbMlQMW0EGgFK73QHgZkTHPbUBwOGyNf+kNM8Z32Ue1jlS/MK9kSzBu96YmXJa7RhXYVzn6cWTD0Jf1Wx1tuWSIcyVTyZ0XT/7tttue8FoO4aSYf1BqPT5vAD+jKgv2aoT1rpMZVP/5JjZHI984WSivvEFr535QkpWMQaMa70ZK5Oq04UAABihSURBVI4Up+VN6spu0HU9odP3jEDTtLtHmhgDw1yQAaDS56sD8JegJbXEZxu1KChlOr+tW/fhxrp1y422zQhULULr6x/uSLXUO5Ihk+JAWFjjWm/GijnihJxZvsI2TdPCB997ZKCq6lKWZW8y2g4jSPoPdF+o9PnWT8speVjlpFNByFYA2vJvX6mWLDbbqPRDDjPavqFiR/BuoG0zV23cik+31AAAZo3Jw/yxo/fYHogoeGnt12jzB8CxDM6YORVZTjv8oTCeWv0FghEFx00eh0k5HgDAkx99jlOmT4JTGpjrXmAtSdWarDemWsa4rd2Wtg+k9RrHc8M9oeeAqKr6BcdxZw2XCSD9ZUQIMgCovPwGok8ESxDNvKDvfvHMe6fM/rktw5nTp3S4ZGZH8M4uMgPKpGjo8uHTLTW45sgjwDIEj6/8DBOyMpFu3xULXPbDZmS7HLjo8Blo9vrx2pfrcdnC2fiqph5zCvMwOScLj6/6DJNyPPiuvgnZLseAxRjoX+vNX757N5ZVrUaanIJlFz8NAOgIenHlmxWo9TZglCMLD590K1zivq0n8v+4EOMzxgAAsh2ZePLUaKe8X7x9G35s2YLywrm4ccHPAAAPrn4a49LH4Nix8/p1L4cIOWliSOhaSr/wcgLvOPgRw49IJPKtIAilFRUVfemFPiwZ9i6LHXy7bTVFdBDihwDyAYBSnb6x5rFXmzprvzXStnjT3Pr/7Z15cBzVnce/r6fnPqTROdbl8W0jy9jGp2ywAeMLCF4gLCGbJWQJC5slxeYgJKnakFRiwiYkyxYhWRJcYEhhg2ED4TJyAF/yCT4kyx7JlkeSJY2kkebU3N1v/3hjRTayLVkjzfU+VV0aTfe8/vWo9O3Xv9/v/X47A2LkXTKaTIpurx8T83OhEhVQCAImF+ajrv3CTKQurw9Ti1hWYZHJAFd/EL5QGApBQDQmIybLEAiBJMvY3XgWN84cXbctpaActiB/uWotXvnyry547/n9f8Yy63zsfug1LLPOx/P7h14EphHV2P7AJmx/YNOAGJ/sPgONqEbNN17C8c5T8Ib96PI7cbTj5IjF+DxlysKcuyJLFbFQtPfKR2cWoVCoEcDyJ5980pdsW5JJ1ggyANTZa2WwIvhHwUSZSHJM/r/9L7zV5jx9MLnWjQ0dnVu8RuGwViEIo3q8t+QY0NzjQn84gkhMwilHN9yB4AXHlOSYUHeOiXRrrxuuQBCeQAjzKkpQ3+HACzsP4OZZU1F7ugXXTSyFSrz6mByhiqhiBIXgl5TPRa72wonnR6f34O7ZrK3R3bPXYnvTnmGfXxQUCMXCkKmMqByDggh4ZvcmfGf5A1f+8GUoFHP090k36GOBaNeoBkojQqFQkyAIizZu3OhNti3JJqsEGQDq7LVRsFZSnyEuygDFe4df+qDZUT/m1eHGi5gUZsE7TWKCd8UmI26cORkv7DqAP+46iJJcE4SLhr1p1hQEo1H85qPd2HPajpJcEwgBtColHrx+ER67ZTlKzTlo6OzGnLIJeOPQcbxc+xnszpEXRBOJdtRBMGe/C8WG+Ixenw9n/9B2hGMRrH/5m/jS5ofxYeNuAMC0AivytblY99KDWDW1GnZXO2Qqo8oyY7RmwaTQae7HjXmyP9px5aPTm2Aw2EAIuW7jxo0jKqdJCNlECOkmhNQPse+7hBBKCLnkIrBUJWt8yIOps9eGq6zVfwDwzwBWArADkD86uuWTFZUbgjPLrluTzgkIobBT8rmuPnh3KRZPrsDiyWzV7/vHTyFHd6H/V6NU4t5F1wJgBY02vvcJ8g0Xxqh2NDTh5llTcaS1A9bCPMwps+DlvZ/hoRWLR2SLUtAktPQmIeSSC8b3PfI6JhgL0eLuwL2vPYaZhZNhNZfiyVXfHjjmgW1P4Kk138P/1G7Gye4zuN66APfNvf2q7dEKauU3yCrLZt/HbbJRkba51pejv7//iCAIy5566qnglY/+Ai8BeA7A5sFvEkLKAawG0Dp6C8efrJshn6fOXhsD+6N+AMCK+M1p54m/7D92ds9fKJXTMsrr8Z2KhDwvhYzqxC+D9oXYpNTVH0RduwPzKy4stBaMRBGTWAPgA81tmFyYB82gHm09vn64AyFMLcpHVJIgACAgiEojbxqsJJro1V8Jo0BvRpffCQDo8juRrx/6/jXByCpnTswtwZKKuTjRdWEt6u1Nu1FlmY5ANIgWdwd+v+GneM/2KYLRoUubDheRiMLXxVXlGg9pGdVAKYjf798ny/KSp59++mrEGJTSXWB9NC/mtwAeB+vfmXZkrSADAz7lLQC2AagAoAKA/Y3bjx1s3LFVliUpmfaNlG7np6MO3l2OzbWf4b8+3IlNew7hzvmzoVUpUXu6BbWnmV50ef349fZdePqDT3HK0Y075lVe8PkP6mxYV8Ue6edWlKD2TAue3bEH10+3jtiWRNRCvmXqMmyrZ01rttV/iNVTl3/hGHfIh3CMTcb7Am4cbq/DtALrwP6oFMOLh7fhkcX3IRQLD8yyZSojIo36ngGBCPgn9U0Tc91q+6gHSxH6+vpe7+vrW/7MM88k+innDrDa6scSOe54ktFLp4dLlbWaALgRwP1gfflCAHBN+aJJy2auv1ehEFN+NVtH5xavWd2eFmUzE0GJ6trW60vXDrtq2rfe+Sn2tx5BX9CDAl0evrv8AayZfj0eefsnaPd2ocxkwfN3/BRmrQnHOk/h1aNv41frfoDD5+rwxPZfQyACZCrjwQVfxr3X3jYw7p8OvQ6Txoh7qtaBUop//+vPYOtpxk1TluBHKx9J6DV/EDrYci7HW5Guf2NJkmLt7e0bN23a9JNEjEcIsQJ4l1I6mxCiA/AJgNWUUk+8HdwCSqkzEecaL7ggD6LKWr0UwL+C1VHuB4DygmmFN1Xd9Y9atSE/qcZdgpgUpt1dL7rztKGE+otTHat2Sctiy4q0LCw0GnaGjrfZTF0lwggyTFKBUCjkt9vtD27ZsmVrosa8SJCrAPwNQCC+uwxAB4BFlNK0qRSX1S6Li6mz1+4D8BsAeQALiLU5m3re2PvcCz3ejpNJNW4IQmGn5Or5nTfbxBgA1EJ2NnReoZlTPs9b0SVL8uj9IeOEx+NpbWhoWJpIMb4YSmkdpbRoUF/OcwDmp5MYA1yQv0CdvfY4gJ8DkACUAEAg4o+8Wfv867b2IztSJdjn8Z2KhMcoeJcOqBRZVyl1gIWaGSXL+6e5pZg0uqjhONDV1bXr8OHDc955550vpKeNBkLIawD2AZhBCDlHCPmXRI6fLLjL4hJUWatNAL4JYA6ANgAxAJhVtsC6dMbaO1XKIdbYjhPdzk8CevKZcrSLPdKZBbl3tk4xT8vqzhtnI51927XHVaJSNCTblouRJElqa2v7/b59+x6z2WxpFRxPJlyQL0OVtVoE8CWw+hfdiPuVc3T5utXzvrIh32iZNt42dXS+5jWrO7ImeHcpluXf115mKk/5BqdjTWe01/u26pAsqpUp0x08EAi4Wlpavr1169aMbkg6FmS0IBNC1gJ4FoACwJ8opb8khLwEYAWA8yuDvk4pPXq5caqs1deCBfsEAAM+qRWVG5bMKJu/SiBjH2CJSWHa0/Wi25yF/uKhuLHoga4ifVFxsu1IBVySN7BV2BsQtaqkrkyjlNK2trZDDQ0NX92/f//pZNqSrmSsIBNCFAAaAdwC5uA/BOArYEnj71JKt41kvCprdT6YC2NmfLwYAEwqrpywfNatd+g1pjETh1C4R/K5XvFnq794KNZMeLgvV5OTl2w7UoV+KRh5FTv7FHqlJSnn7+/31NfXv9Dc3Pxzm82W9TUprpZMFuSlAJ6klK6J//7D+K4ZuApBBgZcGOsA3A22SsgDAApBFK6/5ktLppXMWakQxIT6dT2+UxEE342q07Dn3Vhye9m3/TqlNuV8p8kkIkelzdLHnTCKZeN1TkopbW1tPXr06NFHA4FArc1my0xBGScyWZDvBrCWUvpg/PevAVgMwABgKYAwWN7iE5TSERWqqbJWzwDwEFhqXAfis+XCnNKclZUb1uWbJoy+wgx48O5y3DXxu1ExwTe/TCBGJfpq9JO2mImMecAzEAh46urq/tTc3PwLm8028gpRnC+QjWlvPwRzOywEyzf+wUgHqLPX2gD8GMC7AEoBFAFAj6fd80bt77YcaPxoazgaHNVjW0fnax6j8Pmoy2ZmIoQSiYvx0IhEQb6uWlWh9yjsY3UOSiltaWk5umPHjtubm5u/z8U4cWTyDHlIlwWl9KlBx6wE8D1K6W1DDjIMqqzVZWBV42aABfyCAKBV6VUrq+5cWVEwbQkhwrAzInjw7sqI0AXumvRoVrc6Gg5vh2rtPblBayLHdLlcnQ0NDa+0trY+bbPZhiruwxkFmSzIIlhQ72YA7WBBvfsA9FFKO+NpY78FEKKUPjGac1VZqwUwN8hXwQoUdQCQAcBaNMuybNb624xa8xVTtHjwbnhoiNl9h/WhlEnzSmVqQp+32nP6ykebJhkIBFz19fW1Z86c+TmAA9xXPDZkrCADACFkPYD/Bkt720Qp/QUh5GMAhQAIWOeQhyml/kScr8panQMW8LsBgAuAm9khkOqZ6+bPKJ2/UiWqhwxEuX0NERJ8nwfvhoFRKOleP/FrRcm2I12oDZ1orzd1FAuCMOL655FIJNDY2Hj0xIkTf5Rl+W3unhhbMlqQk0U86PcNAMVgs+UIAKiVWuWymesXTbJULlMqVAPFGHjwbmTkiVM6bim/uyTZdqQTx8JnHPsNZ8wKhWJYfQglSYrZ7fYTx48f3xoKhV612WxtY20jhwvymFFlrVYBWAVgA9gMvRPxbAy92qReOnPtDRMLZyzp6XnLZ1Ynps1StmBRzW5bUXprRnbRGEsaI+ecH+tO6ERRvKT/nVJKHQ7HmSNHjrzv8Xj+F8BJ7p4YP7ggjzHxmhirAawF62LgoDSaSyXXCp1S4ayeUqgqNZsWKgQh5WsupwoVmoUtSyfclHWlNxNBW7Tb/Z76c0FUKS/o+EoppT09PfYTJ07sczgczwE4ZLPZRt0EgDMyuCCPE/GVfuspja2hknM+aLgBkOoBwKhRaxdNKqsuM+cs4sJ8ZabpbrDPL15qTbYd6UpPzO1/U9wfETXKPFmWJYfDcbquru5YX1/fKwD+ZrPZrqqtEmf0cEEeZ2ZXzJ1IJec/ALE5YC6MLrBSnzCoVZr5E0sXlOflLFSLoumyA2UxlcY19tkFc63JtiOd6Yl4vM/1vnX2eENdg9fr/SuAD3nALvlwQU4SlaWWYjA3xgqwFLkuAFEAEAghc8oss6YW5S82aTVZXWJyKObnbGidljeDfy9XgSvkDX7SfrD+z7b36txh318BfGqz2dzJtovD4IKcZCpLLYUAbgLLl1YC6AUwkIY3MT/XMrukeHGhyVAlsIJJWc/S/HvbK0wTs7705khw+JwdO5prW7c117h9CD4LYI/NZktIuicncXBBThEqSy16AAsA3A6gAKz2shPxduYmrVo3r7zkuvK8nIUqUUxacfxUYEXh/Q6LwZKUqmbpRDAa8tc5bI3bT+9pae5rawTwNoDPa5r2jqh2C2f84IKcYlSWWhRgtTbWApgN5mfuxiB3xkxL4eRJhXlzCgy6WdmYu7za8lCvWWtOyaazyUamstzi7ji96+yhll32Qx0ylesBvA/gVE3TXjnZ9nEuDxfkFKay1FICturvZgAiAB/YCkAKABpRVFaWFs+syM+9NlermZwtucy3lT7q1at0POg5CHfI23P4XP3pD5t223sDrm4ANQAO1TTt7U62bZzhwwU5DYi7M2YDuBGsiBEFq8c84APM1WkMs0uKZ5eac+bo1aoJybF0fLiz4jthpUI5rBVnmYwv3N9nc55t3mM/3HnMcaoHwAEAuwA01TTt5X3s0hAuyGlGZamlAMA8sFlzMVjKXA9YfWcAQEmuqWBqUf6MIpNhhlGtKsuomTMFvWfS4xl1SSOhN+DuONXTfGa3/VC3zXnWB9a9ZjuAYzVNe31JNo8zSrggpymVpRYCoBysrvNKsML7UbCZ80B7eKNGrZ1eXDCtJNc03azTThEVgiYZ9iYKBdTBuyc9pr3ykZmBTGW529/bUtfVeHbn2YPOdm9XP5jbqhbAQQDnapr28n/iDIELcgZQWWoRAUwDmzkvARNngFWb8yHuc1YQIkwtLqioyMuZXmDUT9cqlWkXGFOTHM8G68MZXZ60PxJwt3u77Q3dTV07zx5yukPeCFiRqj0A6gG0cxHOTLggZxiVpRYBrIvJNWA1mivASo0GwGbPA77FQoM+x1pgthYa9VazTmtVK8WUrzFsECw9t068vzDZdiSS/kjA0+Httjf12tsOnjvuanF3nK8h0QRgL4CTNU17e5JoImec4IKc4VSWWnIBTAewCMAcsMpzAOAFmz0PpEIVGHQ5FfnmiiKjvjxHqynXqZTFqeasNYuTOleX35PWQcshBFgCe4oJA6gDa6Zgq2nay7s3ZxlckLOIylKLCoAVwFQw98bkQbv9YCI9MIPWKpUqa4G5rNCon5CjVVsManWxRikWJFOki5XXnFtZdvu4dVUeLcFoyOcMuDo7fT0dZ3pbu487TvU7/E6ACXAEzAVxFIAdQCfPFc5uuCBnMZWlFg2YS2MygCowoRbBXBxhMJEOIO6DBgClQiGW5poKC436YrNeazFq1MV6lcoyXsHCcvV1LdUlq1Ku9KYky5I37HP2BtxdDl9P11lXe1dDd5Pf4XcqwL5TLsCcK8IFmTNAPDg4ASx7YzqYQE8AExMS/9kPJtTRwZ/NN+hMhQZ9vkmryTWoVWa9WmnWKJW5GqVoVioUCWtLNUW33L6geJk1UeONhEgsGvJH+l2esN/lCnr6nP0ul8PvdLW42n0t7nZJorIe7HsC2HfVCsAG4AxYUM7BBZhzObggZxiEkE0AbgPQTSmdHX9vLoA/ANCALcX+N0rpweGMV1lqUQIoAst5LgfL5pgUHwtgAiSBddsOxbcLREctKpSFRn1urk5rNmk0uVqVaFSJok6lUOiUCkGnVCh0okLQiYKgvZI75BrDLfaqwvnW4dg+HCiliEqxUEgK94djkUAwGg4Eo8FAIBoK+COB/t6Ay9Pu7XaddbX5egNuANDGr/38DYrEr/ksWBCuDaz7eFdN015e4J0zIrggZxiEkBvAZrCbBwnyRwB+Syn9IN749XFK6cqrPUc8BzoHTKQLAFjAMjssYA1kBfxdrAQw90cY7JE9CnZT+MJMkQDEoFFrDGqVTq9W6TRKUSsKgkioeo5epfXpVGKv1TBHKjNM0ikEhaAggkIggqAQBAWllEpUlmKyFIvJMUmSJSkmS7GoxF5H5VgsKsekmBST/JFAyBX0BpyBvkC3vy8gUUkAq7R3flODdQ+X49ehiNvvAGvF1Qa2GMcFVgDKw9PQOIlgxF1oOakNpXQXIcR68dsAztd+yAF7fL5qTrQ7KFiOsxvskXyAuFgbAZgHbSVgYm2Kb0Ywob5AlCkg+EJh4guFJbBZtweAbFLmebUK2qIQxPZ67KfAfjroswL+LvyXen3+JwZ9Tg020y0Au1H4Bl1TL4B2sDRBV3wLJEJ0CSHlADaD3cwogBcopc/G9z0K4Fvxa3+PUvr4aM/HSS/4DDkDiQvyu4NmyLPAlteeF6ZqSmlLsuyLi7YKgG6IzYC/uwXUANR6MWeKUZnnEoggg81WxfhPAjbbjoKJ6uDX0YteB8H834H4NvC6pmnvBf7wsYQQMgHABErp54QQI4DPwBrhFgP4MYBbKaVhQkgRpZQXBsoy+Aw5O3gEwH9QSt8khNwD4EWwjthJIT7DPu/GyKq2QZTSTjC3ByilPkLISTB3zzcB/JJSGo7v42KchQhXPoSTAdwP4K346zfAFolwkkz8SWYeWJW26QCuJ4QcIITsJIQsTKZtnOTABTk76ADr3QewdlFNSbSFA4AQYgDwJoDHKKVesKfVPLBaJN8H8HqqrZLkjD3cZZFhEEJeA6v+VkAIOQfgJ2CPw88SQkSwFK2HkmchhxCiBBPjP1NKzz+5nAPwFmVBnYOEEBks4MhrWGQRPKjH4Ywj8VnvywD6KKWPDXr/YQAllNL/JIRMB/A3ABWU/4NmFVyQOZxxhBCyHMBusCJC51PwfgRgB4BNAOaCZYZ8j1L6cVKM5CQNLsgcDoeTIvCgHofD4aQIXJA5HA4nReCCzOFwOCkCF2QOh8NJEbggczgcTorABZnD4XBSBC7IHA6HkyJwQeZwOJwUgQsyh8PhpAhckDkcDidF4ILM4XA4KQIXZA6Hw0kRuCBzOBxOisAFmcPhcFIELsgcDoeTInBB5nA4nBSBCzKHw+GkCFyQORwOJ0XggszhcDgpAhdkDofDSRG4IHM4HE6KwAWZw+FwUgQuyBwOh5Mi/D/ApAQHFjMOdwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot pie with fbi\n",
    "plt.pie(fbi_code, labels=fbi_code.index, autopct='%1.1f%%', shadow=True, startangle=140)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Resolução Kaique\n",
    "\n",
    "Para responder à pergunta, é feita uma contagem dos registros na coluna \"Primary Type\" e dessa contagem total é recuperado o maior número de registros e o valor que possui esse número. Após isso, é relacionado o número obtido com a quantidade total de registros no conjunto de dados para obter a porcentagem. Para visualizar a relação do tipo de crime mais comum com os outros tipos, é possível construir um gráfico de barras com as contagens dos tipos de crime.<br> \n",
    "O resultado mostra que o crime mais comumente reportado é o roubo e que só esse tipo representa vinte e dois por cento do total de incidentes, o que é uma grande parcela. Além disso, é possível visualizar que os crimes de roubo e de agressão são os mais reportados e que possuem uma grande diferença na quantidade em relação aos outros tipos de crimes."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Tipo de crime mais comumente reportado: THEFT\n",
      "Porcentagem desse tipo de crime em relação ao total: 22.61666%\n"
     ]
    }
   ],
   "source": [
    "total = crimes['ID'].count()\n",
    "most_common = crimes['Primary Type'].value_counts().idxmax()\n",
    "print('Tipo de crime mais comumente reportado: ' + most_common)\n",
    "most_common_v = crimes['Primary Type'].value_counts().max()\n",
    "print ('Porcentagem desse tipo de crime em relação ao total: {:.5%}'.format(most_common_v/total))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1792d4fef60>"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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JEyRNeG3Ouy02wczM2ssdg+4xu7gu/40i/OfsfO1+HWBx8j0GwGRgWFmApHWBWTlcLhHxWkT8LiL2Be4F6sW8b4/pwAo1aQNJNy+2ol6blyXdIPlkTd4TaONeg4g4JyKGRcSwgX18z6yZWVdxx+B9KCJmkGKJHy6pH3AJsJ2kEVC9GfF04Kf5/Y6V6/aSBgDrseBRBe8FPiZptVzuMNKTCK0GW7kVWErSfnn7PsApwAX5HomqfO/ECnRdrHIzM2uRf3q9T0XEPyXdD4yKiIsl7Qb8QtKZpDCpFwNn5OxbAWdIepfU2ft1RNy7gPW/LOl/gT9JWgyYBYyOiPda3D4k7QGcJem7uV1/Ao5psMkJwLWtlL3k0CFs7JkPzcy6hKMrWo8zbNiw8JTIZmatk3RfRAxrO6cvJZiZmVnBHQMzMzOrcsfAzMzMqtwxMDMzsyp3DMzMzKzKjytajzN5+mQ2vXDTlvJO2n9S25nMzKyqzREDSWtKulbS45KelHSapMXzuuGSZkiamF9/yeljJT1fpJ+Y08dJerRIv7LI/5akVYp6ZxXLx0qaLOmBvN02zcqraf8YSWe0VU8ua+eabQ+VdFZeHiLpNkmP5WPx3RxBcJ46im1/K+krNWl7SrpOUl9JlXDK60sKSV8v8p0taZ+8LEmHS3ok7//9kk7OEwbV7ms/ST+V9ISkByXdXdknSc9JWr7IO0LSNXn5q5J+npePl3RoTbl9Jc3Jx3hy/vfQPL9BpazK9+CRyuddlP1q8RlNlLRhW/ttZmbdo2nHIJ/42gqde0cxve+IIv3UIv2oIn3vIn3PIn0a8O06bfgoMBLYMiI2A0Yw7+x7jcprpG49wKXAqJq0UcClajvscT0Ny6uT92XgMKWgSrUOBnYAtsn7vzUpJPMSdfL+GFgJ2CSHbt4dGNCkje0xMx/jIcDOwG7MO43x7Xk65y2Bz1c6b9klxWe0RUQ8mtOb7beZmXWDtkYMWgmd21nOA/aSNLAmfXVgWkT8O7dhWkS80AX1XAmMlLQEgKTBwCDgTtoOe1zPzcBmldEJScsAw5kbMbH0Ein88r511h0DfC1Pk0xE/DsiflQ7rbDSVMhjgG9WYi9ExIsRMd8oyoKKiJeBg4Bv1Fn3Fimc8hq16+pott9mZtYN2uoYtBI6d/tiiLj8BXlYkV4O0V9SpJ9UpM8inbT/t6YNNwNr5SH8syT9V836RuU1UreeiJgO3APskpNGAZdHmhqyrbDH84mId4BrgC/kpN2BWyLizQbt+jFwRGV4HkDSCkC/iGglPsEHgSkRMatJnjsqxwo4u4UyG4qIx0jRIFcs03OHa11Sh6pi75pLCYsX6+bb73pURFecM3POgjTdzMyaaKtj0Ero3PJSQnmJobyUcFORXg79H1FT7unA/uXJNp/otgIOBF4FLpc0psXyGpmvnqwc/i+H/ZuGPW5ST6Py5i8k4glgIrBXkawyj6RP5RPr05K2blJvI9tXjhXwtQ5sX6ts3w6SHiCNAlwdEa8U62ovJVSiSTba7/mU0RX7DJjv9gozM+skbXUM2hM6d4FFxL+A31Fz7T4i5kTEuIj4P9IQ/ue7oh7SL/ydJG0J9I+If+T0NsMeN/BXYLCkzYAPAze20bQTSJcnlNv5GvCupLXz+z/lk/rDpLDMpceBD0hauo06OoWkDYC38kgLpHsMNgM2A74pqbXHBpJ59tvMzLpPWx2DlkPndqKfka5f9811bijpg8X6LYCnO7seqI5OjCNdaih/3TcNe9xIjkT4e+Ai4Pryl3KD/JNJHa5di+QfA7+UtFyuW8CSdbadmev5uVKoZiQNkrR3szo7It838UvgF3Xa8QjpuBzZankN9tvMzLpB07vBOxA6txWXSJqdl6fVPMlAREyTdDXpJkdIT0H8Ij9q9y7wBOmyQkvlNVKnnopLSU9ijCryzlbzsMcAYyTtXrz/SEQ8l8s7DDi8lXYBxzPv/Qy/APoD90p6m3SPxB2kG/xqHQX8CHg4H5M3ge+2WG9prKRKe98l3U8yIN+bsDjwH+BC4LQG258FPF4Z6SDdYzC8WH8QML1mm9r9bmjIikOYsL+jK5qZdQWHXbYex2GXzczaRw67bGZmZh3hjoGZmZlVuWNgZmZmVe4YmJmZWZU7BmZmZlb1vu4YFBH9HpT0e0lLSRos6cGafNXH6yRdIGlKEenv/4p84yTVTlQ0XNINxftd89S7D+ftT67TrjGaGzHwIUkH1EmvvDYptjtM0tuVOQmK9K0l/VUpUuQjkn6d97VpecU+1Y0KWXusJG0n6Z5cxyOSDizWlY8o1u7vHkqREDfK7zct2vNacbz/0oE6G0bVbOiFf8LY5Vp/mZlZy97XHQNgdp5Cdyjp2flWp/E9Is8QuAVp6uMPtLKRpKGkuQn2iYiNgaHAUw2yX57rGA78SNKqZXrxeqjYZjRwL7BHUeeqpEmQvhMRGwIbk2ZIHNBCedBiFEdJq5Fme/xaRGwEbAccJOnTzY5J0e47K/VExKRiauXryMe7dg6JFutsFO3SzMy6wfu9Y1C6g7mBm1pVmSGwUeCiWkcCJ+TZ+4iIdyPirGYb5JgATwLrNMsnaT3SZE3HkU60FQcDF0bE+FxeRMSVOYJhK5pFhSwdTJqx8h+5nmmk/W0WIbISFfJjwFeYvwPSllbqbBTt0szMukGP6BhI6kuaLndSi5uclGfpew64rCagTzNDaXH2vaJt65KiCT6Rk/aqGfrvn9NHk37F3wFsWAyft1Vno/KANqNCluaLEAlMyOnN7A7cmKMpvqYUR6JVrdTZKKqmmZl1g/d7x6B/PsFPIIV6/g2tRTmsXEpYjRQUadsuaNteuW2XAgflgEcw/9B/ZbrmUaROynukKZe/UKfMehqVV2olimOjCJFtTX05GrgsL1/GvKMdbWm1zkbRLucWVIRdfvUtz9ZpZtZVmsZKeB+YnU/wVZKmAyvU5BsITKndOCJmSRpHurZ9Vwv1TSaFeK4Xh6DW5RFxSAv5UIqu+EHgFkmQ4g08BZxZ1HltK2U1cA3wM80fFbJUiRB5XZG2FVB7z0LZ7hWBHYGhkoIUIyIkHVlnRKKeluqMiH9JqhftssxzDnAOwLBBfdwzMDPrIu/3EYP55AiIL0raCSBfm96F+a+pVy5BbEPrIaJPAo5RCimMpMUkfasTmj0aGBsRg/NrELCGpHVINzvuL2mbot375Bv3WtIkKmTpTFKgpy1yHSsCP6F5hMg9gYsiYp3c7rVIHbDtWmxae+qcL9qlmZktfD31j/B+wJmSTsnvvx8R5cn/JEnHkX6Z30oauq/4o6R38vJ40skLgIh4QNKhwKWSliINef+xnW3bS1J54vwf0vB+bUjhq4FREfETSaOAk/N9B+8Bfy3aPF95EVFv9GO+qJCliHhR0j7AuZIGkIb5fx4R1xfZjsv7X/EEcGJNUVcBXyLdK9FUi3VW8jaKdjm/QR+CsQ6iZGbWFRxd0XocR1c0M2sfObqimZmZdYQ7BmZmZlbljoGZmZlVuWNgZmZmVe4YmJmZWVVPfVzRerFJz89g8FHtfYp0rqknthI3ysysd+o1IwaSTi2f0Zd0k6RfF+9PkfStHDZ4dk18gv2KfB/KIYhrQx3PFyI6p68p6VpJj0t6UtJpkhbP64bnsj5TlHODpOF5eaSkf0q6Xym880E1dQ6W9JykxWrSJyqFci7DUUvScbkdj0m6XdKQYpupklZqcOyulTS+eH9scWzmFMvf7ECdVxXv95R0Qf1P0MzMFoZe0zEgTYm8LaQZDYGVmDeYz7bA3/LykzXxCS4q8lVCENfGDJgvRLTS/Md/AK6JiA8CG5AiLJ5QbPcccGxtYyX1I00B/JmI2Bz4EGl2w6qImAo8C2xfbLcRMCAi7qkp8uC8j5tHxAbAj4HrJC1JE5KWB7YEllcOXx0RJxRhl2cXx+n0DtQ5rOwsmJlZ9+pNHYO/kTsGpA7Bg8BMSSsohSzeGPhnswLyiX5PYAzwySYn1UqI6B2BtyPifICImEOa2e/LlREFUlyGGZI+UVPGANKlnul5239HxKN16ioDKEHjIErfAb4REW/l8m4mdZb2brjDyeeB60kBlNobdrmVOk8GjmlnuWZm1kV6TccgIl4A3pW0NqmDMB64G/goKdDPAxHxn5x9vZpLCZVf5B8DpuTpl8cBn6qtR/OGiJ4v7HBEvEGKFLl+kXw8cFxNvtdIwYeelnSppL1rLxlkVwC753oB9mJuNMRKm5YFlq6ZNhpaC7tcCRd9Ke2IrNiOOq8AtpS0Pk2oiK44560ZrTbDzMzaqdd0DLLKqEGlYzC+eF/GH6i9lFCJC9AsBHG9ENGNwg7Pk14pv+iAVNK/CuwE3AMcTgqSRE2el0hRDHdSClb0TkQ82MZxqNuO+VZKq5I6MHdGxGOkjtXQFstutc45pOBVRzfbKCLOiYhhETGsz1LLLWATzMyskd7WMajcZ7Ap6VLC30kjBuX9BXVJ6kMaVv+epKnAL4Bdc3AgmPda+zfy6EMl7HBZzrLAWswf8fEE6txrEBGTIuJU4BO5/noqlxPqXkbIoxRvSlq3ZtWWNAm7TBp9WAGYkvd5MC1eTmhnnRcDHwfWbqVsMzPrOr2tY/A3YCTwWkTMycP1y5M6B+ObbgkjgPsjYq0cgngdUqTB3ZtscyuwVOWphty5OAW4oHLdvSJff18B2DznXabydEK2BfB0g3quIl3WmO8yQuEk4HRJ/XP5I0jhk3/XpP2jgV0q4aKBrWjffQYt1RkR7wCnAofOV4KZmS1UvW0eg0mkpxF+V5O2TERMK9LWy5cFKs4j/dK9uqa8q4Cvk37xziciQtIewFmSvkvqiP2JxjfbnQBcm5cFHCnpV8Bs4E3STY/16vmXpL8Dq0bElAZl/4LU8ZgkaQ7wErBbRMwu8jwg6b28fA/pF/zfi3qmSHpD0jYRcXeDetpbZ8VvqLnPopFN11iOCZ6LwMysSzjssvU4DrtsZtY+cthlMzMz6wh3DMzMzKzKHQMzMzOrcsfAzMzMqtwxMDMzs6re9rhiXZJWA34OfBj4NzCV9Ez9f4CHgUeBxUmzGn4lIt7JcwwcHhEjJY0BzgdGRMStucw9SAGUvhARV0oal/NPyJMF3RcRn8959wRGRsSYok3XAqtExEeLtLHArIg4ucF+3A0sAQwE+gPP51W7k6ZwnkmadfB1YL+IeDpvN4f02GbFZRFxoqSRwA9JHch+wGkR8avcjgOAV0nfoWMi4rqiHfcDD0XE6CLtI8BpuX1LAJdHxNg8u+JvSJM+9QOmRsR8U02XFjTscmdyCGczW9T0+o5BDox0NXBhRIzKaVsAq5IiFz4ZEVvkyYluAb4IXFKnqEmkCYFuze9HkQIkNTJM0pCImFynTZWIhrMkfaDJ3ATziIht8vZjgGERcUhRJsAOETFN0vdJcwYckFfPzpESyzZUojtuHRHP5UBTg4ssp0bEyZI2Bu6QtEpEvJffLwZ8XNLSEfFmzn8h8MWIuD8fyw1z+g+AWyLitFzvZq3sq5mZdQ1fSoAdSPEFzq4kRMTEIj5CJW0OadKfNRqUcwewtaR+kpYhxRiY2CAvNI8quCARDVsxnsb7UdFSdMeIeBh4lzRxFMCXSBM+3Qx8tsi6CvBi3mZORFSmRV6dFHq6Ut4D7d0ZMzPrPO4YwFBqIiDWk0MsbwPc2CBLAH8BdgZ2I0VGbKZZVMEORTRsh12Aa4r3/WuiSe7VanRHSdsA75EuK0CalvnyOm0/FXhU0tWSDipCVp8J/EbS7ZKOlTSoc3fVzMzawx2DtlWmR54OPNPGL9rKL/y6wYxq1I0q2EURDStul/QKKe5DOS10GQBqi4i4HNqM7nhYPi4nA3vl6Z8/DLya7124ldTxWSGX9QNSQKmbSaMKN+b0m4B1gXOBjYB/Slq5tuEOu2xmtnC4Y5AiIG7VZP2T+fr7+sBHJH22UcaIuIc0ArFSPqm3pV5UwQ5HNGzBDsA6pH3+QSsbNInueGruRGxfE5Z6o9zuJ4Fly20i4smI+CWps7G5pBVz+msR8buI2Be4l3RMatvhsMtmZguBOwZwG7CEpMqNeEj6sKT/KjNFxIvAUdT8wq/jaBrfOzCPBlEFFzSiYVt1zs7aCbFUAAAgAElEQVT17SdpYKN87YzuSL7M8AVgs6Ltu5EvJ0j6dL7RE+CDpBGTf0naUdJSOc8AYD3gmQ7unpmZLaBe/1RCEQHx55KOAt5m7uOKta4Bxkravkl5f25nE6pRBSUNpklEw5x0nKRDi/VrtrM+IuJFSZcCB5MeR+xfE03yRlKkx5aiO2YfB56PiOeLtL8Cm0haHdgXOFXSW6SbFfeOiDmStgLOkPQuqaP664i4t1n7HV3RzKzrOLqi9TiOrmhm1j6OrmhmZmYd4o6BmZmZVbljYGZmZlXuGJiZmVmVOwZmZmZW1esfV7Se5/6Zb7Ha7c3CUPRcL+2wRduZzMy6kEcMFmGS5uTYBw9Kuj5HbaysGyLpNkmPSXpc0ncrExBJGiPp1bztZElXFpMQjZX0fF73iKRfVmIoSLpA0pQi5sJddcp7RNJhOf2TksYX9fbJebZd2MfKzMwSdwwWbZUYCEOB10gTGiGpPylA0okRsQGwObAt8D/FtpfnbYcA/yFN1Vxxap4mehNgU6CcJfKIIubCtrXlAR8DjpW0VkTcTJpN8Ss5zzeAeyPirs7ZfTMzay9fSug9xgOb5eUvAX/LJ2Yi4i1JhwDjSNEOqyT1BZYGXq9T5uLAkg3W1RUR0yU9QQq3/CxwGHCnpPHAIcDW7dgnMzPrZB4x6AUk9SEFLqqEgh5CTajpiHgSWEbSsjlprzxN8vPAQOD6InslsuKLwGMRUV7wP6m4lHBJnbasTepMPJDrfRH4OanjcnwO91xvH6rRFd+b8a/27L6ZmbWDOwaLtkoMhOmkk/stOV1Ao7mwK+mVof/VgEnAEUWeyqWEVYClJZVBnspLCXsX6XtJmgw8BZwWEW8X684E+kTEBY12pIyuuNhyyzfKZmZmC8gdg0Xb7HwCX4c07H9wTp8MzDNntqR1gVkRMbNMjxRM43rqh0J+hxRwab51dVye71fYHjhF0mpFOe/RuKNiZmYLkTsGvUBEzAC+CRwuqR9wCbCdpBFQvRnxdOCnDYrYDniyNjE/TbBtvXVN2jIeuBj43/bsg5mZLRy++bCXiIh/SrofGBURF0vaDfiFpDOBPqST9RnFJntJ2o7UeXyOeUMuHyZpH6Af6V6Bs4p1J0k6rnhf72bCnwD/kPSj2hGKVmw+YCkm+Hl/M7Mu4bDL1uM47LKZWfs47LKZmZl1iDsGZmZmVuWOgZmZmVW5Y2BmZmZV7hiYmZlZ1SLzuKKkAH4bEfvm931JU/beHREjc9ruwA9Ik/28A3w3Iq7Jj+x9LKd/AHg0F3s8cBVwLLA/aRKe54FDImJyLnMqMDOvex3YLyKermnbBcD4iPhVkbY7cGBEfErSHNLsghWXRcSJksYBh0fEhLzNYOCGiBgqaXheV9m3XYEfkuIaKOc7XNJY0sRFJ9e0qW6dxfpmx2RkLv/KIv+siFgmt/HhIj/AzyLiouJYzcnp/wuclpfXBmbk17SIGEEDM2dO4tbb1mu0epGy044tTxFhZtYpFpmOAfAmMFRS/4iYDXyCdBIHQNLmwMnAJyJiiqQPALdIeioiKlEHB5NOeFsU2x1CmsRn8xxs6JPAdZKGFNP67hAR0yR9HzgOOKCmbZcCRwG/KtJG5XSYO0Nhh0gaSpqD4NMR8UjuFB3YxmZN62zjmIxso+wnm5S9Q0RMK95vkcu8gJrOhpmZLXyL2qWEPwOfzsujmXviBTgc+FFETAHI//6YeWMA1PMd4BsR8Vbe7mbgLmDvOnnHA2vUSf8LsJGk1QEkLQWMAK5pYZ9acSRwQkQ8ktv4bkSc1cY2ZmZm81nUOgaXAaMkLUkKMXx3sW6+iILAhJxeV440uHSOPNjKdrtQ52QfEXOAPwBfzEmfBW4vZv3rX0QknChpr2LzSyrpwJ8aNHVonX1rS7M6W1FGUZxYs269mrK3L9bdntPuxszM3ncWpUsJRMQDeeh7NPOfROtFFGwWZbCZ2u1ul7Qq8ArpUkI9lwInka6pjwIuKtY1G9bfu/Yegw60t54FunxBiqI4zz0Gxbr2XEpoiaQDyZdHVlllkframpm9ryxqIwYA15HuJbi0Jn2+iILAlsBDjQqKiDeAN3PkwWbb7UCKYDiZdHNjPX8DVs/3OmxL41//HTEZ2KoTy3vfKcMuL7/8ovi1NTN7f1gU/8KeB/wgIibVpJ8MHJ1/dVd+fR8DnNJGeScBp+cIhOSIhNsBvysz5RseDwX2kzSwtpAcvvgK4ELgT8WNi53hJOAYSRvkNi4m6VudWL6ZmfUSi9yYbEQ8x9xH4Mr0iZK+A1yfQw+/AxwZEbXXx2v9AlgBmJQf8XsJ2C13BGrreFHSpcDBpEcHa11KutnxqJr0/jXX6W+MiNo8DeVLKIcCl+YbGwP4Y5HluLy+kn/NBa2zDevVlH1eRJzeSWUzYMCm7LSjgyiZmXUFR1e0HsfRFc3M2sfRFc3MzKxD3DEwMzOzKncMzMzMrModAzMzM6tyx8DMzMyqFrnHFW3R98ILLzB27Njubsb7mo+PmXVUrxsxkDQnz9U/WdL9kr4labG8brikGTXz/I/I61aTdJmkJyU9JOlPkjaQNFjS7Jpt9svbTJU0Kddzcy7j7pznGUmvFtsMzvlXkjRO0s417T5U0lnN6qvJP07So7nuv0nasE76vZK2qNnuQ5KiTv319n/zog2vSZqSl/+S2/lgsf3Wkv6a635E0q8lLSVpTM1xmChpk876vM3MrH1644hBNUaApFVIMxguB/xfXn9HRMwTVliSgKuBCyNiVE7bAlgVeJYWYgNI+hFwTERsk7cfAwyLiEOKeiqLl5LiKdxUlDOKuZEgm9VX2jsiJuQ4AyeRgjeV6f+d0z9RbDMauDP/e1Mb+79scSwvoAibXJlhMi+vCvweGBUR43N5nwcG5CyXl8fBzMy6T68bMShFxCukwDyHqDgr17ED8E5EnF1sOzEi7mhHdX8F1m8x75XASElLQPUkO4h0wu6IRnXPEyY6H4M9gTHAJ5WiVMKC7//BpE7F+LxtRMSVEfFyu/fEzMy6VK/uGABExFOk47BKTtq+Zlh7PdoOa9wszHDFSKA2fkOjNk0H7iGFcYY0WnB5zJ2mspX6Sp9pUHdtmOiPAVNymOlxwKdyekfCOpfa2n6vmv3pX5tB0oGSJkia8NZbby1AU8zMrJneeCmhnnK0oN6lhLa2bza0f7tSjIUHaBySuZ7K5YRr879fbrG+0iWSZgNTgW/UpC8N9CFFiqwYDVyWly8D9gX+0I42d1SblxIi4hzgHIBBgwZ5Hm8zsy7S6zsGSiGV5wCvABs3yDaZNMTeETtExLQObHcN8DNJWwL9I+IfHShj74ioF1Rgb+B+4ETgTOBzkvqQrvt/VtKxpM7SipIGsGD7D3PDQl+7AGWYmdlC0KsvJUhaGTgbOKMYpq/nNmAJSQcU235Y0n91VdsiYhZpOP880uhBZ5f/DmkE4yOSNgZGAPdHxFoRMTgi1gGuAnZnwff/DGB/SdsU2+8jabXO2h8zM+scvXHEoBJuuB/wLnAx8LNi/faaN2Tw8RFxpaQ9gJ9LOgp4mzQ8Xwll3FVhhi8lDeWPqknvlPoiYrakU4DDSZcVrq7JchXw9Yi4uI39b6uelyWNAk7OT4K8R7ohsnKZYi9J2xWb/E9E3NWovEGDBvk5fTOzLuKwy9bjOOyymVn7yGGXzczMrCPcMTAzM7MqdwzMzMysyh0DMzMzq3LHwMzMzKp64+OKi5Q8q+Ik0oREc4BDIuIuScOBw8tZHMtAR5LGAauTHj38D3BAREzM+ZYhBVf6JPAG6fHCsyPi3By34YaIGFqnLX2Bl4BzI+LoIr1uXZJ+R5pp8pc53zbAucCWEfFuo33+z/OzeO6o9oSpsNKaJ7Y1g7aZ9WYeMej5ZkfEFhGxOXA08ON2bLt33u4sUkeg4tfA68AHI+JDpJgKA1so75PAo8AX6wSlqlfXYcARklZWCn19BmkOg4adAjMz61ruGCxaliWd0NurGmUxB43aGjguIt4DiIhXI+InLZQzGjgNeAb4SFt15eiKJwM/Bb4GPBARHY0gaWZmncCXEnq+ykyOS5KG63fsQBlllMUhpKmR32tPATki4k7AQcDypE7C+DbqgjQl9f7AcKClyTfMzKzruGPQ882uRFqU9FHgIklDgUZTWpbpjaIsVuWASl8AVomIQU3aMRK4PSLeknQV8F1Jh0XEnGZ1RcR7kn4FDMvhpuuSdCBwIMAay67apBlmZrYgfClhERIR44GVgJWB6cAKNVkGAmWkx72BDwC/I0VZBHgI2Dxf8yciTsgdj2XbqH40MELSVOA+YEVghzbqqngvv5rt2zkRMSwihg1cavk2mmJmZh3ljsEiRNJGpF/k04HHgUE5ciKS1gE2B8rgS/NFWYyIJ4AJwPE5FDOSliQ99dCo3mWB7YC1c2TGwcDBpM5Cw7oWfI/NzKyz+VJCz1e5xwDSyXv/PHw/R9I+wPn5xP4O8NWImFFbQE2Uxa8AXyU9OfCEpNeA2cB3ik02lPRc8f404LaI+HeRdi3wU0lLtFFXuy2+xjJ+5M7MrIs4uqL1OI6uaGbWPo6uaGZmZh3ijoGZmZlVuWNgZmZmVe4YmJmZWZU7BmZmZlblxxWtx3n5qSc4Za+RbWe0ln378hu6uwlm9j7hEYNOJmlNSddKelzSk5JOk7S4pJ0lTcyvWZIezcsXSRou6Yaaci6QtGdeHlfknyjpypw+VtLzOe0hSaPrtSnnPVDSI/l1j6TtinW15VfqnVOkTZQ0OLd1Rk36iJw/8hwFlXIPlzQ2L2+Y65ko6WFJ5+T0huWZmdnC5xGDTpRDDf8B+GVE7JZnDjwHOCEijgBuyvnGAYdHxIT8fngLxe9dyV/j1Ig4WdIHgfskXZlnGCzbNZIU3Gi7iJgmaUvgGklbR8RLTcqvxmEoyhoM3BER9X6y/xv4nKQfR8S0mnWn57Zem8vZtFjXqDwzM1vIPGLQuXYE3o6I8wHyDISHAV+WtFRXVhwRjwNvMX98BEizFh5ROVlHxD+AC0nTFnemd0kdocPqrFsdqM6WGBGTOrluMzPrBO4YdK4hpABCVRHxBvAMsH4b225fDqcDn61Zf0mx/qTajfMowOMR8Uor7SLFQxjSoPwVc1r/Iu3qRm2VtF6x7kxgb0nL1dR3KnCbpD9LOkxSGQmpWXmV/TtQ0gRJE97893/q7KKZmXUGX0roXKJ+uONG6aV5htMlXVCzvtGlhMMkHQCsC+yyAG1t6VJCvbaWIuINSRcB3yTFWKikny/pptzG3YCDJG3eVnnF9ueQRiNYa+DynsfbzKyLeMSgc00G5pmLOkceXAt4sovqPDUiNgT2Ai7KAZNqPQRsVZO2ZU7vCj8nBUhaukyMiBci4ryI2I102WFoF9VvZmYd5I5B57oVWErSfgD55sNTgAsi4q2urDgi/kC6PLB/ndU/BX5SuUQgaQtgDHBWF7XlNeAKiuiJknaR1C8vrwasCDzfFfWbmVnH+VJCJ4qIkLQHcJak75I6Xn8CjumE4i+RVBmanxYR9R7p+wHwO0nnRsR7Rbuuk7QGcJekAGYC+0TEix1sy/aaG+oZ4PiIuLImzynAIcX7TwKnSXo7vz8iIl6StFGL5VWtuu76fu7ezKyLOOyy9TgOu2xm1j5y2GUzMzPrCHcMzMzMrModAzMzM6tyx8DMzMyq3DEwMzOzKj+u2IvkRyn/AGwcEY9IWow0GdGOpFkQ3wa+GBFTJH2ZFPMgSB3IY4sASH2Bl4BzI+LoovypwLBKTIYcHOrwiBgpaUxed0iRfwLpOzgQ6M/ceQ0+ExHPNtqPV56eyZlfu21BD4c1cPDZO3Z3E8ysG7lj0LuMBu4ERgFjSbMlDgI2i4j3JK0JvJn/PRbYMiJmSFoGWLko55PAo8AXJR0THXzmtfLojKSvAkMj4tAO7peZmXUSX0roJfLJ/WOk2QhH5eTVgRcrkyFFxHMR8TqwCmkSpFk5fVZETCmKGw2cRgoO9ZGFswdmZrYwuGPQe+wO3BgRjwGv5WiMVwCfyRENT5H0oZz3fuBlYIqk8yV9plKIpP7ATsANwKWkToKZmS0i3DHoPUYDl+Xly4DREfEcsCFwNPAecKuknSJiDikK4p7AY8CpksbmbUcCt+fYD1cBe+SYEFA/gmSnTK1Zhl2e9fa/OqNIMzOrw/cY9AI5eNKOwNAcK6EPEJKOjIh/A38G/izpZdLIwq35voF7gHsk3QKcT7ovYTTwsXyjIaRgSDsAfwGmAysA0/K6gcXyAinDLq+98oaex9vMrIt4xKB32BO4KCLWiYjBEbEWMAX4uKRBAPkJhc2ApyUNypcaKrbI6csC2wFr53IGAwcz93LCOGDfXF4fYB/g9i7fOzMz6zQeMegdRgMn1qRdBVxAut9giZx2D3AGsCpwcu40vA28CnwN+BxwWx5lqLgW+Gku44fALyXdDwi4EfhtkXeMpN2L9x/JlzPaZZV1BviROjOzLuLoitbjOLqimVn7OLqimZmZdYg7BmZmZlbljoGZmZlVuWNgZmZmVe4YmJmZWZUfV7Qe5+0HJ/PwRht3dzOsm238yMPd3QSzRZJHDHogSSHplOL94cWUxZW0+yVdWpN2gaQpOTbC/ZJ2Ktb1k3SipMclPSjpHkm75nXLSbpI0pP5dVFO2zSXNVHSa0XZf5E0WNKDRflbS/qrpEclPSLp15KWkrSqpBtyex6S9KcuO3BmZtYmdwx6pn8Dn5O0Ur2VkjYmfbYfl7R0zeojImIL4FDg7CL9h6Roi0MjYijwGWBAXvcb4KmIWC8i1iPNmvjriJgUEVvk8q6rlB0RI2rasyrwe+A7EbEhsDFp8qMBwA+AWyJi84jYBDiqQ0fEzMw6hTsGPdO7pLgBhzVY/yXgYuBm4LMN8owH1gCQtBRwAPCNyqyGEfFyRFwhaX1gK1LHoeIHwDBJ67XY3oOBCyNifC47IuLKiHiZ1Bmpzn4YEQ+0WKaZmXUBdwx6rjOBvSUtV2fdXsDlNA+LvAtwTV5eH3gmIt6ok28TYGKOuAhAXp4IDGmxrUOB+xqsOxP4jaTbJR1bid1Qq4yu+Nqcd1us1szM2ssdgx4qn8QvAr5Zpkv6MPBqRDwN3ApsKWmFIstJkp4ixTD4UQtVifqhkxult0tE3ASsC5wLbAT8U9LKdfKdExHDImLYwD6+Z9bMrKu4Y9Cz/Rz4ClDeRzAa2CiHRX4SWBb4fLH+CNIIwXHAhTntCWBtSQOY32TgQzn6IlCNxLg50Opt4ZNJlyPqiojXIuJ3EbEvcC/w8RbLNTOzTuaOQQ8WEa8BV5A6B5UT9heAzYqwyLtRczkhIt4DTgMWk7RzRLxFusHwdEmL57JWl7RPRDwB/JPUkag4DvhHXteKM4D9JW1TSZC0j6TVJO2Y73Egd0zWA55p14EwM7NO4zHZnu8U4JC8/HHg+Yh4vlj/V2ATSauXG0VESDoeOBK4iXSyPx54SNLbwJvA93L2rwC/kPQE6RLC+JzWkoh4WdIoUijnVYD3crv+QBpJOEPSu6SO6q8j4t5m5S05dAgbO7qimVmXcNhl63EcdtnMrH0cdtnMzMw6xB0DMzMzq3LHwMzMzKrcMTAzM7MqdwzMzMysyo8rNiFpNdIkQh8mBS6aSgo+9B/S5D6PAosDE4CvRMQ7koYDh0fESEljgPOBERFxay5zD9Jjel+IiCsljcv5J+RJie6LiM/nvHsCIyNiTNGma4FVIuKjRdpYYFZEnNxkX6YCM/PbPrkNP6zERsh5DgN+DKwaETNy2nDgduCrEfGbnPYh4B+koEkn57S+wEvAuRFxdFFmX1JshS+QHoEE+H1EnJDXzwEmFU29LCJObLQfAJOnT2bTCzdtlsWs3SbtP6ntTGa9gEcMGpAk4GpgXI4quAlwDLBqzvJkjiq4KbAm8MUGRU1i3gmGRgH3N6l6mKS6MQgkLQ9sCSwv6QMt78xcO0TEpsDWpGmIz6lZP5o08+AeNemTSPEXKurtwydJHaUv5mNXcTwwCNg0H6/tgX7F+tmVCI351bRTYGZmXcsdg8Z2AN6JiGpo4oiYGBF3lJlyQKF7yJEK67gD2FpSP0nLkKYjntik3pNJHZB6Pg9cD1xGOjl3SETMAr4G7C5pIECOlLgMaaKj2sBLzwBLSlo1n/R3Af5ck2c0aTbFZ4CP5DLLqI1v57pnRsTYjrbdzMy6ljsGjTWLCFglaUlgG+DGBlkC+AuwM2l64uvaKPIKUuCj9eusG02KmNgsamJLchCmKcAHa8q+A9gwz1BYupJ0OWBb0mWE8hJEf2An4IaatlWiNs6ksf6SJhavvZrkNTOzLuaOQcetJ2kiMJ108nugSd7KL/xRpBNnM3OAk4Cjy0RJq5JOtHdGxGPAu5KGdrTxlWKL5VGk6/vvke+BqMl7RU6rdCBKI4Hbc8yFq4A9JPWZrzLpv/PJ/1lJa+Xk2ksJl9dtaBF2ec7MOfWymJlZJ3DHoLGmEQGZe4/B+sBHJH22UcaIuIc0ArFSPqm35WJS3IO1i7S9gBWAKflGwsEswOWEHLBoMPCYpM1IIwe35LJHMX/gpZeAd4BPkMI5l0YDIyo3TwIrki7FzBO1MSLOz8dsBukGyJaVYZf7DGjXpmZm1g7uGDR2G7CEpAMqCZI+LOm/ykwR8SJwFDW/8Os4msb3DswjIt4BTiU9AVExGtiliJq4FR3sGOR7Hc4CromI13PZYytlR8QgYA1J69Rs+j3gO/m+ikpZywLbAWsXbTsYGF1EbTwjX3IhjyQs3pF2m5lZ1/Pjig3k6IN7AD+XdBTwNnMfV6x1DTBW0vZNyqu9Wa8tvyGHOpY0mDR68PeivCmS3ihCGR8n6dBi/Zp1yrw93zy4GOmJix/m9FHArjV5r87pdxdl3lWnzM8Bt5WPPQLXAj+VtARwbK7nQUkzgdnAhcALOW//fEmm4saIOKpOPVVDVhzChP0dRMnMrCs4uqL1OI6uaGbWPo6uaGZmZh3ijoGZmZlVuWNgZmZmVe4YmJmZWZU7BmZmZlblxxWt53nhnzB2ue5uhZktbGNndHcLegWPGPQSkubk6Yjvl/QPSdvWrD9M0tuSlsvvdy7iF8yS9GhefrFB+kWShkuaURP7YERN/Q9Kul7S8pKWlPSIpE2Ldhwp6WzMzKxbeMSg95idpyNG0s7Aj4FyFscy5PIFEXETcFPOPw44PCLmmTygNl3ScOCOiBjZRv0XAgdHxAl5UqazJH2cFJ75IKClZ23NzKzzecSgd1oWeL3ypo2Qy11hPDlMdUTcCLwI7EeaBnpsnqbZzMy6gUcMeo/K1MNLAqsDOxbr5gu5HBGvdLCe7WumOP58RDxZeZNjJexEmvK54lDgHuDxiLi4XqGSDgQOBFh7OdXLYmZmncAjBr1HJbzxRsAuwEU5bgK0HXK5Pe6oCaNc6RRUOibTgYHALZUNIuIFUtCqXzYqtIyuuPJS7hiYmXUVdwx6oYgYD6wErNxKyOVOUrnHYB1SdMWDa9a/l19mZtaN3DHohSRtBPQh/XpvNeRyp4iIGcA3gcMl9euKOszMrON8j0HvUYY3FrB/RMyR1Czk8k86UE/tPQbHR8SVZYaI+Kek+3Mdde8paGrQh2CsoyuamXUFh122Hsdhl83M2sdhl83MzKxD3DEwMzOzKncMzMzMrModAzMzM6tyx8DMzMyq/Lii1SVpReDW/HY1YA7wan6/QUQsVeQdAwyLiEMkjQUOKPICDAe2AK4FpuS0acDtzJ1lcVNgUl4+LyJOb9S2Sc/PYPBRf+zQfpmZ9URTT/z0QqvLHQOrKyKmk07m5JP9rIg4Ob+f1cbmp1byVuTZl+tFXjyhUmYl+qKZmXUfX0owMzOzKo8YWEf0r5ndcCBwXfH+MEn75OXXI2KHvFzOivj7iDihqxtqZmbt446BdcTscti/co9BsX6+SwlZvUsJLSnDLvdZduWOFGFmZi3wpQTrEcqwy32WWq67m2Nmtshyx8DMzMyqfCnBukJ5jwHA7p1Z+KZrLMeEhfjojplZb+LoitbjOLqimVn7OLqimZmZdYg7BmZmZlblSwnW40iaCTza3e1YACuRpoTuqXp6+6Hn70NPbz/0/H3oae1fJyJaetbbNx9aT/Roq9fK3o8kTXD7u1dP34ee3n7o+fvQ09vfjC8lmJmZWZU7BmZmZlbljoH1ROd0dwMWkNvf/Xr6PvT09kPP34ee3v6GfPOhmZmZVXnEwMzMzKrcMTAzM7Mqdwysx5C0i6RHJT0h6aj3QXumSpokaaKkCTltoKRbJD2e/10hp0vS6bntD0jasihn/5z/cUn7F+lb5fKfyNuqE9p8nqRXJD1YpHV5mxvV0UntHyvp+fw5TJT0qWLd0bktj0rauUiv+12S9AFJd+d2Xi5p8Zy+RH7/RF4/uIPtX0vS7ZIeljRZ0v/m9B7xGTRpf0/6DJaUdI+k+/M+fL+j9XbWvr3vRIRffr3vX0Af4ElgXWBx4H5gk25u01RgpZq0nwJH5eWjgJ/k5U8BfwYEfAS4O6cPBJ7K/66Ql1fI6+4BPpq3+TOwaye0+ePAlsCDC7PNjeropPaPBQ6vk3eT/D1ZAvhA/v70afZdAq4ARuXls4Gv5+X/Ac7Oy6OAyzvY/tWBLfPyAOCx3M4e8Rk0aX9P+gwELJOX+wF352Pbrno7c9/eb69ub4BffrXyyn/obireHw0c3c1tmsr8HYNHgdXz8uqkyZgAfgWMrs0HjAZ+VaT/KqetDjxSpM+TbwHbPZh5T6xd3uZGdXRS+8dS/6Q0z3cEuCl/j+p+l/IJYxrQt/Y7V9k2L/fN+dQJn8W1wCd62mdQp/098jMAlgL+AWzT3no7c9/eby9fSrCeYg3g2eL9czmtOwVws6T7JB2Y01aNiBcB8r+r5PRG7W+W/lyd9K6wMNrcqI7Ockgeaj+vGCJvb/tXBP4VEe/WaeEoMDcAAAK5SURBVH91m7x+Rs7fYXlI+kOkX6w97jOoaT/0oM9AUh9JE4FXgFtIv/DbW29n7tv7ijsG1lPUu77e3c/a/v927t41iiCM4/j3AV9RMQoWgbMwksJGggQJKFZyoJ2Qwiqiljb2Af8DrRQLsRKxEBXSWfhSa6HGSFBjJwkJIr6Uoo/FPLuscnt4m/XuFn4fGPZudtn5bebYm+xMcsTdDwEngAtmdqzLsWX5e63vp6Zkvg7sByaAFeBy1NeZv9ZrM7PtwD3gort/63ZoSbsD7YMO+RvVB+7+090ngBZwGDhQod2h7Js6aGAgTfER2Ft43wKWB5QFAHdfju0a8IB0g1k1s1GA2K7F4WX5u9W3OtT/D/3IXNbGurn7atzofwE3SP1QJf8nYMTMNvxV/8e5Yv9O4HOVvGa2kfSletvd70d1Y/qgU/6m9UHG3b8AT0lrDHptt85rGyoaGEhTPAfGY1XvJtIioLlBhTGzbWa2I3sNtIGFyJStED9DmoMl6mdilfkU8DUe5z4E2ma2Kx6/tknzjivAdzObilXlM4Vz1a0fmcvaWLfsyy6cIvVD1ubpWFW+DxgnLczr+FnyNPH7BJgu+Vlk+aeBx3F8r1kNuAksuvuVwq5G9EFZ/ob1wR4zG4nXW4HjwGKFduu8tuEy6EUOKir/WkgrtN+R5gNnB5xljLTa+BXwJstDmkd8BLyP7e6oN+BaZH8NTBbOdQ5YinK2UD9JusF+AK5Sz2K3O6RHvT9Iv9mc70fmsjZqyn8r8s2TbtajheNnI8tbCn/VUfZZin59Ftd1F9gc9Vvi/VLsH6uY/yjp8fE88DLKyab0QZf8TeqDg8CLyLoAXKrabl3XNmxF/xJZREREcppKEBERkZwGBiIiIpLTwEBERERyGhiIiIhITgMDERERyWlgICIiIjkNDERERCT3G2vt1x5pNLeAAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1792d4fd400>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "crimes['Primary Type'].value_counts()[:15].plot(kind='barh', title='Quantidade de incidentes em relação ao tipo de crime')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Resolução Ricardo\n",
    "\n",
    "Há diversas colunas para descrever o tipo de crime registrado. Foi escolhida a coluna '_Primary Type_' - que se relaciona ao IUCR, já descrito pelo Yurick - por ser sucinta e mais inteligível que apenas o número do código.\n",
    "\n",
    "Fazendo a contagem, é fácil verificar que o tipo de cirme mais registrado é o de **roubo**. Expandindo um pouco mais a questão, foram escolhidos o 5 tipos de crimes mais registrados neste dataset e foi feito um gráfico de linhas mostrando a evolução do número de crimes dos tipos por ano. É interessante ver que _criminal damage_ e _narcotics_ ficavam bem próximos dividindo 3º e 4º lugar, até 2015-2016, quando os crimes por narcóticos diminuiram drásticamente, caindo para 5º lugar."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "THEFT                                329460\n",
       "BATTERY                              263700\n",
       "CRIMINAL DAMAGE                      155455\n",
       "NARCOTICS                            135240\n",
       "ASSAULT                               91289\n",
       "OTHER OFFENSE                         87874\n",
       "BURGLARY                              83397\n",
       "DECEPTIVE PRACTICE                    75495\n",
       "MOTOR VEHICLE THEFT                   61138\n",
       "ROBBERY                               57313\n",
       "CRIMINAL TRESPASS                     36912\n",
       "WEAPONS VIOLATION                     17233\n",
       "PUBLIC PEACE VIOLATION                13122\n",
       "OFFENSE INVOLVING CHILDREN            11398\n",
       "PROSTITUTION                           7633\n",
       "CRIM SEXUAL ASSAULT                    6823\n",
       "INTERFERENCE WITH PUBLIC OFFICER       6195\n",
       "SEX OFFENSE                            4885\n",
       "HOMICIDE                               2649\n",
       "ARSON                                  2217\n",
       "GAMBLING                               2212\n",
       "LIQUOR LAW VIOLATION                   1953\n",
       "KIDNAPPING                             1099\n",
       "STALKING                                828\n",
       "INTIMIDATION                            662\n",
       "OBSCENITY                               187\n",
       "NON-CRIMINAL                             93\n",
       "CONCEALED CARRY LICENSE VIOLATION        90\n",
       "PUBLIC INDECENCY                         62\n",
       "NON - CRIMINAL                           38\n",
       "OTHER NARCOTIC VIOLATION                 30\n",
       "HUMAN TRAFFICKING                        28\n",
       "NON-CRIMINAL (SUBJECT SPECIFIED)          4\n",
       "Name: Primary Type, dtype: int64"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "crimes['Primary Type'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "theft_crimes = crimes[crimes[\"Primary Type\"] == \"THEFT\"].groupby('Year')['Primary Type'].value_counts()\n",
    "battery_crimes = crimes[crimes[\"Primary Type\"] == \"BATTERY\"].groupby('Year')['Primary Type'].value_counts()\n",
    "criminal_damage_crimes = crimes[crimes[\"Primary Type\"] == \"CRIMINAL DAMAGE\"].groupby('Year')['Primary Type'].value_counts()\n",
    "narcotics_crimes = crimes[crimes[\"Primary Type\"] == \"NARCOTICS\"].groupby('Year')['Primary Type'].value_counts()\n",
    "assault_crimes = crimes[crimes[\"Primary Type\"] == \"ASSAULT\"].groupby('Year')['Primary Type'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f9f7291d610>"
      ]
     },
     "execution_count": 20,
     "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": [
    "crime_types_df = pd.DataFrame({'THEFT': theft_crimes.values,\n",
    "  'BATTERY': battery_crimes.values,\n",
    "  'CRIMINAL DAMAGE': criminal_damage_crimes.values,\n",
    "  'NARCOTICS': narcotics_crimes.values,\n",
    "  'ASSAULT': assault_crimes.values}, index=[i for i in range(2012, 2018)])\n",
    "crime_types_df.plot.line(figsize=(10,10))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Questão 4"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Resolução Yurick\n",
    "\n",
    "Para verificar o ano com maior quantidade de ocorrências no dataset, foram calculadas as quantidades de ocorrências em cada ano. Depois disso, foram geradas informações estatísticas e plotados gráficos de barra e setores, além de um boxplot. Uma observação extra é a pequena quantidade de ocorrências no ano de 2017, em que aparenta haver uma falta de dados desse ano. O ano com mais crimes reportados é 2012, representando 23% (335670), mas não muito longe da média (242785.666667), mesmo com o ano de 2012 alterando o valor da média para menor do que o esperado."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2012    335670\n",
      "2013    306703\n",
      "2014    274527\n",
      "2016    265462\n",
      "2015    262995\n",
      "2017     11357\n",
      "Name: Year, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "# Count the number of incidents on each year\n",
    "crimes_by_year = crimes[\"Year\"].value_counts()\n",
    "print(crimes_by_year)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fb452d56ed0>"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot boxplot with year\n",
    "crimes_by_year.plot.box()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count         6.000000\n",
       "mean     242785.666667\n",
       "std      116792.378282\n",
       "min       11357.000000\n",
       "25%      263611.750000\n",
       "50%      269994.500000\n",
       "75%      298659.000000\n",
       "max      335670.000000\n",
       "Name: Year, dtype: float64"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# show statistics data for year field\n",
    "crimes_by_year.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<BarContainer object of 6 artists>"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot bar with year\n",
    "plt.bar(crimes_by_year.index, crimes_by_year.values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "([<matplotlib.patches.Wedge at 0x7fb44e7ad3d0>,\n",
       "  <matplotlib.patches.Wedge at 0x7fb44e7ad7d0>,\n",
       "  <matplotlib.patches.Wedge at 0x7fb44e7879d0>,\n",
       "  <matplotlib.patches.Wedge at 0x7fb44e7a05d0>,\n",
       "  <matplotlib.patches.Wedge at 0x7fb44e7a0290>,\n",
       "  <matplotlib.patches.Wedge at 0x7fb44e79e7d0>],\n",
       " [Text(-1.09963,-0.0283595,'2012'),\n",
       "  Text(-0.174871,-1.08601,'2013'),\n",
       "  Text(0.977239,-0.504979,'2014'),\n",
       "  Text(0.850044,0.698158,'2016'),\n",
       "  Text(-0.279062,1.06401,'2015'),\n",
       "  Text(-0.82508,0.727491,'2017')],\n",
       " [Text(-0.599801,-0.0154688,'23.0%'),\n",
       "  Text(-0.0953844,-0.59237,'21.1%'),\n",
       "  Text(0.53304,-0.275443,'18.8%'),\n",
       "  Text(0.46366,0.380814,'18.2%'),\n",
       "  Text(-0.152216,0.580371,'18.1%'),\n",
       "  Text(-0.450044,0.396813,'0.8%')])"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot pie with year\n",
    "plt.pie(crimes_by_year, labels=crimes_by_year.index, autopct='%1.1f%%', shadow=True, startangle=140)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Resolução Kaique\n",
    "\n",
    "Para se obter o resultado basta construir um gráfico de barras com as contagens de todos os registros para cada ano.<br>\n",
    "O resultado mostra que parece haver uma pequena e lenta redução no número de incidentes reportados com o passar dos anos, o que pode significar uma redução na taxa de criminalidade. É possível observar também a pequena quantidade de registros do ano de 2017, que se dá pois o conjunto de dados analisado possui apenas registros do começo desse respectivo ano."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1791640d630>"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1791640f978>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "crimes['Year'].value_counts()[:15].plot(kind='bar', title='Quantidade de incidentes em relação ao ano')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Resolução Ricardo\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2012    335670\n",
       "2013    306703\n",
       "2014    274527\n",
       "2015    262995\n",
       "2016    265462\n",
       "2017     11357\n",
       "Name: Year, dtype: int64"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "crimes_per_year = crimes['Year'].value_counts().sort_index()\n",
    "crimes_per_year"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count         6.000000\n",
       "mean     242785.666667\n",
       "std      116792.378282\n",
       "min       11357.000000\n",
       "25%      263611.750000\n",
       "50%      269994.500000\n",
       "75%      298659.000000\n",
       "max      335670.000000\n",
       "Name: Year, dtype: float64"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "crimes_per_year.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f7fec2be1d0>"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "crimes_per_year.plot.barh()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Questão 5"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Resolução Yurick\n",
    "\n",
    "Como a descrição do dataset menciona, os dados do dataset podem não ter muita confiabilidade. Para responder essa questão, era preciso analisar o campo 'Updated On', porém percebeu-se que ele não possui tanta representatividade. Isso quer dizer que todas as linhas da tabela foram atualizadas em algum momento, e uma atualização em um registro não necessariamente representa que a ocorrência sofreu uma atualização. Por conta disso, o gráfico dos locais de ocorrência de crimes que não tiveram atualização está vazio.\n",
    "\n",
    "Assim, o foco da pergunta foi alterado para descobrir quais as datas que ofertaram maior quantidade de atualizações. O gráfico de barras plotado mostra as datas com maior número de atualizações. Nos dias 04/02/2016 e 17/08/2015 diversos dados tiveram atualização, o que pode indicar algum acerto geral nos dados do dataset. Portanto, os dados perdem sua confiança para análise com respeito a sua data de atualização."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<BarContainer object of 0 artists>"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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B4g3IBeDPx3ydWQrDivfM4m9kBfwQeKp7fHTae3qNvf4p8GMW/3Ll1u7cHcD7u+e/w+JfpCwA3wXeMfK5t3afd5Az9C+vJrln4K+B/x75uT4FnD/t/azmz3jka8x8GPyXz5Kkhn+VJElqGAZJUsMwSJIahkGS1DAMkqSGYZAkNQyDJKlhGCRJjf8FFDYZsBaypoYAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "crimes[\"Updated On\"] = pd.to_datetime(crimes[\"Updated On\"])\n",
    "# check the crimes that were updated\n",
    "crimes[\"Not_updated\"] = np.where((crimes['Date']==crimes['Updated On']),True, False)\n",
    "crimes_local_updated = {}\n",
    "# for updated crimes get its community area\n",
    "for index, crime in crimes.iterrows():\n",
    "    if crime[\"Not_updated\"]:\n",
    "        if crime[\"Community Area\"] not in crimes_local_updated:\n",
    "            crimes_local_updated[crime[\"Community Area\"]] = 0\n",
    "        else:\n",
    "            crimes_local_updated[crime[\"Community Area\"]] += 1\n",
    "# plot bar with community area\n",
    "plt.bar(crimes_local_updated.keys(), crimes_local_updated.values())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2016-01-15      3630\n",
      "2015-09-30      4432\n",
      "2015-09-24      4982\n",
      "2015-09-17      5046\n",
      "2015-08-31      5285\n",
      "2015-10-09      6468\n",
      "2015-09-10      6847\n",
      "2016-04-15      8471\n",
      "2015-08-17    158320\n",
      "2016-02-04    909108\n",
      "Name: Updated On, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "# get the date when data were updated\n",
    "most_updated = crimes[\"Updated On\"].dt.date.value_counts().sort_values().tail(10)\n",
    "print(most_updated)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<BarContainer object of 10 artists>"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot bar with date\n",
    "f, ax = plt.subplots(figsize=(20,5))\n",
    "plt.bar(most_updated.index, most_updated.values)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Questão 6"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Resolução Yurick\n",
    "\n",
    "Para responder a essa questão, foram calculados a quantidade de ocorrência envolvendo agressão doméstica pelos locais de Chicago divididos por '[Community Area](https://data.cityofchicago.org/Facilities-Geographic-Boundaries/Boundaries-Community-Areas-current-/cauq-8yn6)'. Depois disso, foram plotados os dez locais com maior quantidade de ocorrência envolvendo agrssão doméstica, utilizando gráficos de setor e barra. Depois disso, foram geradas informações estatísticas e plotado um boxplot de todos as Community Areas com ocorrência de crimes envolvendo agressão doméstica. Novamente não foi possível localizar um arquivo csv com o Chicago Community Area. O local com maior quantidade de violência doméstica é a Community Area 25, ou seja, Austin, com 17839 ocorrências. Austin difere muito de Edison Park (Community Area 9), o local com menor incidência de agressão doméstica. Isso também pode ser visto no boxplot, onde Austin é um outlier bem distante do limite superior."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "crimes_local_domestic = {}\n",
    "# for domestic violence crimes get its community area\n",
    "for index, crime in crimes.iterrows():\n",
    "    if crime[\"Domestic\"] and not math.isnan(crime[\"Community Area\"]):\n",
    "        if crime[\"Community Area\"] not in crimes_local_domestic:\n",
    "            crimes_local_domestic[int(crime[\"Community Area\"])] = 0\n",
    "        else:\n",
    "            crimes_local_domestic[int(crime[\"Community Area\"])] += 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "9       221\n",
      "12      250\n",
      "47      388\n",
      "76      435\n",
      "13      435\n",
      "5       499\n",
      "74      551\n",
      "34      555\n",
      "18      564\n",
      "57      574\n",
      "55      596\n",
      "72      607\n",
      "62      654\n",
      "37      712\n",
      "33      758\n",
      "59      761\n",
      "36      768\n",
      "10      810\n",
      "7       840\n",
      "11      858\n",
      "41      875\n",
      "4       952\n",
      "64      958\n",
      "50      997\n",
      "56     1154\n",
      "52     1240\n",
      "32     1247\n",
      "60     1283\n",
      "45     1321\n",
      "39     1324\n",
      "      ...  \n",
      "8      2239\n",
      "58     2332\n",
      "2      2409\n",
      "15     2592\n",
      "1      2978\n",
      "73     3033\n",
      "40     3170\n",
      "22     3194\n",
      "24     3207\n",
      "38     3435\n",
      "28     4007\n",
      "30     4111\n",
      "42     4147\n",
      "19     4419\n",
      "61     4616\n",
      "53     4764\n",
      "26     4860\n",
      "27     5027\n",
      "46     5732\n",
      "66     6228\n",
      "44     6335\n",
      "49     6941\n",
      "23     7625\n",
      "69     7869\n",
      "68     7936\n",
      "67     8260\n",
      "71     8913\n",
      "29     8924\n",
      "43    10674\n",
      "25    17839\n",
      "Length: 77, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "# convert dict to series\n",
    "crimes_local_domestic_series = pd.Series(crimes_local_domestic).sort_values()\n",
    "print(crimes_local_domestic_series)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<BarContainer object of 10 artists>"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot bar with community area\n",
    "plt.bar(crimes_local_domestic_series.tail(10).index, crimes_local_domestic_series.tail(10).values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "([<matplotlib.patches.Wedge at 0x7fb450dfb990>,\n",
       "  <matplotlib.patches.Wedge at 0x7fb450e163d0>,\n",
       "  <matplotlib.patches.Wedge at 0x7fb450e16e90>,\n",
       "  <matplotlib.patches.Wedge at 0x7fb450e10990>,\n",
       "  <matplotlib.patches.Wedge at 0x7fb45af69490>,\n",
       "  <matplotlib.patches.Wedge at 0x7fb45af69fd0>,\n",
       "  <matplotlib.patches.Wedge at 0x7fb45af42a90>,\n",
       "  <matplotlib.patches.Wedge at 0x7fb45af64590>,\n",
       "  <matplotlib.patches.Wedge at 0x7fb45af63090>,\n",
       "  <matplotlib.patches.Wedge at 0x7fb45af63b50>],\n",
       " [Text(-0.9756,0.508138,'44'),\n",
       "  Text(-1.0997,0.0257861,'49'),\n",
       "  Text(-0.976871,-0.50569,'23'),\n",
       "  Text(-0.58437,-0.93194,'69'),\n",
       "  Text(-0.017954,-1.09985,'68'),\n",
       "  Text(0.566377,-0.942983,'67'),\n",
       "  Text(0.995645,-0.467645,'71'),\n",
       "  Text(1.08329,0.19103,'29'),\n",
       "  Text(0.726982,0.825528,'43'),\n",
       "  Text(-0.281662,1.06333,'25')],\n",
       " [Text(-0.532146,0.277166,'6.9%'),\n",
       "  Text(-0.599835,0.0140652,'7.6%'),\n",
       "  Text(-0.532839,-0.275831,'8.4%'),\n",
       "  Text(-0.318747,-0.508331,'8.6%'),\n",
       "  Text(-0.00979308,-0.59992,'8.7%'),\n",
       "  Text(0.308933,-0.514354,'9.0%'),\n",
       "  Text(0.543079,-0.255079,'9.8%'),\n",
       "  Text(0.590883,0.104198,'9.8%'),\n",
       "  Text(0.396536,0.450288,'11.7%'),\n",
       "  Text(-0.153634,0.579997,'19.5%')])"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot pie with community area\n",
    "plt.pie(crimes_local_domestic_series.tail(10), labels=crimes_local_domestic_series.tail(10).index, autopct='%1.1f%%', shadow=True, startangle=140)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fb4554b6650>"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot boxplot with community area\n",
    "crimes_local_domestic_series.plot.box()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count       77.000000\n",
       "mean      2856.818182\n",
       "std       3015.459722\n",
       "min        221.000000\n",
       "25%        858.000000\n",
       "50%       1754.000000\n",
       "75%       4007.000000\n",
       "max      17839.000000\n",
       "dtype: float64"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# get statistics info about domestic violence\n",
    "crimes_local_domestic_series.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Questão 7"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Resolução Kaique\n",
    "\n",
    "Para responder à pergunta, é feita uma contagem dos registros na coluna \"Location Description\" e dessa contagem total é recuperado o maior número de registros e o valor que possui esse número. Após isso, é relacionado o número obtido com a quantidade total de registros no conjunto de dados para obter a porcentagem. Para visualizar a relação do local onde mais occorem crimes com os outros locais, é possível construir um gráfico de barras com as contagens de todos os locais.<br> \n",
    "O resultado mostra que o local onde mais ocorrem incidentes é nas ruas e que só esse tipo representa vinte e dois por cento do total de incidentes, o que é uma grande parcela. Além disso, é possível visualizar que curiosamente o número de incidentes reportados que ocorreram em residências e apartamentos são maiores do que o número de incidentes que ocorreram nas calçadas."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Local com mais crimes reportados: STREET\n",
      "Porcentagem desse local em relação ao total: 22.68606%\n"
     ]
    }
   ],
   "source": [
    "total = crimes['ID'].count()\n",
    "loc_common = crimes['Location Description'].value_counts().idxmax()\n",
    "print('Local com mais crimes reportados: ' + loc_common)\n",
    "loc_common_v = crimes['Location Description'].value_counts().max()\n",
    "print ('Porcentagem desse local em relação ao total: {:.5%}'.format(loc_common_v/total))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x179163802e8>"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1791e2c5eb8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "crimes['Location Description'].value_counts()[:15].plot(kind='barh', title='Quantidade de incidentes em relação ao local')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Resolução Ricardo\n",
    "\n",
    "Para verificar os locais nos quais há maior incidência de crimes em Chicago, foram usadas as colunas \"*ward*\" e \"*community area*\", que são divisões das cidade de Chicago, assim como usamos bairros e distritos em São Paulo (apesar de não ser uma comparação equivalente).\n",
    "\n",
    "Primeiro, verificamos as 5 \"*wards*\" e as 5 \"*community areas*\" com maior incidência de crimes.\n",
    "\n",
    "Nos links abaixo, disponibilizados pelo Chicago Data Portal, é possível verificar mapas que mostram tanto \"*wards*\" quanto \"*community areas*\". É possível identificar cada uma delas clicando na área correspondente.\n",
    "\n",
    "Apesar de a usabilidade não ser a melhor, foi possível verificar que a \"*community area*\" com maior incidência de crimes é a \"*community area*\" **Austin** e que as \"*wards 28, 24 e 27*\" ficam próximas dessa região.\n",
    "\n",
    "https://data.cityofchicago.org/Facilities-Geographic-Boundaries/Boundaries-Community-Areas-current-/cauq-8yn6\n",
    "\n",
    "https://data.cityofchicago.org/Facilities-Geographic-Boundaries/Boundaries-Wards-2015-/sp34-6z76\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "25.0    94730\n",
       "8.0     50290\n",
       "43.0    48909\n",
       "23.0    47093\n",
       "29.0    46151\n",
       "        ...  \n",
       "55.0     3209\n",
       "12.0     2543\n",
       "47.0     2197\n",
       "9.0      1433\n",
       "0.0        13\n",
       "Name: Community Area, Length: 78, dtype: int64"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "crimes['Community Area'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "25.0    94730\n",
       "8.0     50290\n",
       "43.0    48909\n",
       "23.0    47093\n",
       "29.0    46151\n",
       "Name: Community Area, dtype: int64"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "crimes['Community Area'].value_counts().nlargest(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "28.0    71667\n",
       "42.0    66836\n",
       "24.0    63730\n",
       "2.0     56919\n",
       "27.0    51640\n",
       "17.0    49478\n",
       "6.0     48471\n",
       "20.0    44981\n",
       "21.0    44830\n",
       "8.0     40278\n",
       "16.0    39798\n",
       "7.0     39721\n",
       "34.0    39504\n",
       "37.0    38674\n",
       "15.0    38182\n",
       "3.0     38032\n",
       "9.0     37292\n",
       "29.0    35151\n",
       "5.0     35037\n",
       "1.0     26280\n",
       "10.0    25967\n",
       "4.0     24613\n",
       "18.0    24240\n",
       "32.0    23934\n",
       "44.0    23059\n",
       "26.0    22298\n",
       "25.0    20472\n",
       "14.0    20151\n",
       "31.0    20142\n",
       "30.0    20109\n",
       "11.0    19538\n",
       "46.0    19459\n",
       "49.0    19053\n",
       "13.0    18666\n",
       "43.0    18645\n",
       "12.0    18080\n",
       "35.0    17963\n",
       "23.0    17381\n",
       "22.0    17135\n",
       "41.0    16849\n",
       "38.0    15570\n",
       "45.0    15282\n",
       "36.0    14814\n",
       "50.0    14627\n",
       "40.0    14515\n",
       "47.0    14215\n",
       "33.0    13844\n",
       "39.0    13672\n",
       "48.0    13235\n",
       "19.0    12671\n",
       "Name: Ward, dtype: int64"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "crimes['Ward'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "28.0    71667\n",
       "42.0    66836\n",
       "24.0    63730\n",
       "2.0     56919\n",
       "27.0    51640\n",
       "Name: Ward, dtype: int64"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "crimes['Ward'].value_counts().nlargest(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Questão 8"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Resolução Kaique\n",
    "\n",
    "Para responder à pergunta, é feita uma contagem dos registros na coluna \"District\" e dessa contagem total é recuperado o maior número de registros e o valor que possui esse número. Após isso, é relacionado o número obtido com a quantidade total de registros no conjunto de dados para obter a porcentagem. Para visualizar a relação dos distritos policiais, é possível construir um gráfico de barras com as contagens de todos os locais ou gráfico de pizza.<br> \n",
    "O resultado mostra que o distrito com maior número de incidentes reportados é o distrito 11, com sete por cento do total, e que os incidentes criminosos reportados estão bem distribuídos entre os distritos policiais, com exceção dos distritos 13 e 31, que curiosamente possuem um número extremamente menor que todos os outros distritos, com 1 e 26 incidentes apenas respectivamente."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Distrito policial com mais crimes reportados: 11.0\n",
      "Porcentagem desse distrito em relação ao total: 7.06233%\n"
     ]
    }
   ],
   "source": [
    "total = crimes['ID'].count()\n",
    "dis_common = crimes['District'].value_counts().idxmax()\n",
    "print('Distrito policial com mais crimes reportados: ' + str(dis_common))\n",
    "dis_common_v = crimes['District'].value_counts().max()\n",
    "print ('Porcentagem desse distrito em relação ao total: {:.5%}'.format(dis_common_v/total))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x17916649c18>"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x17916618358>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "crimes['District'].value_counts().plot(kind='pie')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1791656f128>"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x179165a2048>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "crimes['District'].value_counts().plot(kind='bar', title='Quantidade de incidentes por distrito policial')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Quantidade de incidentes no distrito policial 13: 1\n",
      "Quantidade de incidentes no distrito policial 31: 26\n"
     ]
    }
   ],
   "source": [
    "print('Quantidade de incidentes no distrito policial 13: '+str(crimes[crimes['District']==13.0].shape[0]))\n",
    "print('Quantidade de incidentes no distrito policial 31: '+str(crimes[crimes['District']==31.0].shape[0]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Resolução Ricardo\n",
    "\n",
    "O distrito policial com maior incidência de crimes é o **11**. Relacionando com a questão anterior, ele fica próximo da comunidade de **Austin** e próximo das \"*wards*\" com maior incidência de crimes.\n",
    "\n",
    "Verificando os distritos com o **menor número de incidências** é estranho ver quantidades tão baixas para os distritos 31 e 13. Verificando o mapa no link abaixo e relacionando com os links da questão anterior, é possível ver que **não existe um distrito policial 13**, provavelmente simbolismo do número 13 para os americanos.\n",
    "\n",
    "O único registro para um distrito 13 foi, provavelmente, apenas um *typo*, dado que é possível verificar nos links que a *ward* 1 e *community area* 24 pertencem ao distrito 12.\n",
    "\n",
    "Já o distrito 31 possui uma área peculiar, sendo apenas uma parte pequena ao extremo sul, e outra área ao norte de Chicago. Já no site da polícia de Chicago, não é descrito um distrito 31.\n",
    "\n",
    "Por fim, foi feito um gráfico comparando a evolução do número de crimes dos distritos 11 (o com maior incidência) e 20 (3º com menor incidência) ao longo dos anos.\n",
    "\n",
    "https://data.cityofchicago.org/Public-Safety/Boundaries-Police-Districts-current-/fthy-xz3r\n",
    "\n",
    "https://home.chicagopolice.org/community/districts/\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "11.0    102878\n",
       "8.0      96325\n",
       "6.0      86915\n",
       "4.0      85681\n",
       "7.0      84373\n",
       "25.0     83204\n",
       "3.0      73195\n",
       "9.0      70869\n",
       "12.0     69482\n",
       "10.0     66276\n",
       "19.0     66115\n",
       "5.0      65146\n",
       "15.0     64508\n",
       "18.0     63682\n",
       "1.0      62022\n",
       "2.0      60312\n",
       "14.0     52509\n",
       "16.0     50134\n",
       "22.0     47484\n",
       "17.0     41406\n",
       "24.0     40449\n",
       "20.0     23721\n",
       "31.0        26\n",
       "13.0         1\n",
       "Name: District, dtype: int64"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "crimes['District'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Year  District\n",
       "2012  11.0        22044\n",
       "2013  11.0        21913\n",
       "2014  11.0        20519\n",
       "2015  11.0        19412\n",
       "2016  11.0        18211\n",
       "2017  11.0          779\n",
       "Name: District, dtype: int64"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "district_11_crimes_per_year = crimes[crimes[\"District\"] == 11.0].groupby('Year')['District'].value_counts()\n",
    "district_11_crimes_per_year"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Year  District\n",
       "2012  20.0        5737\n",
       "2013  20.0        4903\n",
       "2014  20.0        4318\n",
       "2015  20.0        4253\n",
       "2016  20.0        4334\n",
       "2017  20.0         176\n",
       "Name: District, dtype: int64"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "district_20_crimes_per_year = crimes[crimes[\"District\"] == 20.0].groupby('Year')['District'].value_counts()\n",
    "district_20_crimes_per_year"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
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       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
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       "      <th></th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>ID</th>\n",
       "      <th>Case Number</th>\n",
       "      <th>Date</th>\n",
       "      <th>Block</th>\n",
       "      <th>IUCR</th>\n",
       "      <th>Primary Type</th>\n",
       "      <th>Description</th>\n",
       "      <th>Location Description</th>\n",
       "      <th>Arrest</th>\n",
       "      <th>...</th>\n",
       "      <th>Ward</th>\n",
       "      <th>Community Area</th>\n",
       "      <th>FBI Code</th>\n",
       "      <th>X Coordinate</th>\n",
       "      <th>Y Coordinate</th>\n",
       "      <th>Year</th>\n",
       "      <th>Updated On</th>\n",
       "      <th>Latitude</th>\n",
       "      <th>Longitude</th>\n",
       "      <th>Location</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>76494</td>\n",
       "      <td>885046</td>\n",
       "      <td>10057802</td>\n",
       "      <td>HY246896</td>\n",
       "      <td>07/18/2014 09:30:00 AM</td>\n",
       "      <td>021XX W SUPERIOR ST</td>\n",
       "      <td>2820</td>\n",
       "      <td>OTHER OFFENSE</td>\n",
       "      <td>TELEPHONE THREAT</td>\n",
       "      <td>RESIDENCE</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>26</td>\n",
       "      <td>1162001.0</td>\n",
       "      <td>1904982.0</td>\n",
       "      <td>2014</td>\n",
       "      <td>02/04/2016 06:33:39 AM</td>\n",
       "      <td>41.894939</td>\n",
       "      <td>-87.680461</td>\n",
       "      <td>(41.894939076, -87.680461181)</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1 rows × 23 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       Unnamed: 0        ID Case Number                    Date  \\\n",
       "76494      885046  10057802    HY246896  07/18/2014 09:30:00 AM   \n",
       "\n",
       "                     Block  IUCR   Primary Type       Description  \\\n",
       "76494  021XX W SUPERIOR ST  2820  OTHER OFFENSE  TELEPHONE THREAT   \n",
       "\n",
       "      Location Description  Arrest  ...  Ward  Community Area  FBI Code  \\\n",
       "76494            RESIDENCE   False  ...   1.0            24.0        26   \n",
       "\n",
       "       X Coordinate  Y Coordinate  Year              Updated On   Latitude  \\\n",
       "76494     1162001.0     1904982.0  2014  02/04/2016 06:33:39 AM  41.894939   \n",
       "\n",
       "       Longitude                       Location  \n",
       "76494 -87.680461  (41.894939076, -87.680461181)  \n",
       "\n",
       "[1 rows x 23 columns]"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "crimes[crimes[\"District\"] == 13.0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f7fe6d45d10>"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
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   ],
   "source": [
    "district_df = pd.DataFrame({'District 11': district_11_crimes_per_year.values, 'District 20': district_20_crimes_per_year.values}, index=[i for i in range(2012, 2018)])\n",
    "district_df.plot.bar(figsize=(10,10))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Conhecimento aplicado ao EP\n",
    "\n",
    "A partir das aulas sobre Análise de Dados Exploratórias, os alunos adquiriram maiores conhecimentos para decidir quais os recursos a serem utilizados para a resolução do EP. Infelizmente, pela natureza dos datasets, não foi possível aplicar uma grande variedade de gráficos para uma análise mais robusta. De qualquer forma, as perguntas aqui trabalhadas não necessitavam de muitos recursos para serem respondidas.\n",
    "\n",
    "Para muitos dos casos, tornou-se muito importante o uso de conhecimentos estatísticos, como análise de: valor máximo, mínimo, média, quartis, entre outros. Isso reflete também no uso de alguns boxplots para facilitar na conclusão de algumas perguntas. Além disso, os gráficos mais utilizados para o exercício programa foram os gráficos de setores e barras. Eles foram fundamentais para resumir um grande número de informações que sem auxílio visual, dificultaria a interpretação do dataset. A partir dos gráficos, diversas questões foram respondidas.\n",
    "\n",
    "Não foram utilizados muitos conceitos que não foram vistos em aula para a construção da Análise de Dados. Algumas pesquisas feitas foram mais em relação à documentação do Pandas para utilizá-la de forma correta. Além disso, foram feitas algumas pesquisas para auxiliar na interpretação de alguns campos e dados do dataset. Um exemplo, para responder à questão 2, o integrante Kaique realizou uma pesquisa sobre outros feriados dos Estados Unidos. Outro exemplo, para responder à questão 3, o aluno Yurick buscou os crimes por códigos IUCR e FBI para distinguir os tipos de crime."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Discussão e próximos passos\n",
    "\n",
    "A discussão em grupo em relação à construção do notebook se resumiu mais em qual seria o formato a ser usado no documento e em quais perguntas seriam colocadas nele. No final, ficou decidido colocar todas as perguntas individuais de cada integrante do grupo com suas respectivas resoluções, para que possa ser feito uma visualização e comparação das diferentes formas adotadas de análise para responder a pergunta, e das eventuais perguntas surgidas do pensamento conjunto dos integrantes. \n",
    "\n",
    "Em relação aos próximos passos que seriam adotados caso os integrantes estivessem em uma empresa, o grupo acredita que a realização de novas pesquisas específicas para tentar entender e esclarecer melhor alguns resultados obtidos na análise exploratória e o compartilhamento das conclusões adquiridas seriam uma boa ideia.<br>\n",
    "As pesquisas específicas referidas focariam em tentar responder perguntas advindas dos resultados das análises, como na questão 8 em que não foi encontrado motivo explícito para que os distritos policiais 13 e 31 possuam tão poucas ocorrências de crimes.\n",
    "Já o compartilhamento das conclusões focaria em tentar utilizar de maneira construtiva os dados e a análise, como na questão 6 em que é obtido as quantidades de incidentes criminosos envolvendo agressão doméstica e as 'Community Areas' que possuem os maiores números nesse quesito. Assim, o resultado dessa análise poderia ser compartilhado com as autoridades locais e utilizado para promover algum tipo de ação social ou conscientizadora nas áreas com os piores números."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Some examples of analysis/visualizations:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "1) Present the mean, variance, min, median and max values for each attribute. If the dataset has too many attibutes, choose the more significant ones. Present your code and some remarks to help the understanding."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "2) How well distributed are the dataset in relation the each attribute. How can you visually check that? Hint: plot a histogram"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "3) Is there any attribute that can be used to better interpret the dataset? Show this in a graphic where you used this attribute to group (groupby) the data."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "4) Is there any outlier's pattern? "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "5) Make your own remarks about the dataset. Try to use one or more graphics to justify your remarks."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "6) Is there any symmetry that can be stressed?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "7) How important was EDA to help you understand the dataset?"
   ]
  }
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