AVA_Tutorial_COM360.ipynb

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    "## Mineração de dados - Semana 3: \n",
    "### Exercício sobre análise descritiva de dados (ADD)\n",
    "\n",
    "Neste exercício, iremos fazer a análise descritiva de uma base de dados com o objetivo de aplicar o conteúdo visto nesta semana. \n",
    "\n",
    "1. Crie um novo notebook e inclua uma descrição para ele. \n",
    "\n",
    "2. Nesta atividade, além da biblioteca **pandas**, vamos importar as bibliotecas **seaborn** e **matplotlib** para gerar os gráficos."
   ]
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   "source": [
    "import pandas as pd\n",
    "import seaborn as sb\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "sb.set(rc={'figure.figsize':(15,8)})"
   ]
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   "source": [
    "A base de dados deste exercício é a \"Quantidade de alunos por tipo de ensino\" da rede estadual de São Paulo de 2021. A base tem 25 atributos, entre os quais estão:\n",
    "\n",
    "* **MUN**: município\n",
    "* **ZONA**: zona (1 = urbana, 2 - rural)\n",
    "* **ANOS INICIAIS**: Anos iniciais do ensino fundamental\n",
    "* **ANOS FINAIS**: Anos finais do ensino fundamental\n",
    "* **ENSINO MEDIO**: Anos iniciais do ensino fundamental\n",
    "\n",
    "Os dados estão sendo importados diretamente do [Portal de Dados Abertos da Educação do Estado de São Paulo](https://dados.educacao.sp.gov.br/dataset/quantidade-de-alunos-por-tipo-de-ensino-da-rede-estadual).\n",
    "O dicionário de dados está disponível neste [link](https://dados.educacao.sp.gov.br/dicion%C3%A1rio-de-dados-de-quantidade-de-alunos-por-tipo-de-ensino)\n",
    "\n",
    "3. Importe a base de dados direto da URL a seguir e verifique as primeiras linhas. O arquivo contém 50 registros. Olhando os 20 primeiros, podemos observar que há valores ausentes nos atributos **idade**, **uf** e **renda**. \n",
    "\n"
   ]
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       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>CDREDE</th>\n",
       "      <th>DE</th>\n",
       "      <th>CODMUN</th>\n",
       "      <th>MUN</th>\n",
       "      <th>CATEG</th>\n",
       "      <th>COD_ESC</th>\n",
       "      <th>TIPOESC</th>\n",
       "      <th>CODVINC</th>\n",
       "      <th>NOMESC</th>\n",
       "      <th>ENDESC</th>\n",
       "      <th>...</th>\n",
       "      <th>ZONA</th>\n",
       "      <th>ED_INFANTIL</th>\n",
       "      <th>CLASSES ESPECIAIS</th>\n",
       "      <th>SALA DE RECURSO</th>\n",
       "      <th>ANOS INICIAIS</th>\n",
       "      <th>ANOS FINAIS</th>\n",
       "      <th>ENSINO MEDIO</th>\n",
       "      <th>EJA FUNDAMENTA_AI</th>\n",
       "      <th>EJA FUNDAMENTAL_AF</th>\n",
       "      <th>EJA ENSINO MEDIO</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>10101</td>\n",
       "      <td>NORTE 1</td>\n",
       "      <td>100</td>\n",
       "      <td>SAO PAULO</td>\n",
       "      <td>1</td>\n",
       "      <td>73</td>\n",
       "      <td>8</td>\n",
       "      <td>NaN</td>\n",
       "      <td>JULIO DE FARIA E SOUZA PROFESSOR</td>\n",
       "      <td>POMPEU PEQUENO DE SOUZA BRASIL</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>456</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>10101</td>\n",
       "      <td>NORTE 1</td>\n",
       "      <td>100</td>\n",
       "      <td>SAO PAULO</td>\n",
       "      <td>1</td>\n",
       "      <td>127</td>\n",
       "      <td>8</td>\n",
       "      <td>NaN</td>\n",
       "      <td>JOSE ALTENFELDER SILVA PROFESSOR</td>\n",
       "      <td>CARLOS ALBERTO VANZOLINI</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>9</td>\n",
       "      <td>717</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>10101</td>\n",
       "      <td>NORTE 1</td>\n",
       "      <td>100</td>\n",
       "      <td>SAO PAULO</td>\n",
       "      <td>1</td>\n",
       "      <td>139</td>\n",
       "      <td>8</td>\n",
       "      <td>NaN</td>\n",
       "      <td>MARIANO DE OLIVEIRA PROFESSOR</td>\n",
       "      <td>RUA ALMIRANTE ISAIAS DE NORONHA</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>372</td>\n",
       "      <td>1200</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>240</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>10101</td>\n",
       "      <td>NORTE 1</td>\n",
       "      <td>100</td>\n",
       "      <td>SAO PAULO</td>\n",
       "      <td>1</td>\n",
       "      <td>164</td>\n",
       "      <td>8</td>\n",
       "      <td>NaN</td>\n",
       "      <td>PIO TELLES PEIXOTO PROFESSOR</td>\n",
       "      <td>ARTUR ORLANDO</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>10</td>\n",
       "      <td>0</td>\n",
       "      <td>251</td>\n",
       "      <td>336</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>63</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>10101</td>\n",
       "      <td>NORTE 1</td>\n",
       "      <td>100</td>\n",
       "      <td>SAO PAULO</td>\n",
       "      <td>1</td>\n",
       "      <td>231</td>\n",
       "      <td>8</td>\n",
       "      <td>NaN</td>\n",
       "      <td>CHIQUINHA RODRIGUES</td>\n",
       "      <td>CAIAPE</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>28</td>\n",
       "      <td>987</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>10101</td>\n",
       "      <td>NORTE 1</td>\n",
       "      <td>100</td>\n",
       "      <td>SAO PAULO</td>\n",
       "      <td>1</td>\n",
       "      <td>243</td>\n",
       "      <td>8</td>\n",
       "      <td>NaN</td>\n",
       "      <td>ISABEL VIEIRA DE SERPA E PAIVA PROFESSORA</td>\n",
       "      <td>JERIMANDUBA</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>28</td>\n",
       "      <td>462</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>10101</td>\n",
       "      <td>NORTE 1</td>\n",
       "      <td>100</td>\n",
       "      <td>SAO PAULO</td>\n",
       "      <td>1</td>\n",
       "      <td>395</td>\n",
       "      <td>8</td>\n",
       "      <td>NaN</td>\n",
       "      <td>JOAQUIM LUIZ DE BRITO PROFESSOR</td>\n",
       "      <td>RUA SANTA LUCIA FILIPPINI</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>280</td>\n",
       "      <td>413</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>157</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>10101</td>\n",
       "      <td>NORTE 1</td>\n",
       "      <td>100</td>\n",
       "      <td>SAO PAULO</td>\n",
       "      <td>1</td>\n",
       "      <td>644</td>\n",
       "      <td>8</td>\n",
       "      <td>NaN</td>\n",
       "      <td>PIO XII</td>\n",
       "      <td>OTAVIO DOMINGUITO</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>322</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>10101</td>\n",
       "      <td>NORTE 1</td>\n",
       "      <td>100</td>\n",
       "      <td>SAO PAULO</td>\n",
       "      <td>1</td>\n",
       "      <td>36959</td>\n",
       "      <td>8</td>\n",
       "      <td>NaN</td>\n",
       "      <td>MANUEL BANDEIRA</td>\n",
       "      <td>JULIO CESAR LEAL</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>432</td>\n",
       "      <td>997</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>147</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>10101</td>\n",
       "      <td>NORTE 1</td>\n",
       "      <td>100</td>\n",
       "      <td>SAO PAULO</td>\n",
       "      <td>1</td>\n",
       "      <td>37655</td>\n",
       "      <td>8</td>\n",
       "      <td>NaN</td>\n",
       "      <td>UBALDO COSTA LEITE DOUTOR</td>\n",
       "      <td>JOSE DA COSTA PEREIRA</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>573</td>\n",
       "      <td>714</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>10 rows × 24 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   CDREDE       DE  CODMUN        MUN  CATEG  COD_ESC  TIPOESC  CODVINC  \\\n",
       "0   10101  NORTE 1     100  SAO PAULO      1       73        8      NaN   \n",
       "1   10101  NORTE 1     100  SAO PAULO      1      127        8      NaN   \n",
       "2   10101  NORTE 1     100  SAO PAULO      1      139        8      NaN   \n",
       "3   10101  NORTE 1     100  SAO PAULO      1      164        8      NaN   \n",
       "4   10101  NORTE 1     100  SAO PAULO      1      231        8      NaN   \n",
       "5   10101  NORTE 1     100  SAO PAULO      1      243        8      NaN   \n",
       "6   10101  NORTE 1     100  SAO PAULO      1      395        8      NaN   \n",
       "7   10101  NORTE 1     100  SAO PAULO      1      644        8      NaN   \n",
       "8   10101  NORTE 1     100  SAO PAULO      1    36959        8      NaN   \n",
       "9   10101  NORTE 1     100  SAO PAULO      1    37655        8      NaN   \n",
       "\n",
       "                                      NOMESC                           ENDESC  \\\n",
       "0           JULIO DE FARIA E SOUZA PROFESSOR   POMPEU PEQUENO DE SOUZA BRASIL   \n",
       "1           JOSE ALTENFELDER SILVA PROFESSOR         CARLOS ALBERTO VANZOLINI   \n",
       "2              MARIANO DE OLIVEIRA PROFESSOR  RUA ALMIRANTE ISAIAS DE NORONHA   \n",
       "3               PIO TELLES PEIXOTO PROFESSOR                    ARTUR ORLANDO   \n",
       "4                        CHIQUINHA RODRIGUES                           CAIAPE   \n",
       "5  ISABEL VIEIRA DE SERPA E PAIVA PROFESSORA                      JERIMANDUBA   \n",
       "6            JOAQUIM LUIZ DE BRITO PROFESSOR        RUA SANTA LUCIA FILIPPINI   \n",
       "7                                    PIO XII                OTAVIO DOMINGUITO   \n",
       "8                            MANUEL BANDEIRA                 JULIO CESAR LEAL   \n",
       "9                  UBALDO COSTA LEITE DOUTOR            JOSE DA COSTA PEREIRA   \n",
       "\n",
       "   ... ZONA ED_INFANTIL CLASSES ESPECIAIS  SALA DE RECURSO  ANOS INICIAIS  \\\n",
       "0  ...  1.0           0                 0                0            456   \n",
       "1  ...  1.0           0                 0                9            717   \n",
       "2  ...  1.0           0                 0                0              0   \n",
       "3  ...  1.0           0                 0               10              0   \n",
       "4  ...  1.0           0                 3               28            987   \n",
       "5  ...  1.0           0                 0               28            462   \n",
       "6  ...  1.0           0                 0                0              0   \n",
       "7  ...  1.0           0                 0                0            322   \n",
       "8  ...  1.0           0                 0                0              0   \n",
       "9  ...  1.0           0                 0                0              0   \n",
       "\n",
       "   ANOS FINAIS  ENSINO MEDIO  EJA FUNDAMENTA_AI  EJA FUNDAMENTAL_AF  \\\n",
       "0            0             0                  0                   0   \n",
       "1            0             0                  0                   0   \n",
       "2          372          1200                  0                   0   \n",
       "3          251           336                  0                   0   \n",
       "4            0             0                  0                   0   \n",
       "5            0             0                  0                   0   \n",
       "6          280           413                  0                   0   \n",
       "7            0             0                  0                   0   \n",
       "8          432           997                  0                   0   \n",
       "9          573           714                  0                   0   \n",
       "\n",
       "   EJA ENSINO MEDIO  \n",
       "0                 0  \n",
       "1                 0  \n",
       "2               240  \n",
       "3                63  \n",
       "4                 0  \n",
       "5                 0  \n",
       "6               157  \n",
       "7                 0  \n",
       "8               147  \n",
       "9                 0  \n",
       "\n",
       "[10 rows x 24 columns]"
      ]
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   ],
   "source": [
    "url = 'https://dados.educacao.sp.gov.br/sites/default/files/Quantidade%20de%20alunos%20por%20tipo%20de%20ensino%20da%20rede%20estadual%20-%202021.csv'\n",
    "escolas = pd.read_csv(url)\n",
    "escolas.head(10)"
   ]
  },
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   "source": [
    "4. A função **info()**) mostra que há 5351 registros para a maioria dos atributos, portanto é o número de escolas."
   ]
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     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 5351 entries, 0 to 5350\n",
      "Data columns (total 24 columns):\n",
      " #   Column              Non-Null Count  Dtype  \n",
      "---  ------              --------------  -----  \n",
      " 0   CDREDE              5351 non-null   int64  \n",
      " 1   DE                  5351 non-null   object \n",
      " 2   CODMUN              5351 non-null   int64  \n",
      " 3   MUN                 5351 non-null   object \n",
      " 4   CATEG               5351 non-null   int64  \n",
      " 5   COD_ESC             5351 non-null   int64  \n",
      " 6   TIPOESC             5351 non-null   int64  \n",
      " 7   CODVINC             259 non-null    float64\n",
      " 8   NOMESC              5351 non-null   object \n",
      " 9   ENDESC              5351 non-null   object \n",
      " 10  NUMESC              5327 non-null   object \n",
      " 11  BAIESC              5348 non-null   object \n",
      " 12  EMAIL               5273 non-null   object \n",
      " 13  FONE1               5328 non-null   float64\n",
      " 14  ZONA                5350 non-null   float64\n",
      " 15  ED_INFANTIL         5351 non-null   int64  \n",
      " 16  CLASSES ESPECIAIS   5351 non-null   int64  \n",
      " 17  SALA DE RECURSO     5351 non-null   int64  \n",
      " 18  ANOS INICIAIS       5351 non-null   int64  \n",
      " 19  ANOS FINAIS         5351 non-null   int64  \n",
      " 20  ENSINO MEDIO        5351 non-null   int64  \n",
      " 21  EJA FUNDAMENTA_AI   5351 non-null   int64  \n",
      " 22  EJA FUNDAMENTAL_AF  5351 non-null   int64  \n",
      " 23  EJA ENSINO MEDIO    5351 non-null   int64  \n",
      "dtypes: float64(3), int64(14), object(7)\n",
      "memory usage: 1003.4+ KB\n"
     ]
    }
   ],
   "source": [
    "escolas.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "NwjcqMS4N-mD"
   },
   "source": [
    "### Distribuição de frequências\n",
    "\n",
    "5. Vamos observar a distribuição de alguns atributos, gerando o histograma do atributo **ANOS INICIAIS** com a função **displot()**. "
   ]
  },
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\n",
      "text/plain": [
       "<Figure size 500x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sb.displot(escolas['ANOS INICIAIS'])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "vBQd56opXr4s"
   },
   "source": [
    "6. Os dados têm muitos valores 0 no campo **ANOS INICIAIS** porque há escolas que têm somente os anos finais do fundamental, outras têm somente ensino médio etc. Vamos filtrar os dados e ver novamente selecionando o valor de 10 pastas para o histograma."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 365
    },
    "id": "pM14yqrNWDjg",
    "outputId": "cbd85fcf-f070-4f99-8db0-ec520dc0639f"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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HHnpIe/fu1Z49e9S7d2+1atVKKSkpuueeezR06FCtX7/es/6zzz6rzz//XDNnztQrr7yivXv3qn///gHsCACA0nMG8soPHDigdevWadmyZWrSpIkk6amnntJnn32md955R4cPH1ZsbKwGDBggSapbt662bdum+fPnq2XLlkpPT9ebb76pOXPmqGnTppKkadOmKTk5WV999ZVuuOGGgPUGAEBpBHQPOSoqSvPmzVOjRo0802w2m2w2m7KysrR582a1bNnSa50WLVpoy5YtsixLW7Zs8UwrVKdOHdWqVUupqakV0wQAAOUgoHvIkZGRuvnmm72mffDBBzpw4IBGjhypN954Q9HR0V7za9asqezsbB05ckTp6emKiopSSEhIkWXS0tLKVJvTWbbnKg7H7+sXPskINE8NNvmnHtvvlzaVbHy/1+SDUtXkQ89lqen0+1UgFF5/oOuoSPR84TOl34AG8h99+eWXGjFihNq2bavWrVsrJydHwcHBXssU/p2Xl6fs7Owi8yUpJCREubm5Ptdht9sUFVXF5/X/yOGwy+l0lNt4Ptdht3su/VmP01HysSuqptLwpabS9OyLwgeKyMgwv15PSZlSR0Wi5wtfoPs1JpDXrFmjwYMHq3Hjxpo6daqkgmDNy8vzWq7w77CwMIWGhhaZLxWceR0W5vsN63Zbyso66fP6UsEDaOHGdbncys93lWm88uByuz2XfqnHVhBM+S6XZBlSkw9KVZMPPftUk6ugpqysbM/vgVB4vw50HRWJni/8nv3db0l38IwI5FdffVUTJkxQcnKynn/+ec9eb+3atZWRkeG1bEZGhsLDwxUREaHo6GgdPXpUeXl5XnvKGRkZqlWrVplqys8vv41iWZYsy4+P1qWoo+AX+aUezyHbUozv75p8UZqafOm5LDUVPLkL/AOkKXVUJHq+8AW634C/QLBs2TKNGzdO3bt317Rp07yCtWnTptq0aZPX8hs2bFDjxo1lt9vVpEkTud1uz8ldkrRv3z6lp6crISGhwnoAAKCsAhrI+/bt08SJE3Xrrbeqd+/eOnTokA4ePKiDBw/qt99+U8+ePfXtt99q6tSp2rNnjxYuXKj3339fvXr1kiTVqlVL7dq10+jRo7Vx40Z9++23GjhwoJo1a6b4+PhAtgYAQKkE9JD1Bx98oFOnTunDDz/Uhx9+6DWvY8eOmjRpkmbNmqUpU6bolVde0WWXXaYpU6Z4vRVq3Lhxmjhxoh577DFJUmJiokaPHl2hfQAAUFYBDeQ+ffqoT58+Z10mMTFRiYmJZ5wfHh6u8ePHa/z48eVdHgAAFSbgryEDAAACGQAAIxDIAAAYgEAGAMAABDIAAAYgkAEAMACBDACAAQhkAAAMQCADAGAAAhkAAAMQyAAAGIBABgDAAAQyAAAGIJABADAAgQwAgAEC+n3IAErH4Qjsc+jC6y+8dLstud1WIEsCLhgEMnAeiAgPktttKTIyLNClSJKnDpfbraNHThLKQDkgkIHzQGiIU3a7Tf/+YLvSD58IWB02m00Oh10ul1s1osLULbm+7HYbgQyUAwIZOI9kZJ7UzwePB+z6bTabnE6H8vNdsixCGChPfnlBKi0tzR/DAgBwwfIpkOvXr69vv/222HmbN2/W7bffXqaiAACobEp8yHrhwoU6efKkJMmyLL3++utau3ZtkeW++uorBQcHl1+FAABUAiUO5NzcXL300kuSCl5Hev3114ssY7fbFRERob59+5ZfhQAAVAIlDuS+fft6gvbaa6/Va6+9pri4OL8VBgBAZeLTWdbbt28v7zoAAKjUfH7b07p16/Tf//5X2dnZcrvdXvNsNpsmTpxY5uIAAKgsfArkhQsXavLkyQoJCVH16tVls9m85v/xbwAAcHY+BfKrr76qO+64QxMmTOCMagAAyoFP70M+dOiQ7r77bsIYAIBy4lMgN2jQQLt27SrvWgAAqLR8OmQ9cuRIPfnkkwoPD9f111+vsLCi30Dzpz/9qczFAQBQWfgUyF27dpXb7dbIkSPPeALX999/X6bCAACoTHwK5HHjxnEmNQAA5cinQL7rrrvKuw4AACo1nwI5NTX1nMskJCT4MjQAAJWST4Hcs2dP2Ww2ry8o/+MhbF5DBgCg5HwK5MWLFxeZdvLkSW3evFlvvfWWZs6cWebCAACoTHwK5GbNmhU7vXXr1goPD9fs2bM1d+7cMhUGAEBl4tMHg5xN06ZNtWnTpvIeFgCAC1q5B/LHH3+sKlWqlPewAABc0Hw6ZH3fffcVmeZ2u5WWlqaff/5ZDz/8cJkLAwCgMvEpkE8/u7qQ3W5XTEyMevfurU6dOpW5MAAAKhOfAnnJkiXlXQcAAJWaT4FcaO3atdq0aZOysrJUvXp1NWnSRK1atSqv2gAAqDR8CuS8vDz169dPn3/+uRwOh6KionTkyBHNnTtXLVq00Ny5c/muZAAASsGns6xnzpypLVu2aPLkyfr222/1+eef65tvvtFzzz2nr7/+WrNnzy7vOgEAuKD5FMjvvvuuHnvsMd15551yOBySJKfTqQ4dOuixxx7TO++8U65FAgBwofMpkDMzM9WgQYNi5zVo0EDp6ellKgoAgMrGp0C+4oortGXLlmLnpaamqnbt2mUqCgCAysank7ruvfdeTZo0SaGhoWrXrp0uueQSHTp0SO+++67+9a9/6bHHHivvOgEAuKD5FMhdu3bVtm3bNHXqVL3wwgue6ZZlqWPHjnrkkUfKrUAAACoDn9/2NGHCBD344IPatGmTjh07JpvNpjZt2qhu3brlXSMAABe8Ur2GvGPHDnXq1Ekvv/yyJKlu3brq2rWrunXrpn/84x8aOHCg9u3b53Mxc+fOVc+ePb2mjR49WrGxsV4/SUlJnvlut1svvviiWrVqpfj4eD388MP68ccffa4BAIBAKHEg//TTT7rvvvt06NAh1alTx2teUFCQhg4dqqNHj6pbt24+nWW9dOlSzZgxo8j0HTt2qE+fPvr88889PytXrvTMnzVrlpYtW6Zx48Zp+fLlcrvd6tWrl/Ly8kpdAwAAgVLiQJ43b54uuugivfHGG0pOTvaaFxYWpvvvv18rV65USEiI5s6dW+IC0tPT1adPH02dOlVXXXWV1zzLsrR79241bNhQNWrU8PxUr15dUsGh84ULF6p///5q3bq1rr32Wk2fPl1paWlavXp1iWsAACDQShzI69evV69evTxhWJwaNWrowQcf1Lp160pcwHfffaegoCC9/fbbuv76673m/fDDDzp58qSuvvrqYtfdvn27Tpw4oZYtW3qmRUZGqkGDBkpNTS1xDQAABFqJT+rKyMgosgdbnJiYGKWlpZW4gKSkJK/XhE+3c+dOSQXfLrV27VrZ7XYlJiZqwIABioiI8FzPH9/3XLNmzVLVUByn06e3aHs4HL+vb7PZZLPZyjReefDUYJN/6rH9fmlTycb3e00+KFVNPvTs95r86fR+/1fH6ff1C1Fhfxd6n6erbD2b0m+JA7l69erKyMg453JHjhxRtWrVylRUoZ07d8put6tmzZqaM2eOfvjhB02ePFm7du3SK6+8ouzsbEkq8kUWISEhOnbsmM/Xa7fbFBVVpUy1n87hsMvpdJTbeD7XYbd7Lv1Zj9NR8rErqqbS8KWm0vTsC9NuJ6fD4XnwiowMC3A1FaOy9Hm6ytZzoPstcSAnJCQoJSVF7dq1O+tyb7755hk/VrO0+vbtq27duikqKkpSwd53jRo11LlzZ23dulWhoaGSCl5LLvxdknJzcxUW5vsN63Zbyso6WabaHQ67Z+O6XG7l57vKNF55cLndnku/1GMreKDOd7kky5CafFCqmnzo2e81+dNp/bpcBTVlZWV7fr8QFf4vX+h9nq6y9ezvfku6g1fiQO7Zs6e6du2qSZMmacCAAQoJCfGan5eXpxkzZmjt2rWaN29e6ao9A7vd7gnjQtdcc40kKS0tzXOoOiMjQ1dccYVnmYyMDMXGxpbpuvPzy2+jWJYly/Ljo3Up6ij4RX6px3PIthTj+7smX5SmJl969ndN/lRcvwVPOC/8B+3K0ufpKlvPge63xIHcqFEjjRgxQhMnTtRbb72lli1b6rLLLpPL5dIvv/yijRs36siRI3riiSfUqlWrcilu6NChysjI0KJFizzTtm7dKkmqV6+eLr/8clWtWlUbN270BHJWVpa2bdumHj16lEsNAABUhFJ9Ulf37t117bXXasGCBfroo4+Um5srSapSpYpuuukmPfjgg0XOlC6L2267Tf369dNLL72kO++8U/v27dPYsWPVvn17zyeC9ejRQ1OnTlX16tV16aWXasqUKYqOjlbbtm3LrQ4AAPyt1B+d2aRJEzVp0kRSwdcwOp1ORUZGlnthkvSXv/xFM2bM0Lx58/Svf/1LERERuuOOO/Tkk096lunfv7/y8/M1evRo5eTkKCEhQQsWLFBQUJBfagIAwB98+izrQmd7T7IvJk2aVGTa7bffrttvv/2M6zgcDg0ZMkRDhgwp11oAAKhIleNNZgAAGI5ABgDAAAQyAAAGIJABADAAgQwAgAEIZAAADEAgAwBgAAIZAAADEMgAABiAQAYAwAAEMgAABiCQAQAwAIEMAIABCGQAAAxAIAMAYAACGQAAAxDIAAAYgEAGAMAABDIAAAYgkAEAMACBDACAAQhkAAAMQCADAGAAAhkAAAMQyAAAGIBABgDAAAQyAAAGIJABADAAgQwAgAEIZAAADEAgAwBgAAIZAAADEMgAABiAQAYAwAAEMgAABiCQAQAwAIEMAIABCGQAAAxAIAMAYAACGQAAAxDIAAAYgEAGAMAABDIAAAYgkAEAMACBDACAAQhkAAAMQCADAGAAAhkAAAMQyAAAGIBABgDAAAQyAAAGMCqQ586dq549e3pN+/7779WjRw/Fx8crKSlJixcv9prvdrv14osvqlWrVoqPj9fDDz+sH3/8sSLLBgCgzIwJ5KVLl2rGjBle044cOaIHHnhAV1xxhVatWqVHH31UU6dO1apVqzzLzJo1S8uWLdO4ceO0fPlyud1u9erVS3l5eRXcAQAAvnMGuoD09HQ988wz2rhxo6666iqvea+99pqCgoI0duxYOZ1O1a1bVwcOHNC8efPUqVMn5eXlaeHChRo8eLBat24tSZo+fbpatWql1atXq3379hXfEAAAPgj4HvJ3332noKAgvf3227r++uu95m3evFnNmjWT0/n784YWLVpo//79OnTokLZv364TJ06oZcuWnvmRkZFq0KCBUlNTK6wHAADKKuB7yElJSUpKSip2XlpammJiYrym1axZU5L066+/Ki0tTZJUu3btIssUzgPgXw5HwJ/Xe7jdltxuK9BlAD4JeCCfTU5OjoKDg72mhYSESJJyc3OVnZ0tScUuc+zYsTJdt9NZtgeZ0x+kbDabbDZbmcYrD54abPJPPbbfL20q2fh+r8kHparJh579XpM/ndZvZJVgud2WIiPDAlfPH7jcbv2WlSPLKr9QLvxfNumJh79Vtp5N6dfoQA4NDS1yclZubq4kKTw8XKGhoZKkvLw8z++Fy4SF+f4gYbfbFBVVxef1/8jhsMvpdJTbeD7XYbd7Lv1Zj9NR8rErqqbS8KWm0vTsC9NuJ6fDoSphwbLbbVrx4U4dPHIy0CWpRlS4utwao4suCvfL+CY98agola3nQPdrdCBHR0crIyPDa1rh37Vq1VJ+fr5n2hVXXOG1TGxsrM/X63Zbysoq2wOMw2H3bFyXy638fFeZxisPLrfbc+mXemwFD9T5LpdUwh0Uv9fkg1LV5EPPfq/Jn07rt7CmtMPH9XPG8cDV9D8uV0E9WVnZnt/LQ+H/cnmPa7LK1rO/+y3pDp7RgZyQkKDly5fL5XLJ8b89kA0bNqhOnTq6+OKLFRERoapVq2rjxo2eQM7KytK2bdvUo0ePMl13fn75bRTLssr1EFpZ6ij4RX6px3PIthTj+7smX5SmJl969ndN/lRsv4Zsu8IaCp4Al/+Dqr/GNVll6znQ/Rr9AkGnTp10/PhxjRo1Srt371ZKSooWLVqk3r17Syp47bhHjx6aOnWqPvroI23fvl0DBgxQdHS02rZtG+DqAQAoOaP3kC+++GLNnz9fEyZMUMeOHVWjRg0NHTpUHTt29CzTv39/5efna/To0crJyVFCQoIWLFigoKCgAFYOAEDpGBXIkyZNKjItLi5OK1asOOM6DodDQ4YM0ZAhQ/xZGgAAfmX0IWsAACoLAhkAAAMQyAAAGIBABgDAAAQyAAAGIJABADAAgQwAgAEIZAAADEAgAwBgAAIZAAADEMgAABiAQAYAwAAEMgAABiCQAQAwAIEMAIABCGQAAAxAIAMAYAACGQAAAxDIAAAYgEAGAMAABDIAAAYgkAEAMACBDACAAQhkAAAMQCADAGAAAhkAAAMQyAAAGIBABgDAAAQyAAAGIJABADAAgQwAgAEIZAAADEAgAwBgAAIZAAADEMgAABiAQAYAwAAEMgAABiCQAQAwAIEMAIABCGQAAAxAIAMAYAACGQAAAxDIAAAYgEAGAMAABDIAAAYgkAEAMACBDACAAQhkAAAMQCADAGAAAhkAAAMQyAAAGIBABgDAAOdFIKenpys2NrbIT0pKiiTp+++/V48ePRQfH6+kpCQtXrw4wBUDAFA6zkAXUBLbt29XSEiI1qxZI5vN5pkeERGhI0eO6IEHHlBSUpLGjBmjr7/+WmPGjFGVKlXUqVOnAFYNAEDJnReBvHPnTl111VWqWbNmkXmvvPKKgoKCNHbsWDmdTtWtW1cHDhzQvHnzCGQAwHnjvDhkvWPHDtWtW7fYeZs3b1azZs3kdP7+3KJFixbav3+/Dh06VFElAgBQJudFIO/cuVOZmZnq3r27/vznP6tr165au3atJCktLU3R0dFeyxfuSf/6668VXisAAL4w/pB1fn6+9u7dq3r16mn48OGqWrWq3nvvPT3yyCN6+eWXlZOTo+DgYK91QkJCJEm5ubk+X6/TWbbnKg7H7+vbbDav174DxVODTf6px/b7pU0lG9/vNfmgVDX50LPfa/Kn0/s1pab/Kazh9P+98lA4XnmPa7LK1rMp/RofyE6nUxs3bpTD4VBoaKgkqWHDhtq1a5cWLFig0NBQ5eXlea1TGMTh4eE+XafdblNUVJWyFX4ah8Mup9NRbuP5XIfd7rn0Zz1OR8nHrqiaSsOXmkrTsy9Mu52cDodxNRU+mEZGhvllfH+Na7LK1nOg+zU+kCWpSpWi4XjNNdfo888/V3R0tDIyMrzmFf5dq1Ytn67P7baUlXXSp3ULORx2z8Z1udzKz3eVabzy4HK7PZd+qcdW8ECd73JJliE1+aBUNfnQs99r8qfT+jWmpv9xuQrqycrK9vxeHgr/l8t7XJNVtp793W9Jd/CMD+Rdu3apS5cumj17tpo3b+6Z/n//93+qV6+e6tevr+XLl8vlcsnxv72UDRs2qE6dOrr44ot9vt78/PLbKJZlybL8+GhdijoKfpFf6vEcsi3F+P6uyRelqcmXnv1dkz8V268h266whoInwOX/oOqvcU1W2XoOdL/Gv0BQt25dXX311Ro7dqw2b96sPXv26LnnntPXX3+tvn37qlOnTjp+/LhGjRql3bt3KyUlRYsWLVLv3r0DXToAACVm/B6y3W7XnDlz9MILL+jJJ59UVlaWGjRooJdfflkxMTGSpPnz52vChAnq2LGjatSooaFDh6pjx44BrhxAIJh2UpfbbcntDvwRBJjP+ECWpEsuuUTPPffcGefHxcVpxYoVFVgRANNEhAfJ7baMO6nL5Xbr6JGThDLO6bwIZAA4l9AQp+x2m/79wXalHz5RbuPabDY5HHa5XO5Sv1Zes3q4uiXXl91uI5BxTgQygAtKRuZJ/XzweLmNZ7PZ5HQ6lJ/vMuLkNVy4jD+pCwCAyoBABgDAAAQyAAAGIJABADAAgQwAgAEIZAAADEAgAwBgAAIZAAADEMgAABiAQAYAwAAEMgAABiCQAQAwAIEMAIABCGQAAAxAIAMAYAACGQAAAxDIAAAYgEAGAMAABDIAAAYgkAEAMACBDACAAQhkAAAMQCADAGAAAhkAAAMQyAAAGIBABgDAAAQyAAAGIJABADAAgQwAgAGcgS4AAC50DodZ+z5utyW32wp0GfgDAhkA/CQiPEhut6XIyLBAl+LF5Xbr6JGThLJhCGQA8JPQEKfsdpv+/cF2pR8+EehyJEk1q4erW3J92e02AtkwBDIA+FlG5kn9fPB4oMuA4cx6YQMAgEqKQAYAwAAEMgAABiCQAQAwAIEMAIABCGQAAAxAIAMAYAACGQAAAxDIAAAYgEAGAMAABDIAAAYgkAEAMACBDACAAQhkAAAMwNcvAkAl5HCceX+scN7ZlilvbrdV6b+fmUAGgEokIjxIbrelyMiwcy5bkmXKi8vt1tEjJyt1KBPIAFCJhIY4Zbfb9O8Ptiv98Ilil7HZbHI47HK53LIs/wdkzerh6pZcX3a7jUA+37ndbr300kt6/fXX9dtvvykhIUFPP/20Lr/88kCXBgBGysg8qZ8PHi92ns1mk9PpUH6+q0ICuVBFHiIv7nr/eP0VfRj9ggjkWbNmadmyZZo0aZKio6M1ZcoU9erVS++8846Cg4MDXR4A4CxKcxjdn/54/RV9GP28D+S8vDwtXLhQgwcPVuvWrSVJ06dPV6tWrbR69Wq1b98+sAUCAM6qJIfR/am4Q/SBOIx+3gfy9u3bdeLECbVs2dIzLTIyUg0aNFBqaiqBDADnibMdRvenQB2iL1KHFchrLwerV6/W448/rm+++UahoaGe6U888YRycnI0d+7cUo9pWWV/3cBmk+z2gtcjjp/Mk8uAExWCnHaFhwYZU49ETSVFTedfPRI1lZSJNTnsNlUND5bb7VZZU7Kkr42f93vI2dnZklTkteKQkBAdO3bMpzELDl/YylxboarhZr2ObVo9EjWVFDWdm2n1SNRUUibWVLhjVSHXVWHX5CeFe8V5eXle03NzcxUWFtgTBAAAKKnzPpBr164tScrIyPCanpGRoVq1agWiJAAASu28D+Rrr71WVatW1caNGz3TsrKytG3bNiUkJASwMgAASu68fw05ODhYPXr00NSpU1W9enVdeumlmjJliqKjo9W2bdtAlwcAQImc94EsSf3791d+fr5Gjx6tnJwcJSQkaMGCBQoKCgp0aQAAlMh5/7YnAAAuBOf9a8gAAFwICGQAAAxAIAMAYAACGQAAAxDIAAAYgEAGAMAABLIfuN1uvfjii2rVqpXi4+P18MMP68cffwx0WT47evSonn76aSUmJqpx48bq2rWrNm/e7Jn/wAMPKDY21uunZ8+envm5ubkaM2aMWrZsqRtuuEGDBg1SZmZmIFopsfT09CI9xcbGKiUlRZL0/fffq0ePHoqPj1dSUpIWL17stf75dB/YuHFjsb3GxsbqL3/5iyRp9uzZxc4/3dKlS/WXv/xFcXFx6tatm7Zt2xaIdkpk7ty5XvdRqXy26bnGCKTiev7444/VqVMn3XDDDUpKStLzzz+vnJwcz/wtW7YUu91P/2TE9evX66677tL111+v5ORkvffeexXW09kU1+/o0aOL9JKUlOSZH/BtbKHczZw502revLn13//+1/r++++tBx980Grbtq2Vm5sb6NJ88sADD1jt27e3UlNTrb1791pjxoyx4uLirD179liWZVktW7a0li1bZmVkZHh+jhw54ll/+PDhVps2bazU1FTrm2++sTp06GB17949QN2UzCeffGI1atTISk9P9+orOzvbyszMtJo3b26NGDHC2r17t7Vy5UqrUaNG1sqVKz3rn0/3gdzcXK8eMzIyrNWrV1uxsbGenp544glryJAhRZYrlJKSYsXFxVlvvfWWtWvXLmvIkCFWs2bNrMOHDweqrTN69dVXrWuvvdbq0aOHZ1p5bNOSjBEoxfWcmppq1a9f35o9e7a1b98+65NPPrESExOt4cOHe5ZZunSp1aZNmyLbvbDn3bt3W40aNbKmTZtm7d6925o/f77VoEED64svvqjwHk9XXL+WZVl33323NW3aNK9eTr+PBnobE8jlLDc317rhhhuspUuXeqYdO3bMiouLs955550AVuab/fv3WzExMdbmzZs909xut9WmTRtrxowZ1qFDh6yYmBjru+++K3b9tLQ069prr7U++eQTz7S9e/daMTEx1pdffun3+n01b94864477ih23pw5c6ybbrrJOnXqlGfaCy+8YLVt29ayrPP/PnDixAnrlltu8Xpgvv32262XX375jOu0bdvWmjx5sufvU6dOWTfffLM1Z84cf5ZaKmlpaVbv3r2t+Ph4Kzk52evBujy26bnGCISz9Txo0CDr/vvv91r+jTfesK677jpPAD3zzDNWnz59zjj+U089Zd19991e0wYOHGg9+OCD5dhFyZ2tX7fbbcXHx1urV68udl0TtjGHrMvZ9u3bdeLECbVs2dIzLTIyUg0aNFBqamoAK/NNVFSU5s2bp0aNGnmm2Ww22Ww2ZWVlaceOHbLZbKpTp06x62/ZskWS1KJFC8+0OnXqqFatWkbfHjt27FDdunWLnbd582Y1a9ZMTufvnzzbokUL7d+/X4cOHTrv7wNz5sxRdna2hg0bJqngq03379+vq6++utjlDx8+rP3793v163Q61bRpU6P6/e677xQUFKS3335b119/vde88tim5xojEM7W84MPPujZxoXsdrtOnTql48ePSzr7/4FU0PPpt4lU0POWLVtkBeBDIM/W7w8//KCTJ0+e8X5swja+ID7L2iRpaWmSfv9ayEI1a9b0zDufREZG6uabb/aa9sEHH+jAgQMaOXKkdu7cqYiICI0dO1br1q1TeHi4kpOT1a9fPwUHBys9PV1RUVEKCQnxGsP022Pnzp2KiopS9+7dtW/fPl155ZXq27evEhMTlZaWppiYGK/la9asKUn69ddfz+v7QGZmphYtWqRBgwbpoosukiTt3r1bLpdLH3zwgSZMmKDc3FwlJCRoyJAhXj0V1+/27dsruoUzSkpK8nq98HTlsU3PNcYll1xS9iZK6Ww9N2jQwOvvU6dOadGiRWrYsKGqV68uSdq1a5eioqJ01113KT09XTExMRowYIDi4uIkFfQcHR3tNU7NmjWVnZ2tI0eOeMapKGfrd+fOnZKkJUuWaO3atbLb7UpMTNSAAQMUERFhxDZmD7mcZWdnSyr4FqrThYSEKDc3NxAllasvv/xSI0aMUNu2bdW6dWvt3LlTubm5iouL0/z589W3b1+9/vrrGj16tKSC2+OPt4Vk9u2Rn5+vvXv36tixY3r88cc1b948xcfH65FHHtH69euVk5NT7PaVCk5gO5/vA8uWLVNERIS6dOnimVb4QBYWFqZ//OMfmjBhgvbu3av77rtPOTk553W/hcpjm55rDJPl5+dr6NCh2rVrl5555hlJBQHz22+/6eTJkxo9erRmzZqlSy65RD169NDu3bslFd9z4d95eXkV28Q57Ny5U3a7XTVr1tScOXM0fPhwff755+rXr5/cbrcR25g95HIWGhoqqeDOWPi7VLCxwsLCAlVWuVizZo0GDx6sxo0ba+rUqZKksWPHatiwYapWrZokKSYmRkFBQRowYICGDh2q0NDQYv8xTb49nE6nNm7cKIfD4dmGDRs21K5du7RgwYJieyr8ZwwPDz+v7wNvvvmmOnTo4FV3hw4dlJiY6LW3c8011ygxMVEff/yxrrjiCklFH4DPh34Llcc2PdcYpjp+/LiefPJJbdq0SS+99JJn77d27dpKTU1VWFiY55vzGjVqpG3btmnJkiUaM2aMQkJCivRc+Ldp275v377q1q2boqKiJBU8VtWoUUOdO3fW1q1bjdjG7CGXs8LDHRkZGV7TMzIyVKtWrUCUVC5effVVPf7447rllls0Z84cz7NCp9PpCeNC11xzjaTfD2cdPXq0yJ3Y9NujSpUqXv+UUkFf6enpio6OLnb7SlKtWrXO2/vA9u3b9eOPP+qOO+4oMu+Phx5r1qypiy66SGlpaedtv6crj216rjFMlJGRoe7du+vrr7/WggULirw8FRkZ6fU1tna7XXXr1lV6erqkgse74noODw9XRESE/xsoBbvd7gnjQqc/VpmwjQnkcnbttdeqatWqXu/Ty8rK0rZt25SQkBDAyny3bNkyjRs3Tt27d9e0adO8Dtn07NlTI0aM8Fp+69atCgoK0lVXXaUmTZrI7XZ7Tu6SpH379ik9Pd3Y22PXrl1q3Lix1zaUpP/7v/9TvXr1lJCQoC1btsjlcnnmbdiwQXXq1NHFF1983t4HNm/e7Kn/dNOnT9dtt93mdZLOTz/9pCNHjqhevXq6+OKLVadOHa9+8/PztXnzZqP7PV15bNNzjWGaY8eO6e9//7syMzO1dOnSIttq7dq1uuGGG7zeh5ufn6/t27erXr16kqSmTZtq06ZNXutt2LBBjRs3lt1uVrwMHTpU999/v9e0rVu3SpLq1atnxjYul3O14WXatGlWs2bNrDVr1ni9ly0vLy/QpZXa3r17reuuu8569NFHi7wXMSsry1qyZIlVv359a9myZdYPP/xgvffee1bz5s2tadOmecYYOHCglZSUZG3YsMHzPuQ/vj/QJC6Xy+rUqZP117/+1UpNTbV2795tTZw40WrYsKG1Y8cO69ChQ1ZCQoI1bNgwa9euXdaqVausRo0aWSkpKZ4xzsf7wIgRI4q8DcayLGvr1q3WddddZz399NPW3r17rU2bNlkdOnSw7r33XsvtdluWZVkrVqyw4uLirJSUFM/7kJs3b27k+5Aty7KGDRvmdR8sj21akjEC6Y89Dxs2zLruuuus9evXF/nfzs/Pt3777Tfrlltusbp27Wpt3brV2r59uzVw4EArISHBOnjwoGVZlrVz507ruuuus6ZMmWLt3r3bWrBggRHvQ7asov2uWbPGiomJsWbOnGkdOHDA+uSTT6ykpCRr4MCBnmUCvY0JZD/Iz8+3Jk+ebLVo0cKKj4+3Hn74YevHH38MdFk+mT17thUTE1Psz7BhwyzLKngT/u233241bNjQuuWWW6zZs2dbLpfLM8aJEyesUaNGWU2bNrWaNm1qDRw40MrMzAxUSyVy8OBBa/jw4daNN95oNWrUyOrSpYuVmprqmf/NN99YnTt39vS8ZMkSr/XPx/tAr169rCeffLLYeV988YXVpUsXKz4+3mrWrJk1YsQI6+jRo17LzJ8/30pMTLTi4uKsbt26Wdu2bauIsn3yxwdryyqfbXquMQLp9J7z8/OtRo0anfF/u7CvAwcOWI8//rjVrFkz6/rrr7cefPBBa8eOHV7jfvrpp1b79u2thg0bWsnJydZ7771X4b0Vp7ht/J///Mfq0KGDFRcXZ914443WpEmTrJycHM/8QG9jm2UF4M1iAADAi1kH+QEAqKQIZAAADEAgAwBgAAIZAAADEMgAABiAQAYAwAAEMgAABiCQAQAwAIEMVJBBgwYpNjZWCxcuLDJv+PDhio2N1Zo1a4pdt2fPnurZs2eR6Tt27NDQoUOVmJiohg0bqnXr1ho4cKC++eabYsfZuXOnBgwYoBtvvFENGzbUTTfdpCeffLJE31v8xxpmzpyp2NhYLVq0qNjlhw8f7vXdtBs3blRsbGyRzwiXCr5j+6GHHtKf//xnxcfHq3379po1a5aOHz9eottBkl577TXFxsaqT58+xc7/6aefFBsbq5SUFK/pW7ZsUZ8+fdS8eXPPbThy5Eivz3AGKgKBDFSA3377TWvWrFFMTIxWrFihM31A3jPPPKOjR4+WaMy33npLnTp10v79+zVgwAAtXLhQAwcO1KFDh9S1a1e9/PLLXsvv2rVLXbp00dGjRzV69GgtXLhQQ4cO1S+//KLOnTvr66+/9qm36dOn68CBAz6t63a7NWjQIA0cOFCXXnqpxo4dq9mzZ6tdu3ZauHCh7rvvPmVlZZVorFWrVikmJkZr167Vr7/+WqJ11q9fr/vuu0+hoaGaMGGCFixYoEcffVRfffWV7r77bv3www8+9QX4gkAGKsC7774rSRo1apT279+vDRs2FFkmLCxMx44d07hx48453rZt2zRq1Cj97W9/0/Lly9WxY0c1a9ZMd955pxYvXqzu3bvr+eef1xdffOFZ5+WXX1ZUVJT+9a9/6fbbb/csv2jRIl100UWaNWuWT70FBwdr5MiRZ3yScTbz58/Xu+++qxkzZmjs2LFq06aNWrZsqb59+2ru3Lnatm2b/vnPf55znD179ujrr7/W0KFDFR4erhUrVpTo+ufMmaO4uDjNmDFDbdq0UfPmzXXPPffolVdeUU5OTpEnNYA/EchABVi1apVatmypFi1a6Morr9Ty5cuLLFO9enU98sgjevfdd/XRRx+ddbw5c+YoPDxco0ePLvZr7oYMGaLatWt7hdmhQ4dkWZbcbrfXsuHh4Ro5cqRuv/12n3obPny4Nm/erMWLF5dqvVOnTmnhwoVKTEzUrbfeWmR+kyZN1L9/f89X/Z3NqlWrVK1aNbVo0UK33XabVq5cqfz8/HOuV3ib/FHNmjU1evRo3XjjjSVrBigHBDLgZ7t27dLWrVvVoUMHSVKHDh300Ucf6dChQ0WW7du3r2JjY8966NrtdmvdunVq2bKlwsLCil0mODhYbdq00ZYtW3TkyBFJUuvWrfXLL7/o3nvv1dKlS7Vnzx5PGCUnJ6tjx44+9depUyclJiZq+vTppTrE+9133+nIkSO65ZZbzrhMv379dM8995x1nPz8fL399ttq3769goKC1LFjRx08eFAff/zxOWto3bq1vvrqK/Xs2VMrV670et34nnvuUZs2bUrcD1BWBDLgZ6tWrdJFF13kOcGpY8eOcrlcWrlyZZFlg4KCNGnSJB05ckTjx48vdryjR4/q+PHjuvTSS896vVdeeaUsy/K8ntqtWzf169dPu3fv1tixY/XXv/5VLVu21ODBg/Xtt9+Wqcdx48bJ6XSW6tB1YV2XXXZZma577dq1OnjwoO666y5JUtOmTXXVVVcVexTij5544gl16dJFW7Zs0ahRo9SmTRvdfPPNevrpp7V3794y1QWUFoEM+NGpU6f09ttvq02bNsrJyVFWVpaqVKmiJk2a6LXXXity+FiSGjRooIcffljvvPPOWffygoKCznrdDodDkrwC8oknntBnn32mF154QXfffbeqVq2qd955R507dy71IefTRUdHa9iwYUpNTdWSJUtKtI7T6ZSkYm+D0li1apXq1KmjK664QllZWcrKylJycrK++OKLc+6xBwcHa+zYsfr00081YcIE3XHHHXK73VqxYoXuvPNOrV69uky1AaXhDHQBwIXsk08+0eHDh7Vy5cpi94g/++wz3XzzzUWm9+vXTx999JGefvppNWnSxGteVFSUwsPD9dNPP531ugsPv9auXdtrerVq1dS+fXu1b99eUsEJYkOGDNGUKVN0xx13KCoqqlQ9Frrnnnv0/vvva9q0aWc9DF3oT3/6kyTp559/PuMymZmZqlq1qoKDg4udf/jwYX366ac6deqUEhISisxfsWKFhgwZcs5aatSoobvvvlt33323JGnDhg0aMmSInn32WbVp06bY1+mB8sa9DPCjVatW6fLLL9fixYu9fl555RVFRESc8bBqcHCwnnvuOR05ckQTJkzwmmez2XTLLbfos88+04kTJ4pd3+Vyac2aNWrcuLGqV6+u9PR03XTTTXr99deLLNugQQMNGDBAeXl5ZX7v7fjx4+VwOEp06Lp+/fq65JJLtHbt2jMuM3r0aLVu3Vp5eXnFzn/77beVn5+vf/7zn0Vu44SEBKWkpJxx3W+++UZ//vOftW7duiLzWrRooYceekiHDx/2vAYP+BuBDPjJwYMH9dlnn6ldu3Zq3ry510+LFi2UnJysTz/9VOnp6cWu37BhQ/Xq1UtvvfWWtm3b5jWvd+/eys7O1tNPPy2Xy1Vk3WnTpunAgQOeD8m45JJL5HQ6tWzZMuXm5hZZfu/evQoJCdGVV15Zpp5r166tYcOGadOmTec8U9xut+v+++/XJ598Uuyh+Q0bNujTTz9VcnLyGfeQU1JSFB8f73nL0uk/nTt3VmZmpj788MNi173qqquUnZ2txYsXF3vYfN++fapRo4aqV69egs6BsuOQNeAnb775pvLz89WuXbti53fo0EGvv/66XnvttTOO8eijj+qjjz7Srl27vKbHxsZq0qRJGjFihLp27apu3brpsssuU0ZGhlJSUrRu3ToNHjzYczjc4XDo2Wef1aOPPqpOnTqpe/fuqlu3rrKzs7Vu3TotXbpUTzzxhKpVq1bmvjt37qz3339f69atU2Rk5FmXvf/++5WamqrHH39cnTt31s033yy73e55Lbp+/foaNGhQset+++232rlzp5566qli5996662qUqWKli9fXuw2qFatmoYNG6ZnnnlG3bp1U+fOnXX55Zfrt99+04cffqg33nhDU6dOlc1mK/2NAPiAQAb8JCUlRddcc41iYmKKnd+kSRNddtllev3119W8efNilyk8dN2lS5ci89q1a+f56MoXX3xRBw8eVPXq1dW0aVP9+9//Vnx8vNfyrVu31muvvaYFCxZozpw5yszMVHBwsBo0aKDp06erbdu2Ze650Pjx4z2vUZ9NUFCQZs2apRUrVuitt97Sf/7zH+Xl5enyyy9Xv3791KNHD4WHhxe77qpVq+RwOJScnFzs/LCwMN12221KSUnRnj17FBISUmSZe++9V1deeaUWL16sadOm6ejRo6pSpYri4uL0yiuvnHG7AP5gs3z5eB0AAFCueA0ZAAADEMgAABiAQAYAwAAEMgAABiCQAQAwAIEMAIABCGQAAAxAIAMAYAACGQAAAxDIAAAYgEAGAMAABDIAAAb4f7pYIh8XLJ7UAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 500x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "filtro_escolas = escolas[escolas['ANOS INICIAIS'] > 0]\n",
    "sb.displot(filtro_escolas['ANOS INICIAIS'],bins=10)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "p5yJj5TDYllT"
   },
   "source": [
    "7. Vamos olhar o atributo **ANOS FINAIS**, já filtrando escolas sem alunos destes anos."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 365
    },
    "id": "uvn1m7xWfWWe",
    "outputId": "16baa306-ed6e-4d11-f8a7-c6f61133f11e"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 500x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "filtro_escolas = escolas[escolas['ANOS FINAIS'] > 0]\n",
    "sb.displot(filtro_escolas['ANOS FINAIS'],bins=10)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "PjKB6r72ft6j"
   },
   "source": [
    "8. Para o atributo **ENSINO MEDIO** faremos igual."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 365
    },
    "id": "oXU796qmf3K6",
    "outputId": "fe461c95-8f93-436c-87e2-03f04487df0d"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 500x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "filtro_escolas = escolas[escolas['ENSINO MEDIO'] > 0]\n",
    "sb.displot(filtro_escolas['ENSINO MEDIO'],bins=10) \n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "C8m44EQsguRM"
   },
   "source": [
    "Podemos observar que a maioria das escolas nas três faixas de ensino tem uma maior concentração de estudantes na faixa de 250 a 500 alunos."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "MA7XNt0shpwJ"
   },
   "source": [
    "9. Que tal agora olharmos a proporção de alunos no ensino regular dos anos iniciais e finais do fundamental e do ensino médio? \n",
    "\n",
    "Vamos somar o total de alunos matriculados em cada período e gerar um gráfico de setores usando a função **pie()** do mattplotlib.\n",
    "\n",
    "O total de alunos desses períodos é de mais de 3.3 milhões."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "XqttJTC5kGj-",
    "outputId": "2648381a-56e0-4d9c-f3f7-4650eb297147"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3319779"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fundamental_iniciais = escolas['ANOS INICIAIS'].sum()\n",
    "fundamental_finais = escolas['ANOS FINAIS'].sum()\n",
    "medio = escolas['ENSINO MEDIO'].sum()\n",
    "(fundamental_iniciais + fundamental_finais + medio)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "W4kbFxPJswO2"
   },
   "source": [
    "A soma dos valores de cada um dos três períodos serão os dados passados como uma lista **alunos** para a função **pie**. Vamos passar também os rótulos para identificar os dados como uma lista **periodo**. O parâmetro **autopct** coloca os percentuais dos dados no gráfico no formato desejado."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 466
    },
    "id": "zQqJyXBKsxs5",
    "outputId": "13b1d1a8-7e04-4346-80cf-981813b803ba"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1500x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "alunos = [fundamental_iniciais,fundamental_finais,medio]\n",
    "periodo = ['fundamental anos iniciais', 'fundamental anos finais', 'ensino medio']\n",
    "\n",
    "plt.pie(alunos,labels=periodo,autopct = '%0.0f%%')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "dC7WTBWGrEbe"
   },
   "source": [
    "Assim, vemos que as maiores concentrações de alunos do ensino público estadual são do ensino fundamentais nos anos finais, seguido de perto pelos alunos do ensino médio.\n",
    "\n",
    "Para simplificar o exemplo, não estamos considerando aqui os alunos do EJA (ensino de jovens e adultos) nem os alunos do ensino infantil."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "vt5W3yUQv_IC"
   },
   "source": [
    "### Posição relativa\n",
    "\n",
    "10. Vamos seguir olhando as medidas de posição relativa dos dados usando o diagrama de caixa (gráfico boxplot) do ensino médio."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 466
    },
    "id": "tQ8nq57Zimd0",
    "outputId": "66f4ea61-4d4a-4ac9-f3b1-08452c5c52c6"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1500x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "filtro_escolas = escolas[escolas['ENSINO MEDIO'] > 0]\n",
    "sb.boxplot(y=filtro_escolas['ENSINO MEDIO'])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "EIsA1LaZYpxs"
   },
   "source": [
    "O boxplot nos mostra que há uma grande concentração de escolas com aproximadamente de 200 a 500 estudantes do ensino médio."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "QMJ4TLWd-4y2"
   },
   "source": [
    "### Tendência central e dispersão\n",
    "\n",
    "11. A seguir, podemos ver as medidas de tendência central e dispersão desses dados com a função **describe()**, olhando a distribuição desses dados. Por exemplo, a média (mean) de alunos dos anos finais do ensino fundamental por escola é de ~257, com desvio padrão de 234,97. Podemos ver também os valores dos quartis para esse atributo."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "fpVzouxc8oS8",
    "outputId": "7942209f-5d5c-47fb-e00f-30943378fba0"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    5351.000000\n",
       "mean      257.373388\n",
       "std       234.971969\n",
       "min         0.000000\n",
       "25%         0.000000\n",
       "50%       253.000000\n",
       "75%       410.500000\n",
       "max      1467.000000\n",
       "Name: ANOS FINAIS, dtype: float64"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "escolas['ANOS FINAIS'].describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "v8uoJ7vDtbAN"
   },
   "source": [
    "12. Vamos agora olhar se há alguma correlação entre atributos desses dados?\n",
    "Para isso vamos usar o gráfico de dispersão para comparar os atributos com a quantidade de alunos das escolas com estudantes do ensino fundamental dos anos iniciais e finais. \n",
    "Primeiro, filtramos escolas cujo número de estudantes seja maior que zero em ambos os casos. Depois, usamos a função **scatterplot()** para plotar os dados."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 502
    },
    "id": "LXsO279jwAp6",
    "outputId": "c3a343e9-c907-4851-81ff-c4eebaa3bd88"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1500x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "filtro_escolas = escolas[(escolas['ANOS INICIAIS'] > 0) & (escolas['ANOS FINAIS'] > 0)]\n",
    "sb.scatterplot(x=filtro_escolas['ANOS INICIAIS'],y=filtro_escolas['ANOS FINAIS'])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "hnCgsT16jg10"
   },
   "source": [
    "Embora haja alguma dispersão pelo gráfico, podemos ver uma grande concentração de escolas de ambos os períodos de ensino fundamental na faixa entre 1 e 600 estudantes, mostrando uma correlação positiva. Isso faz sentido, visto que filtramos escolas que abrigam os dois períodos de ensino e que, portanto, têm uma capacidade proporcional de alocar esses estudantes. Além do mais, a quantidade de estudantes tende a ser proporcional ao local na qual elas estão."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "qykUYJwjZYjG"
   },
   "source": [
    "Com isso, concluímos o exercício de análise descritiva de dados desta semana. \n",
    "Para praticar mais, sugiro que você escolha um outro conjunto de dados do seu interesse e use ADD para conhecer melhor as características dos dados.\n",
    "\n",
    "Algumas sugestões de bases são: \n",
    "\n",
    "* [Portal Brasileiro de Dados Abertos](https://dados.gov.br/)\n",
    "* [Basedosdados.org](https://basedosdados.org/)\n",
    "* [Kaggle](https://www.kaggle.com/datasets)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "U93TbRNNNfvd"
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "colab": {
   "collapsed_sections": [],
   "name": "semana-3-tutorial-add.ipynb",
   "provenance": []
  },
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}