QuarentenaDados_aula01.ipynb

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      "name": "QuarentenaDados - aula01",
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  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
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      "source": [
        "<a href=\"https://colab.research.google.com/github/luiscarlosjunior/100-days-of-code/blob/master/QuarentenaDados_aula01.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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    {
      "cell_type": "markdown",
      "metadata": {
        "id": "sG6vp0eCNdi2",
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      },
      "source": [
        "#Aula 01"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "f7ibSyNTXtv9",
        "colab_type": "text"
      },
      "source": [
        "## Introdução\n",
        "\n",
        "Olá seja bem-vinda ou bem-vindo ao **notebook da aula01**, nesta aula vamos realizar nossa primeira análise de dados e no final já seremos capazes de tirar algumas conclusões.\n",
        "\n",
        "Nós estaremos desenvolvendo nosso projeto aqui no google colaboratory, assim podemos mesclar células contendo textos em formato markdown e células de código, além disso você não precisar instalar nada na sua máquina. Então que tal começar testando algumas linhas de código.\n",
        "\n",
        "Nesta primeira célula estamos realizando um ```print()```, lembre-se que esta função python imprime a string que estamos passando como parâmetro, então  o retorno é exibido logo abaixo da célula com código."
      ]
    },
    {
      "cell_type": "code",
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        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 50
        }
      },
      "source": [
        "print(\"Guilherme Silveira\")\n",
        "print(\"Paulo Silveira\")"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Guilherme Silveira\n",
            "Paulo Silveira\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "R2uQtgGLlaDs",
        "colab_type": "text"
      },
      "source": [
        "Agora vamos analisar a proxima célula de código. \n",
        "\n",
        "Aqui estamos fazendo uma atribuição de variável, conforme dito em aula, as atribuições não tem retorno, assim, diferente da célula anterior não temos um *output* logo abaixo do código."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "i9-JUfxERKcN",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "nome_do_filme = \"Totoro, o filme\""
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "gQi3djrZmwZ8",
        "colab_type": "text"
      },
      "source": [
        "Agora que criamos a variável ```nome_do_filme```, podemos reutilizá-la, por exemplo na função ```print```, para assim imprimir a *string* \"Totoro, o filme\"."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3aFMfB0ERUcF",
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        "outputId": "1f3a1207-767c-4bd5-fb2c-074a41ed4781",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "print(nome_do_filme)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Totoro, o filme\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Sg2NRrX9RXM-",
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      },
      "source": [
        "nome_do_filme"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'Totoro, o filme'"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 51
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "OuYA1aSIX0vJ",
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      "source": [
        "## Lendo os dados do MovieLens\n",
        "\n",
        "Nosso primeiro passo foi conhecer e realizar um \"hello-world\" no colab, agora chegou a hora de importar os dados para o notebook e começar as análises.\n",
        "\n",
        "Vamos importar a biblioteca [pandas](https://pandas.pydata.org/), um poderoso projeto open source para análise de manipulação de dados. O primeiro passo é ler uma base de dados e podemos fazer isso com o comando ```pd.read_csv()```.\n",
        "\n",
        "Estamos lendo um arquivo **CSV** (Comma-separated values), neste tipo de arquivo os valores são separados por vírgulas e podem ser abertos em outras ferramentas como excel e google-sheet. CSV não é o único formato lido pelo pandas, temos o ```pd.read_excel()``` que lê arquivos **xlsx** entre diversos outros formatos, você pode encontrar mais informações na seção de [input/output da documentação](https://pandas.pydata.org/pandas-docs/stable/reference/io.html).\n",
        "\n",
        "Depois de ler o dataset, nós trocamos os nomes das colunas pelos termos em português, logo em seguida utilizamos o método ```filmes.head()``` para visualizar as primeiras 5 linhas do nosso dataframe. Outra forma de visualizar as informações dos dados é utilizando o método ```filmes.sample()```, se você tentar, vai verificar que ele retorna uma linha aleatória do seus dados. Para escolher aleatoriamente mais de 1 linha, por exemplo 5, é só passar esse valor desejado como parâmetro (```filmes.sample(5)```). "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fm9UvpjdRZjP",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import pandas as pd"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "uIoEtYpDUSbk",
        "colab_type": "code",
        "outputId": "ee0198a3-cf8f-4c01-b27a-86a2a3dc3be8",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        }
      },
      "source": [
        "filmes = pd.read_csv(\"https://raw.githubusercontent.com/alura-cursos/introducao-a-data-science/master/aula0/ml-latest-small/movies.csv\")\n",
        "# filmes é um DataFrame\n",
        "filmes.columns = [\"filmeId\", \"titulo\", \"generos\"]\n",
        "filmes.head()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "\n",
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              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>filmeId</th>\n",
              "      <th>titulo</th>\n",
              "      <th>generos</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1</td>\n",
              "      <td>Toy Story (1995)</td>\n",
              "      <td>Adventure|Animation|Children|Comedy|Fantasy</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2</td>\n",
              "      <td>Jumanji (1995)</td>\n",
              "      <td>Adventure|Children|Fantasy</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>3</td>\n",
              "      <td>Grumpier Old Men (1995)</td>\n",
              "      <td>Comedy|Romance</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>4</td>\n",
              "      <td>Waiting to Exhale (1995)</td>\n",
              "      <td>Comedy|Drama|Romance</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>5</td>\n",
              "      <td>Father of the Bride Part II (1995)</td>\n",
              "      <td>Comedy</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   filmeId  ...                                      generos\n",
              "0        1  ...  Adventure|Animation|Children|Comedy|Fantasy\n",
              "1        2  ...                   Adventure|Children|Fantasy\n",
              "2        3  ...                               Comedy|Romance\n",
              "3        4  ...                         Comedy|Drama|Romance\n",
              "4        5  ...                                       Comedy\n",
              "\n",
              "[5 rows x 3 columns]"
            ]
          },
          "metadata": {
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          "execution_count": 5
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      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "bZ46r5yAYCDO",
        "colab_type": "text"
      },
      "source": [
        "Há pouco falamos para consultar a documentação para obter mais informações, mas será que é realmente preciso sair do notebook para tirar algumas dúvidas mais simples? \n",
        "\n",
        "Os notebooks facilitam a nossa vida podendo consultar o docstring das funções e métodos, rodando a célula com um **?** na frente da chamada, uma view é aberta com as informações resumidas. Veja a seguir alguns exemplos."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "sKew1Wy0UYbu",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# lendo a documentação de um método/atributo\n",
        "\n",
        "?filmes.head"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "i6F4_qpeUzOq",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# lendo a documentação do tipo (docstring)\n",
        "\n",
        "?filmes"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "wwcenqbGYGAO",
        "colab_type": "text"
      },
      "source": [
        "A base de dados que usamos até o momento contém o nome do filme, ano de lançamento e gêneros. O MovieLens conta com outras informações que estão em bases separadas, uma delas é a de avaliações. \n",
        "\n",
        "Agora vamos analisar um pouco melhor o dataset de avaliações."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7A1UmccVVLeK",
        "colab_type": "code",
        "outputId": "8ebf0492-4701-40d4-b2ea-6075e7936e00",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        }
      },
      "source": [
        "avaliacoes = pd.read_csv(\"/~https://github.com/alura-cursos/introducao-a-data-science/blob/master/aula0/ml-latest-small/ratings.csv?raw=true\")\n",
        "avaliacoes.head()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>userId</th>\n",
              "      <th>movieId</th>\n",
              "      <th>rating</th>\n",
              "      <th>timestamp</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>4.0</td>\n",
              "      <td>964982703</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>4.0</td>\n",
              "      <td>964981247</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1</td>\n",
              "      <td>6</td>\n",
              "      <td>4.0</td>\n",
              "      <td>964982224</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1</td>\n",
              "      <td>47</td>\n",
              "      <td>5.0</td>\n",
              "      <td>964983815</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>1</td>\n",
              "      <td>50</td>\n",
              "      <td>5.0</td>\n",
              "      <td>964982931</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   userId  movieId  rating  timestamp\n",
              "0       1        1     4.0  964982703\n",
              "1       1        3     4.0  964981247\n",
              "2       1        6     4.0  964982224\n",
              "3       1       47     5.0  964983815\n",
              "4       1       50     5.0  964982931"
            ]
          },
          "metadata": {
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          "execution_count": 11
        }
      ]
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    {
      "cell_type": "markdown",
      "metadata": {
        "id": "otNlLBcYE1gP",
        "colab_type": "text"
      },
      "source": [
        "Para visualizar algumas linhas estamos usando o ```.head()```, como ela mostra apenas as 5 primeiras linhas não sabemos qual é a quantidade de linhas que temos. Para descobrir a \"forma\" dos nossos dados podemos utilizar o ```avaliacoes.shape```, retornando uma [tupla](https://www.alura.com.br/artigos/conhecendo-as-tuplas-no-python), onde o primeiro termo indica o número de linhas e o segundo o número de colunas."
      ]
    },
    {
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        "outputId": "76a92920-a00a-4f3e-ad39-d064f977ac28",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "avaliacoes.shape"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(100836, 4)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 13
        }
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    {
      "cell_type": "code",
      "metadata": {
        "id": "Wo5ZCjYdV6To",
        "colab_type": "code",
        "outputId": "3615d411-9111-422f-ef63-0e651ce59ce8",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "len(avaliacoes)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "100836"
            ]
          },
          "metadata": {
            "tags": []
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          "execution_count": 58
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5E9e-dkuGUaK",
        "colab_type": "text"
      },
      "source": [
        "Vamos substituir os nomes das colunas de inglês para português e entender o que são essas colunas. \n",
        "\n",
        "usarioId => ID para para usuário que votou em determinado filme.\n",
        "\n",
        "filmeId => ID para identificar um filme votado.\n",
        "\n",
        "nota => A nota dada para pelo usuário para o respectivo filme.\n",
        "\n",
        "momento => A data da votação que não está formatada como data\n",
        "\n",
        "Como cada linha contém um voto para o respectivo filme é de se esperar que um filme tenha diversos votos, mas repare que nas 5 primeiras linhas temos o filme **1, 3, 6, 47, 50**. Mas e se eu quiser analisar apenas as notas do filme 1, como posso separar essa informação?\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QblDDv4SWBvC",
        "colab_type": "code",
        "outputId": "cad87aa0-4a76-493d-fc8b-a7f57a94b2b3",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        }
      },
      "source": [
        "avaliacoes.columns = [\"usuarioId\", \"filmeId\", \"nota\", \"momento\"]\n",
        "avaliacoes.head()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
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              "\n",
              "    .dataframe tbody tr th {\n",
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              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>usuarioId</th>\n",
              "      <th>filmeId</th>\n",
              "      <th>nota</th>\n",
              "      <th>momento</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>4.0</td>\n",
              "      <td>964982703</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>4.0</td>\n",
              "      <td>964981247</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1</td>\n",
              "      <td>6</td>\n",
              "      <td>4.0</td>\n",
              "      <td>964982224</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1</td>\n",
              "      <td>47</td>\n",
              "      <td>5.0</td>\n",
              "      <td>964983815</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>1</td>\n",
              "      <td>50</td>\n",
              "      <td>5.0</td>\n",
              "      <td>964982931</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   usuarioId  filmeId  nota    momento\n",
              "0          1        1   4.0  964982703\n",
              "1          1        3   4.0  964981247\n",
              "2          1        6   4.0  964982224\n",
              "3          1       47   5.0  964983815\n",
              "4          1       50   5.0  964982931"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "W7qCWhXVICxr",
        "colab_type": "text"
      },
      "source": [
        "Uma forma para \"separar\" as informações apenas do **filmeId 1** é chamando o método ```avaliacaoes.query(\"filmeId==1\")```, esse método retornará apenas as linhas para quais a expressão booleana, \"filmeId==1\", for verdadeira.\n",
        "\n",
        "Tendo as informações do **filmeId 1** podemos chamar o ```avaliacoes_do_filme_1.describe()```, para analisar as estatítiscas gerais dos dados."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "t26TcAKMWLx8",
        "colab_type": "code",
        "outputId": "14e20a5f-f865-48b9-f849-f02b9d6f7a62",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 195
        }
      },
      "source": [
        "avaliacoes_do_filme_1 = avaliacoes.query(\"filmeId==1\")\n",
        "avaliacoes_do_filme_1.head()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "\n",
              "    .dataframe tbody tr th {\n",
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              "\n",
              "    .dataframe thead th {\n",
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              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>usuarioId</th>\n",
              "      <th>filmeId</th>\n",
              "      <th>nota</th>\n",
              "      <th>momento</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>4.0</td>\n",
              "      <td>964982703</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>516</th>\n",
              "      <td>5</td>\n",
              "      <td>1</td>\n",
              "      <td>4.0</td>\n",
              "      <td>847434962</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>874</th>\n",
              "      <td>7</td>\n",
              "      <td>1</td>\n",
              "      <td>4.5</td>\n",
              "      <td>1106635946</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1434</th>\n",
              "      <td>15</td>\n",
              "      <td>1</td>\n",
              "      <td>2.5</td>\n",
              "      <td>1510577970</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1667</th>\n",
              "      <td>17</td>\n",
              "      <td>1</td>\n",
              "      <td>4.5</td>\n",
              "      <td>1305696483</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "      usuarioId  filmeId  nota     momento\n",
              "0             1        1   4.0   964982703\n",
              "516           5        1   4.0   847434962\n",
              "874           7        1   4.5  1106635946\n",
              "1434         15        1   2.5  1510577970\n",
              "1667         17        1   4.5  1305696483"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 60
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "se3soadiWuG_",
        "colab_type": "code",
        "outputId": "c203782c-51c6-48a3-c2f9-a1bb137a1302",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 284
        }
      },
      "source": [
        "avaliacoes_do_filme_1.describe()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>usuarioId</th>\n",
              "      <th>filmeId</th>\n",
              "      <th>nota</th>\n",
              "      <th>momento</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>215.000000</td>\n",
              "      <td>215.0</td>\n",
              "      <td>215.000000</td>\n",
              "      <td>2.150000e+02</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>306.530233</td>\n",
              "      <td>1.0</td>\n",
              "      <td>3.920930</td>\n",
              "      <td>1.129835e+09</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>180.419754</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.834859</td>\n",
              "      <td>2.393163e+08</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.500000</td>\n",
              "      <td>8.293223e+08</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>155.500000</td>\n",
              "      <td>1.0</td>\n",
              "      <td>3.500000</td>\n",
              "      <td>8.779224e+08</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>290.000000</td>\n",
              "      <td>1.0</td>\n",
              "      <td>4.000000</td>\n",
              "      <td>1.106855e+09</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>468.500000</td>\n",
              "      <td>1.0</td>\n",
              "      <td>4.500000</td>\n",
              "      <td>1.348523e+09</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>610.000000</td>\n",
              "      <td>1.0</td>\n",
              "      <td>5.000000</td>\n",
              "      <td>1.535710e+09</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "        usuarioId  filmeId        nota       momento\n",
              "count  215.000000    215.0  215.000000  2.150000e+02\n",
              "mean   306.530233      1.0    3.920930  1.129835e+09\n",
              "std    180.419754      0.0    0.834859  2.393163e+08\n",
              "min      1.000000      1.0    0.500000  8.293223e+08\n",
              "25%    155.500000      1.0    3.500000  8.779224e+08\n",
              "50%    290.000000      1.0    4.000000  1.106855e+09\n",
              "75%    468.500000      1.0    4.500000  1.348523e+09\n",
              "max    610.000000      1.0    5.000000  1.535710e+09"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 63
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "zUD1xulBKcou",
        "colab_type": "text"
      },
      "source": [
        "Caso queira uma estatística particular, podemos apenas chamar o método desajado, repare abaixo como calculamos apenas a média das avaliações do **filmeId 1**."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lf4dH46SW2ro",
        "colab_type": "code",
        "outputId": "7ea1ae4b-4a29-4351-a8f4-51692b2de429",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 101
        }
      },
      "source": [
        "avaliacoes_do_filme_1.mean()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "usuarioId    3.065302e+02\n",
              "filmeId      1.000000e+00\n",
              "nota         3.920930e+00\n",
              "momento      1.129835e+09\n",
              "dtype: float64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 64
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "dylF75HzK_nI",
        "colab_type": "text"
      },
      "source": [
        "Calculamos as estatísicas apenas para o **filmeId 1**, mas também podemos chamar o método ```.describe()``` para a base completa (avaliacões)."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jZ9ZtovSWS0C",
        "colab_type": "code",
        "outputId": "f44aa241-22d4-4b67-cd57-4ac3aac47cac",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 284
        }
      },
      "source": [
        "avaliacoes.describe()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>usuarioId</th>\n",
              "      <th>filmeId</th>\n",
              "      <th>nota</th>\n",
              "      <th>momento</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>100836.000000</td>\n",
              "      <td>100836.000000</td>\n",
              "      <td>100836.000000</td>\n",
              "      <td>1.008360e+05</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>326.127564</td>\n",
              "      <td>19435.295718</td>\n",
              "      <td>3.501557</td>\n",
              "      <td>1.205946e+09</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>182.618491</td>\n",
              "      <td>35530.987199</td>\n",
              "      <td>1.042529</td>\n",
              "      <td>2.162610e+08</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.500000</td>\n",
              "      <td>8.281246e+08</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>177.000000</td>\n",
              "      <td>1199.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>1.019124e+09</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>325.000000</td>\n",
              "      <td>2991.000000</td>\n",
              "      <td>3.500000</td>\n",
              "      <td>1.186087e+09</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>477.000000</td>\n",
              "      <td>8122.000000</td>\n",
              "      <td>4.000000</td>\n",
              "      <td>1.435994e+09</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>610.000000</td>\n",
              "      <td>193609.000000</td>\n",
              "      <td>5.000000</td>\n",
              "      <td>1.537799e+09</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "           usuarioId        filmeId           nota       momento\n",
              "count  100836.000000  100836.000000  100836.000000  1.008360e+05\n",
              "mean      326.127564   19435.295718       3.501557  1.205946e+09\n",
              "std       182.618491   35530.987199       1.042529  2.162610e+08\n",
              "min         1.000000       1.000000       0.500000  8.281246e+08\n",
              "25%       177.000000    1199.000000       3.000000  1.019124e+09\n",
              "50%       325.000000    2991.000000       3.500000  1.186087e+09\n",
              "75%       477.000000    8122.000000       4.000000  1.435994e+09\n",
              "max       610.000000  193609.000000       5.000000  1.537799e+09"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 61
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4Kq3eGVXLmca",
        "colab_type": "text"
      },
      "source": [
        "Ok, nós calculamos um tanto de coisa usando `.describe()` e `.mean()`, mas a informação que realmente queremos é a média da nota. Então o ponto é, como calcular a média apenas das notas?\n",
        "\n",
        "A primeira coisa que precisamos fazer é selecionar apenas as informações de notas. Usando uma estrutura muito parecida com a de [chave-valor dos dicionários python](https://www.alura.com.br/artigos/trabalhando-com-o-dicionario-no-python). \n",
        "\n",
        "Com o comando ```avaliacoes[\"nota\"]```, obtemos os valores da coluna nota (repare que o tipo retornado é uma Série pandas, por isso o index de cada nota é mantido). Para calcular a média de todas as notas executamos ```avaliacoes[\"notas\"].means()```\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "OnltfnYqWi40",
        "colab_type": "code",
        "outputId": "0ca1a5ff-434c-45a0-a8bb-b5e808da09f7",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 225
        }
      },
      "source": [
        "avaliacoes[\"nota\"]"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0         4.0\n",
              "1         4.0\n",
              "2         4.0\n",
              "3         5.0\n",
              "4         5.0\n",
              "         ... \n",
              "100831    4.0\n",
              "100832    5.0\n",
              "100833    5.0\n",
              "100834    5.0\n",
              "100835    3.0\n",
              "Name: nota, Length: 100836, dtype: float64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Xos6cYlmPWO6",
        "colab_type": "code",
        "outputId": "a5ed72a3-24e1-4e6f-ed93-ec135a41a028",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "avaliacoes[\"nota\"].mean()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "3.501556983616962"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "u03bvyYZPvza",
        "colab_type": "text"
      },
      "source": [
        "Podemos calcular também na nota média do **filmeId 1**, repare que o resultado é um pouco maior que a geral. Apenas com essa análise não da para bater o martelo que o filme 1 é acima da média, mas apenas com essa análise conseguimos formular uma primeira hipótese!"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "h8JDwB_2W8yR",
        "colab_type": "code",
        "outputId": "24b7c5d7-b38e-4c1c-8f0a-b37fc0f21acb",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "avaliacoes_do_filme_1[\"nota\"].mean()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "3.9209302325581397"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 65
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Wv6npVXeRuVl",
        "colab_type": "text"
      },
      "source": [
        "Nós calculamos uma média geral, uma média para o filmeId 1. Agora eu quero calcular a média das notas para todos os filmes, podemos fazer isso usando o método ```.groupby(filmeId)```, o parâmetro passado é para indicar qual coluna ele deve utilizar para \"agrupar\" os dados. Depois só calcular a média como fizemos anteriormente.\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "78qsVDbWW-kf",
        "colab_type": "code",
        "outputId": "21bb8fcb-1654-4b28-f605-e03ec8dfc4a7",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 134
        }
      },
      "source": [
        "notas_medias_por_filme = avaliacoes.groupby(\"filmeId\")[\"nota\"].mean()\n",
        "notas_medias_por_filme.head()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "filmeId\n",
              "1    3.920930\n",
              "2    3.431818\n",
              "3    3.259615\n",
              "4    2.357143\n",
              "5    3.071429\n",
              "Name: nota, dtype: float64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 66
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "gRyxEdyOWs4I",
        "colab_type": "text"
      },
      "source": [
        "Temos as notas médias calculadas, mas agora precisamos juntar as informações de notas médias com a base de dados **filmes**.\n",
        "\n",
        "Poderíamos criar uma nova coluna e atribuir a váriável `notas_medias_por_filme`, de forma direta:\n",
        "\n",
        "```filmes[\"nota_media\"] = notas_medias_por_filme```\n",
        "\n",
        "Como discutimos em aula, essa não é uma boa prática pois precisamos garantir que a nota média seja do respectivo filme.\n",
        "\n",
        "Para garantir essa condição vamos utilizar o ```.join()```, criando um novo dataframe (```filmes_com_media = filmes.join(notas_medias_por_filme, on=\"filmeId\")```). \n",
        "\n",
        "Veja como fazer, nas células a seguir."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jPTL419OYjto",
        "colab_type": "code",
        "outputId": "89c6a520-c453-4d75-8eb9-0ab16f83012f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 402
        }
      },
      "source": [
        "filmes"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>filmeId</th>\n",
              "      <th>titulo</th>\n",
              "      <th>generos</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1</td>\n",
              "      <td>Toy Story (1995)</td>\n",
              "      <td>Adventure|Animation|Children|Comedy|Fantasy</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2</td>\n",
              "      <td>Jumanji (1995)</td>\n",
              "      <td>Adventure|Children|Fantasy</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>3</td>\n",
              "      <td>Grumpier Old Men (1995)</td>\n",
              "      <td>Comedy|Romance</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>4</td>\n",
              "      <td>Waiting to Exhale (1995)</td>\n",
              "      <td>Comedy|Drama|Romance</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>5</td>\n",
              "      <td>Father of the Bride Part II (1995)</td>\n",
              "      <td>Comedy</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9737</th>\n",
              "      <td>193581</td>\n",
              "      <td>Black Butler: Book of the Atlantic (2017)</td>\n",
              "      <td>Action|Animation|Comedy|Fantasy</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9738</th>\n",
              "      <td>193583</td>\n",
              "      <td>No Game No Life: Zero (2017)</td>\n",
              "      <td>Animation|Comedy|Fantasy</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9739</th>\n",
              "      <td>193585</td>\n",
              "      <td>Flint (2017)</td>\n",
              "      <td>Drama</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9740</th>\n",
              "      <td>193587</td>\n",
              "      <td>Bungo Stray Dogs: Dead Apple (2018)</td>\n",
              "      <td>Action|Animation</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9741</th>\n",
              "      <td>193609</td>\n",
              "      <td>Andrew Dice Clay: Dice Rules (1991)</td>\n",
              "      <td>Comedy</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>9742 rows × 3 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "      filmeId  ...                                      generos\n",
              "0           1  ...  Adventure|Animation|Children|Comedy|Fantasy\n",
              "1           2  ...                   Adventure|Children|Fantasy\n",
              "2           3  ...                               Comedy|Romance\n",
              "3           4  ...                         Comedy|Drama|Romance\n",
              "4           5  ...                                       Comedy\n",
              "...       ...  ...                                          ...\n",
              "9737   193581  ...              Action|Animation|Comedy|Fantasy\n",
              "9738   193583  ...                     Animation|Comedy|Fantasy\n",
              "9739   193585  ...                                        Drama\n",
              "9740   193587  ...                             Action|Animation\n",
              "9741   193609  ...                                       Comedy\n",
              "\n",
              "[9742 rows x 3 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 67
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "AVlwJvZRZCQP",
        "colab_type": "code",
        "outputId": "662d813d-21b4-4934-d4be-2c8e999c9dc3",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 235
        }
      },
      "source": [
        "notas_medias_por_filme"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "filmeId\n",
              "1         3.920930\n",
              "2         3.431818\n",
              "3         3.259615\n",
              "4         2.357143\n",
              "5         3.071429\n",
              "            ...   \n",
              "193581    4.000000\n",
              "193583    3.500000\n",
              "193585    3.500000\n",
              "193587    3.500000\n",
              "193609    4.000000\n",
              "Name: nota, Length: 9724, dtype: float64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 68
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hoUllrZFZgsA",
        "colab_type": "code",
        "outputId": "d1c778bf-0205-4936-db7e-53f1fd8fd862",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 195
        }
      },
      "source": [
        "filmes_com_media = filmes.join(notas_medias_por_filme, on=\"filmeId\")\n",
        "filmes_com_media.head()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>filmeId</th>\n",
              "      <th>titulo</th>\n",
              "      <th>generos</th>\n",
              "      <th>nota</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1</td>\n",
              "      <td>Toy Story (1995)</td>\n",
              "      <td>Adventure|Animation|Children|Comedy|Fantasy</td>\n",
              "      <td>3.920930</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2</td>\n",
              "      <td>Jumanji (1995)</td>\n",
              "      <td>Adventure|Children|Fantasy</td>\n",
              "      <td>3.431818</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>3</td>\n",
              "      <td>Grumpier Old Men (1995)</td>\n",
              "      <td>Comedy|Romance</td>\n",
              "      <td>3.259615</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>4</td>\n",
              "      <td>Waiting to Exhale (1995)</td>\n",
              "      <td>Comedy|Drama|Romance</td>\n",
              "      <td>2.357143</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>5</td>\n",
              "      <td>Father of the Bride Part II (1995)</td>\n",
              "      <td>Comedy</td>\n",
              "      <td>3.071429</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   filmeId  ...      nota\n",
              "0        1  ...  3.920930\n",
              "1        2  ...  3.431818\n",
              "2        3  ...  3.259615\n",
              "3        4  ...  2.357143\n",
              "4        5  ...  3.071429\n",
              "\n",
              "[5 rows x 4 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 73
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "IDLHWb9cYkuy",
        "colab_type": "text"
      },
      "source": [
        "Agora que temos as médias, que tal visualizar o nosso dataframe ordenado pela nota de forma decrescente?"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MLNCjcgdaOb5",
        "colab_type": "code",
        "outputId": "920b6c0b-ccd2-4596-d8fa-fef8a1bf4d02",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 491
        }
      },
      "source": [
        "filmes_com_media.sort_values(\"nota\", ascending=False).head(15)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>filmeId</th>\n",
              "      <th>titulo</th>\n",
              "      <th>generos</th>\n",
              "      <th>nota</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>7656</th>\n",
              "      <td>88448</td>\n",
              "      <td>Paper Birds (Pájaros de papel) (2010)</td>\n",
              "      <td>Comedy|Drama</td>\n",
              "      <td>5.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8107</th>\n",
              "      <td>100556</td>\n",
              "      <td>Act of Killing, The (2012)</td>\n",
              "      <td>Documentary</td>\n",
              "      <td>5.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9083</th>\n",
              "      <td>143031</td>\n",
              "      <td>Jump In! (2007)</td>\n",
              "      <td>Comedy|Drama|Romance</td>\n",
              "      <td>5.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9094</th>\n",
              "      <td>143511</td>\n",
              "      <td>Human (2015)</td>\n",
              "      <td>Documentary</td>\n",
              "      <td>5.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9096</th>\n",
              "      <td>143559</td>\n",
              "      <td>L.A. Slasher (2015)</td>\n",
              "      <td>Comedy|Crime|Fantasy</td>\n",
              "      <td>5.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4251</th>\n",
              "      <td>6201</td>\n",
              "      <td>Lady Jane (1986)</td>\n",
              "      <td>Drama|Romance</td>\n",
              "      <td>5.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8154</th>\n",
              "      <td>102217</td>\n",
              "      <td>Bill Hicks: Revelations (1993)</td>\n",
              "      <td>Comedy</td>\n",
              "      <td>5.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8148</th>\n",
              "      <td>102084</td>\n",
              "      <td>Justice League: Doom (2012)</td>\n",
              "      <td>Action|Animation|Fantasy</td>\n",
              "      <td>5.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4246</th>\n",
              "      <td>6192</td>\n",
              "      <td>Open Hearts (Elsker dig for evigt) (2002)</td>\n",
              "      <td>Romance</td>\n",
              "      <td>5.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9122</th>\n",
              "      <td>145994</td>\n",
              "      <td>Formula of Love (1984)</td>\n",
              "      <td>Comedy</td>\n",
              "      <td>5.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8115</th>\n",
              "      <td>100906</td>\n",
              "      <td>Maniac Cop 2 (1990)</td>\n",
              "      <td>Action|Horror|Thriller</td>\n",
              "      <td>5.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9129</th>\n",
              "      <td>146662</td>\n",
              "      <td>Dragons: Gift of the Night Fury (2011)</td>\n",
              "      <td>Adventure|Animation|Comedy</td>\n",
              "      <td>5.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8074</th>\n",
              "      <td>99636</td>\n",
              "      <td>English Vinglish (2012)</td>\n",
              "      <td>Comedy|Drama</td>\n",
              "      <td>5.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5785</th>\n",
              "      <td>31522</td>\n",
              "      <td>Marriage of Maria Braun, The (Ehe der Maria Br...</td>\n",
              "      <td>Drama</td>\n",
              "      <td>5.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9131</th>\n",
              "      <td>146684</td>\n",
              "      <td>Cosmic Scrat-tastrophe (2015)</td>\n",
              "      <td>Animation|Children|Comedy</td>\n",
              "      <td>5.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "      filmeId  ... nota\n",
              "7656    88448  ...  5.0\n",
              "8107   100556  ...  5.0\n",
              "9083   143031  ...  5.0\n",
              "9094   143511  ...  5.0\n",
              "9096   143559  ...  5.0\n",
              "4251     6201  ...  5.0\n",
              "8154   102217  ...  5.0\n",
              "8148   102084  ...  5.0\n",
              "4246     6192  ...  5.0\n",
              "9122   145994  ...  5.0\n",
              "8115   100906  ...  5.0\n",
              "9129   146662  ...  5.0\n",
              "8074    99636  ...  5.0\n",
              "5785    31522  ...  5.0\n",
              "9131   146684  ...  5.0\n",
              "\n",
              "[15 rows x 4 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 76
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "D4Yw8FhlhDT1",
        "colab_type": "text"
      },
      "source": [
        "Fizemos um tanto de análise e manipulação de dados interessante, não é?\n",
        "\n",
        "Mas diz a verdade, você está sentindo falta daquele gráfico que todo cientista de dados adora =D, então bora plotar nosso primeiro gráfico!\n",
        "\n",
        "O pandas facilita muito o plot de alguns gráficos simples, apenas selecionamos a informação que gostaríamos de visualizar e chamamos o método ```.plot()```"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "usWDMMoUiG-l",
        "colab_type": "code",
        "outputId": "b91298d1-3d4d-423d-968a-4bd992ee52c6",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 282
        }
      },
      "source": [
        "avaliacoes.query(\"filmeId == 1\")[\"nota\"].plot()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7ff083a81160>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 19
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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kk2Y1XTZJ07y7no9nlkPm3Ulg3sNCEdlEOCwn03gnIYw2yXBYJmje9Yo99jIA46xtssGMN924usNSvOpx3qKqYJpsEo2wSEOece8XSVvVmXejYmOm5valeRP0aBNVEtCNt6ygp42ukOatSC7EQndvEsa73Udxpo7nY7buwmJ9RJt4cebdlsw7qnlbFoNrM/hchAk2KnYkEifisGwnj4MM93TNCTIs40k645VNJjfOO2kFUihUMCHaRGjek3mcw8CGMN5hiU/xKpJ01FDBqNGhCA7XYqkZWCHzzt73qIx3qHnHj4NQd23M1B2cK5EeryOepBPuQ5dNiN3op6xYtIl4JebNWBjq2C/zrjoWZutueYdlhuZdi4QKir+Jfbs2Q9WxNeYdbjdNNqGVxtbpqojzDvbrcS4LgA3rLioim1TWmWyy0sl/FqNx3qHmPd5ok/FiQxhvepjUJWMx5p2eYalXJEzDqJ6BxCQdbazNqoPZ+oDMO8NhSQyTWKle1hUALBYu0bOct6G/kuPUYhtbmhUZU93PEr7d81FxLMw1KgNo3orx9uLGmyYlSkJybRF9kuqwzDHe26ar4AhDBVsdT0leGs6jX1Q2WU/1vCXzzjCPEdmENO/K+B2Ww2plVwQbwnjHa5vo6fFRoxPGeRdwWK6R5q2HNyaNRcomA2jeWQ7Lts68ZahgOA7HtkQVRwBe1kymHM+pxTbmp6oyAaafRJ1OYLwF8y6pecsMywSHZQbzrtgWaq6dGuedaryXyHjXItEmK0OuawIUS9JxHTaxzDsJMj0+5VHjQWo8IAqqhczbGnuo4DixoYx3mGHJIjOgXldDrSqYJpvQcjaPeY9a81bZhjpRMCaY4UzdHSjaJK0kLBDKJmGooHhfPSVuUNxf/TwJqq/z1FIbW6erMgyvX+ZddWzMNdwBMizjskk9kXmTbCKYt+dzafgjDssUzVuVTTjC+3RFaTU3rLuIjHJeevykxnnrj5OndXVKQqRJSdeT16bm2qYk7LjR7nn4q68+Ds/n+NJDp/DA8XOZ39eZlGNFDyuM845GcDhW+sy82onqvGkYWbSJst+e5+Mvv/J45CZuVhwwxjBTc7DaLV/fg5CWYQmEsklHk01UUxNh3sGJPr3Uxns/9xCeXgr7f8pJiHOcXiTjLa7TJ+85ht/7zINYWO3ir776eMSopY7bC5l3WeMtHdxqqGDguI3IJsHfNE7XYdKQt3rx+0Nl3v/80Cncf0zct6eX2piqOqi7tqjnrdRlIQzy4Le64fNSSDYZcoblA8fP4b//0/34f7/4yNBDZ9VeqvRM3Ht0Af/y8Cnl/fD7rWeRw7JoA+Kx4ssPn8avfvxeXLVnDj/6Z98AkN3VOmyDFjDv1MJU0d+5dnqywton6YhXzoE7njiDX/uHe/HvXxh2+6ZMwBkqC9vqYn6qWno/Mc1b0UL14vZJ/Rpdm8EJjDexnA9+/Um893MPY6rq4CdffEAcjx8e16nAeJNs8gdfeAQAYFkM7/3cw6i7Nn7oYHZH+E7PQ9W2MDdkh2Uk2iQYH41TMG9h0NtdD1NVR9ZYdywW6bbzqx+/B1ftmcMfvOUaebyMQWPeqmzS/31Ez8tlO2fQ9XxYDHJCTUJWclo/+J9fegwf/dZhAMArL9uOi7dP970tXdemWt5AeN/94RcewQPHF3H7L70MQJTorHY8uXoaN/MeNyaSeROD0bt7p0FPd9YLU+n2tUxtk7xLP+qqgpyHGYRqTDCtLmZq5Wt6E5KiDpKSdNpa2KR6yhwrZN7Euo4trMbGRD9ZWO2i4/nYqmjeBDJmj55ezh271LwbFZxrdUutgJLS44mNqxmWUjZxQu27qun0xN43NSuRqJ+VjicTe04ttrF1qiqbYkvNOyMjswzoOh1baKFToKsSAxvqfXtC6fyeVSKgCPRhqRMiGfZnljuRVU5MNvF92EGI51hLwpraJuEFK/pAxjRvOzvDMtS8hX6p38hdz090ziVBtX3DjJ1VnazkkFRZMjmkqLt5P+GCM3U3RzaJMu+wMqAqmzDYLMq8Hz+9AiB01KnHc3JRPOhbp6uxcDYay2OnChjvQDaZq7vgvJzRoGJYkWiTQqGCYcZlW9PNNzcqWFLG0O56Mmnn9FIb89MVyRrIoAxLNqGxnFhooefx3EqYww6IOHGuhe0zYtW31B6uEzbiFwjO0cJqN9LZKNIesCuSdByLBXkc4442Gd++JtJ40wUr2vmZHGqyEL2WYZlWVZC6jejsW2W4ede+SIZdP1BHRCxDNazErgZh3rN1J7MsaivmsEySTUQiC2Mh83709BIApYa18puT50Lnnc68yQg9VpR52xbmGuL4y0gnibIJ6aSVLIclkxmYMjw1uEHmGm6EDbZ6viQhKvNW97U6JIcljeX4uRa6np8ZaTKM/ek4ca6FA/NTAAZ/BvRxLSuTAT1qZ1e6WO16IcFSbuFWR8R5V2wryOMwsslYQbN3UWeD/jDaORmWBCcwgPrSqtVRb5ji0SZleknmwVeYPxlmtXwqSRWq5l0WswnMu+v5aFRsWCzusNTbygGQerfNxIOy1O7hRGCgTy7GHZYnlZjnasx4i+N77Onl3FUXySbSeJeYvKRsomZYBsdaU8aUybw1P8vmZkUaa3JKLrZ6aHU9nGv1MD9Vlffk8B2WYntkvPNlk+FFRqx2xPEd2NoEEO312Q/0lW50e+Kzs0EJYCJ5KvOmhtyOzWBbFjgfY1lYE20SXrCidQl0/VEPFYzFeQevZHj0QP4I884ZQrSq3GB6nwp1t4myCRlvybzLPzSzdTcxVLDiiHjmWG2ThCeeJkDbYvA4l5LHVNVJZN4ybG6qlsq8Oz0fR8+uZo49jPOuAChX3ySMnlFK4xZI0qk4ocOSzg1N/HONiiiK5PnyOBZbXTwdpMZvna5KuU42eR5SnDdNeicWWuh6PNd4D3NtTzLYBVsF886qrNgPljXZpNX15GRFrDxJ83ZsS65AxlkK4Fkvm5DxLqpX6XG7rp0sm4RZi2GcNxBn3qrxztW8R8W8le2SYU6UTaTmXW7isC2GRtVJdFi6toW6aydUFUyONqHteR6XkskLDmzG6aW2XEHQb08ttlGxLczUnbjxVkIh85yWUvMOmHeZcEF1EqQHOywJa4ECNWp6qGCGw3JzU4xjue3J41hq93DyXKjxx5m3apj6p23tGPMenwWhVdb5w2Le2v9XtNWJep3p/KnPCqXHu4HDEhhvQ4ZxYjKNdyfKavKgs0crWs47HucdvB/KJtHfpxUeSoIa1zpM461Gm2Q5LOuuDddmpTXvumujmlBdrutxhXmLz8LCVOI7ibJJoC9+99QyGAMO7t+Mrsflw0a/6Hh+YMgYqnbIcukzwmOnljLH3+4Gmne9vOat3i+kiXZ6IsTOsS1YgZWNhwqyMFRQIwybGmIFsNjuhgleHHjqjFhBzCuatwwVVMPgCo8+/XhOnmuj0ysmmwDDiZSiSJPdc3XUXTsSHdIP9CHRZNCs2PA5j1xn+iwSKhgk6biOBdtKlkU3CibTeEvZpCDzjjBSIZkkFqaSzDv8LpDgsOwUl03Uz9My7PpBKPWomndcNhGJOuVT5Kk/Y1K0CWm7ZISyOunQBOhYDD7nePTUEvZuamBPUDHwpNJ4lzA/LSIT4sw7PO95zLvthYWpgJIOS7VBLTHvgMkDIt4cSGbeNRltEvXLSOPd6kVWECQjqcyboEsC/YLG0vF8nFxs5xvvIRJzMt7bp2toVp3BCUzMeItja1ZFRUZVHtNlE9tiWA0clo7CvMdV32TcU8REGu8lKZsUZN4J8c/RNmjBa/D/UDZJcVjK/pXl6nkPlXnTK1fi3iPGO7x0M3UXCyU175prJ2baiSgOhrprS8etrnmrZ0SVTXo+x6OnlnFgaxNbg4Qh0rjV87gtzXgH+5mpOZkRJ+QQrDoWHNvCVNWRTqwiiMomIfMmxyTJJjrzrkSSdKLhqZsC2WSp3ZOTHgA8FshIW6YqsaJFw6ptot4Xh8+sFJZNhuG0PLnYDso0OJiuOQPLJjpWOj1UHQuuLZyPqmNaMu/g8JsVW5aEdSOZv+Mzq8/67vFS8+6DeUtGqnzuqxqEAidlZiatt1lxcm/wkUWbqJo3ySZeXDYBEHTTKS+bJBXl73qKw7KXHCoYlU1Ch2XP8/HY6WUcmJ8S5U8BnFoSzEw9jfSZrdRFEfsRY7l4+zQezYj1JoNLRnW27mKhZKgg2VHSrEWVQmGYKW49TI9Xqgpqcd50/uYk8+5GmffTK5itu5HkH8JyQgJKP1D3d+JcAeY9RANz8lwL22dqYIyhWR2CbKKdh6V2D1NVR36mXudlLdpkquqg1fXQ8zkcm8EN7s1JrV0+KCbSeBMjKRJtotaKAEKjlpger72fx7zrgc6WhZExb2W1kCSbqA9oP9106hU7UTYh1lJzLSkf6bKJ+nxJ5s0YjpxdxWrXw4GtTWwLOtsT81ZP41YljV8NF6RJ4uId0zi6sJraWZ0mMTLecw23dKhgIzDMPYV501hI86awwUhtk+BvGW0SZPORfLPY0pj3qSU5WelyRcRXM6BsohbUyo02GXyXEifOtWWCzlTVGbrmvdLx0KjaorQAR2SFpcsmjaqDricaXLhKtMm4NO9RZVunYSKN91KJaJOez6Pp2sGNG9W8xSvnUUaeFkoUMu984x1l3sPTvGW/SJ/L8KskzRtAX910SDbp+TzidO0EoWb1wGHJOVdkE/GdSIYlMW+b4eETQiI4sLWJZsVG3bVlUo56nsiYAVHphI7v4m1T4Bx4/Olk9k3fI5ljrlGuLGyn56MRsLluiuZtBxl6QGi8HcuKOSwpm2862J6ueYsYb8HKsxjvoA7LPZvqkbIPWaDvDcVhudiSE/VU1RlqohogVifNigOLiZR+1behNyZu0jVod+EqZRvGmahjQgVLxHnrzDF05IXvqZ10VN0xz2HZqDil4rwHZR1RiO0utnuSjaTKJgW76ahGuh4Yb327FK1QDWQT9Rok1jZRmDc5Jy/YOgXGGLZOV8PGuyrzVo23rTLvwHjvEIWN0qQTabwDQzpXr5QOFWwGmZR07dtdL6J5q8k6FUX7DkMFw9Wha1uYroWat9ppRxyvMG6jerDbPR/NqoMtzcCXUDDaZBg4ea6N7dOh8V4uUBEyC/rjttzpoVl1ZFGvs6tdbGqE5xpQZRNxTRdbPSGbUDTZBu0gP3HG2/e5lE2KLHdixptkE3WbSrRJhHlb1DhXDxUU+5+qOgVqm4jPZ+vukDMsxavKqKPMW5FNCjLvnm687bjx7gZRHOSwVA1Rcpx3qHkDYrVCDsmt01VFNsln3uR4vmibMN5pTsvQeAead4ma3r7P0fM5GpUM5s2Y1LsBNdqEwbIYKrYVifN2bIaaK5jeYqsbqz9NMlGW0Ry0JGzVsbBjthqMs3/ZpAwbX2r3sNTuSdmkOQTmHc+w9NCo2DIrdGGli81BFyYiefQMNivh6sexrdQ8jlHhWR9tsqLonEVmTD1agpwUKs0J47w1zVvKJrrmLWJ+K46Vy7zpXputu0OubRIwwl6UFROizFtkSqZpxAR1lVCv2NIo6UkrbmCMWj0/8llSA2I1zhsADgSsGxBRJWG0STgOVfOOGG+KNqk72DFTw3dTYr2p9rbUvIOysEUMD90vzWqG5m2xCPNWHZbi/1YkPd6xLDDGMF0Txktn3vPTgWySYL2lhDGIw7Lno+rakgEXl03in5WZRCgBaTvJJrXBNW8dJJswJkJRz652MNeooFm1ZT4IPd9TUrrqomKzUPMea7TJ+DBxxlv1wBeRTfTO40l1jJNaigHhw5iUYVl3bVhB7HIWaMk224fTMAtJ95t6E7oR5h0yjiyok2FNlU1U4x3IJjVHpMeTsbMtFurwCT4GSoigGheAYNgkpajGKU82cS0L5883U5l3O0Hz7vk8UiskDfRbYt50TijdHhCyicq81XregChUpcZ5k/w2FcQ5pzPv+L1JjtOB4rwD5r09aOicX1VwONo7ZVduI4dlxUG75w8U3aHvf6XjRWWTlS7m6i6aVSeWpNMIJmSaUMOABCObjAVLEeNdhHlHH1gyJqoUohob9QEiQ58UKlgPijMV1bz1qnKDIu9htjXmDT4dgiYAACAASURBVOSnyHuabELGSDXenSA7rR7EzNLkWHftxJBLMlzEwKm6HCCM1sJqF+1e2Gh3uuYkdqshWEww3wNbm3j01HIim1ZT2QGheQPF6pvQb5vKg07HTUbaZiwSBSPjvSXzDrNPKSwNCIy3onnTuZlPiTYBolUM+wWtGnYEDLi4bBI/t2Xa+lFdE5V5A4OlyMcyLDs9NKtCNgEXxnu24aJRccJoE81hCYjVh6xdNLZok7HsRmLijLd64Yu09tJT490EKUR1WKrkx00LFex4qLm29HBnwVeY9zCXjHn7da248c7TfSOad8VKdVhSo13Ow8m05tqKbBKC2I0lZZOQeRMjO73UkZOgyroBoKoZGpp8z59vYmG1izMJ8dttTfOeKZFlSccqmXdCkg7TNG81PR4IZBOlDLGrNMZQNW9yIG7N6HBEE9mg0SY115bGu0hJWCDZ2JQx3ic02aRZLbYCLIPlNjksmYjzXu1irl7BVNVWknTEmKcV4y1CBYnIjTPaZAKTdBhjNmPs24yx20Y5ILV+bz/Gm9i0+lt57VJCBWO1TXpkvItnWJLxHlasZ95WHFuVTYrV9NaZdyWBeVNtE2KbNCHUK5Y8F2rUSox5a7IJQNqo+I1uyPQsS5qUqEodZSiq0B2WZYpTSeYto03isomdpnkrUSeRUEFi3oHmS4Z9SxAiuE0y7/iDXZeyySCad1Q2GSQ9vswwTpxro1mxpdZMxnMQEqOuBjo90Qm+GTgsO0GN9LmAecuSsOSwjBhvlpqEt1FQhnm/A8D9oxoIIcK8i8gmOvNOyKpSS8KqN25WqGDdtYWTJGcIqmzi+TxSkXAQ6JOGrmOq7Gq2YDcd9ThVzbutyyY2k8t5MogN1wlrmySMg7ISz59XjPdUmKjDU5i3zhJV5g0A300IF0yK8wYKMm/SvKtR5t0uoXnXXDsabRLcc9O1UPOu2BZmai4YE7W+gWRnVqMyBObd9VF1QuatT4hlUM54hzHeQGg8B0qRV/ZPxrkRxHnT9Z1ruJGEILqtKdoEwLMi2qRQA2LG2B4ArwXwGwB+YVSD+W+33YeHTizK/6uG+W+/+RRuuGAL9m5uRH6TFirYVbLwwhA3HtG86aH70DefwtGzq/ipl14AQHFYBsz7o4cO4/rzN2Pv5gY+9u3DuGbvJhw5uwrXtiKyCQD82sfvlYbPthh+8sUHsHuuLvf54PFFfPDrT2C65uIdr7wIrm3h9gdO4vYHT+KK3bN408G9uOfIAj5z7wn5m2ZFTCRJZQCAkHn/9defxB2PPxM7r1fsmsWbrtsrm+UCYYYlADxychGPn17G91+zG57PpcMSCI13zbUSO+mo6fG7ZmtSjgBCQ/0/PvOgPE8x421pzDu4fns21WFbDO/52D24Zu8cLlKa2tJ5oFR1qXkXqG+iM+8PfP0J9HwfR86uRqJNsjVvS4Y1djwuGTklqRATnqo52NyohIljGZr3f/vEfbj9gZO5429WHbzjxouk3PLJu49hsd1D1Q01byfBaa9CfQZOnGvh1i89Kv//67fdl1gbZbbu4h03XhRZ8Z0815arCiDUvO89eg633XUsddV63f7NeP3Vu/AvD5/CNx97Bu945cWx73zlu6fx0UNHxHYDh+WZwKcxW3fRqNiy8cLvfPoBeW4IrsXkvfX+rz0R6TZfdSy87aUXYEuBht2fuuc4vvHYM7jlJQewY7aGT959DAe2TuGSIBfhiw+exGzdxTX7NgEYb7RJ0e7x7wXwTgCpbaEZY7cAuAUA9u3b19dgPnn3Max2PVy0bSpwdIXG6p0fvQv/4cXn41dee3nkN2myCbFM12KRrufqA7RtpooLt03h0BNn8OVHTuMtz9+HmZqL1a6P2boLi4nf/uKH78T8VBXf/JUb8Usfvgtve+kBfPW7T6NesfHii7YCAK7aM4cdMzV87n5hdMkzvmOmJicFAPibbz6Jv/zqEwCAVz9nO67aM4c/vP0RHHriDOYaLt50cC/+5F8ejTD4esXB1Xtm8a0nz0gNWDV6m5sVXLl7Fg8cP4cHjp+LnI/ljoe6exRvum5vJNrk6j1zcnn/wa8/iTsPL+DVz9kOALK2CRBKMTXXVjrAi/P5vPM24Zp9cwCA77lgC67eOxfZNxnqh06E0scLL5iPfGdBM7h0XI5t4aYrduAf7zqGDx86jF+++TL5HZ15U1EotflDGsjBfd6WJs6fb+KLD53C7Q+KB/vynTMAgJdctBXnbQlJwp5NdVy9dw7P2SU+r7q2PC89z5dSz3TNxWJbMO+qa+FFF85HJqukB5tkk0dPL+OZlU7mw9/zRLbtSy7aihsu2AIA+N3PPgQAuHrPLGbqDl5+yVZpSNKghgp+5r4T+NMvPyY/+9Q9x2Lf73qiQ9KrL9+BK/fMyvdPL7VxWXBOgDBU76+/8SQeOL6IuYYbO57ltocvPHASr796F97+/m9hqd3Da6/aJT+nifnWLz2Kf3n4NHbM1HBZcF1IS5+uOSKmvN3DPUcW8K0nzwIALtwWOssd28LuuTrOn2/irsNncddh8R3P5zjX6uGynTP4wWv3ZJ4nQBDKI2dXsXdzHT/+wvPx9g98CwDw+G++FgDwG/94Py7YOoU//pHn5W5r2Mg13oyx1wE4yTk/xBh7Wdr3OOe3ArgVAA4ePNjXCuIr775R/v3K3/vnmGzy5DMrsd/EZBNNXrAsFqkTot5MjYqDz/3CS/GXX3kcv/YP96IbbKvd9VCbrgrjHfz49FIbPZ/DC5I8PC7CmGhieO7eOXztl8Pxc85x0a98MqbDqmFkem0GOha9iStjwJ++9ToAwH/6yF340B1PRdiRY1v4xM+8KHZuAOC/fuJefOTQ4ch+/uAt1+CK3bO4+/CCGEcQYnd8QTigKraFeiWqeTcSSgX8+Y9fJ1n/z9x4UWzftsXwW2+8Ev/po3cDAN76Pfvxysu3R75z1Z45fPPxM5HfEP7o312Lf33kM5GqkUBc8646NrbPVPHUM9ndd4Bwsp+fquL2X3oZXvG7X5SZnG++XpCO//KG50R+M11z8fGffqH8f9WxIp10aLU3XXOC9mddVB0bP/Y9+yPbSdS8lWiTb/zyKzMlj7sPL+D1f/jliKbs+RxvuHoXbrpiJwDgz3/8+uwToIAjrAt056+9Wq4edXzt0afx5lu/FusUtaBkOwKh8X56uQOLAd/51VfHtvWev78b/3T3cQChNq7mJyy3w/N69Z5Z/N1/FOfdYkwSmpojdPaVoAUbAHz07Tdg51wo4bi2hdmGi9t/6WWR/R9faOEF//3zsXDONJDkmuZPWWz1JFGcxNomLwTwBsbY4wD+BsArGGPvH+moIJZ+HS3ZIenhjIUKaktGi7FohmXCA6SXjqRQQbWpLqCURvU5wEUBHPpcjy9njCUWjFIN0YqWSkwPkp5so26ZJhOnYDgYA5NiHN1kdI7IUFA5gKOB8U6STeoVOyabWAU86+r5Tvp+PPY++p2q4hwktLXCVACwd1MDT52JT+46dMPfUIxnntygjkl2b/LD5gfTgWxweqktJR0VibKJ62R+riIpFI8KY5WB+m19FZO434QoEs45zq52IwafZIszy51Up2mzEi8bq97vS0rWpHpcjIU5HVXXQqMqop9OB6ut6VqU5aeVxdXLG+SBmqCnGe9lPUhhkmqbcM7fzTnfwznfD+DNAL7AOf/fRj0wN6Fc6VPPrMRmNz1JJ0yPF69qrDYHTzy3YdH2wHh3SPNmkThvMryeL+zhSseTBs1OePJmE+psq4aIygDQLqhIlG7Uk+q0FDU06vY9baKpaDfysaBvpGtbqFV0zVuEDnLO5RiKjMCKGO+ksUWvpz4pVR07ZrxlnLfSiWfv5gYOJ6zMdOgx4iRbVByrcJiXOiYqTAWERu70UkdOfioSZZNKeLx5k6E0oorx8/1ik2gSOI/6htJAk5KaQbzU7sHzufQ3qOPr+Tx1e82qSORRI7xaSq0ZtV6JelyMKb4OJ4xwObawKseoXj/dl0IgOVC/p9JAE0uS8eacY6nTKxVeOUxMXJw3wbFZTDZZbPdiEQX6d/SLpjPvpCeIsgPJobfaDUMFvSTmzTk4F98j5m0lWCbRJEFj3j1PRkfoRhoQTE4v0q86mOg+KWq8RSlN8SM6FprgaNLSmXfFCZk3LUvJyPlcbWqRv391mEnnSL/v9eNSY6oJOnsGgL2b6jh2rpUbXqqXk60HDta8rEQVNTca5+1I5i2u69MpzDvphKmlXPNOZ5IR9XxeaiJXh8ERtn/LYu9q0S0CPYezimxiWyycDFPOJ6101DIY7a4Xi1SJMW/l7FQdSzrGjwX37HTNjdxrabHudN3zSkmEYxP3S1KD75WOSD5LqJQ8FpQy3pzzL3LOXzeqwahwrTjzBhBbGqdFmxCoDrD8f9K+tHjQdteXSTrqrBox3hCyicd56o2f1CSh1fWxOSjcL420so9Oz88MNwwNcFHZJMrsgXCykrJJV2feTLb7UjVv2n8Z2cTKlU2it7y+3K66Vmx1lWS892xugHPkdp2nbZFxqbvRc1EEVceOdNJR0+MB4MxKtzjzVo13zumsOiL8TW231/N54qSYBdUQdpXM0jRQNqpqvOm+mNN0cpJ2UmWT4Byp/TtbQVVEIJyYdOOtHmLNtWUFwRPnWrBYEJGlHFfa5GEH7dGKMG/P53KyT8qhSOqhOc5ok4lm3kknWHda0neIfeglYVmEefNczbvn+eh4vozzjjLv0MHIuWDOnp8smQBBvZME5r2pGTXeqvnqejzOvJXNk0xTtNWVOnl5muZNsgMd4rGIwzIabRIyb15qmaiOPcnG6Mlv+uSbKJt4XqwLz95NIjokT/eOMW+Xik4Vf+zU/p49LxrnrX5HR3KooKp554T4MSYSgRTm7XOOEouGCKhWe15ST9URYaWq/4aMt+7kpAksbUIgErDc6cnr1+p68v0lxSBGSJFybnTmPV1zRd/aAsybjkcnBElQ2XmSbLKUYLzHiQk23lHmTRdGd1rqqdI6I7VV4522LyWxpxVsr16xYrVN6IIH/kr4XFzgtGduNkE2aXV9TNcc2BZLlk08P9IAGYjO5sRU7RRNTwelFQNhNqGueROOBvqhKpvQ+KuK8Q63nb//XOatXRVXO64k2YQ6x6vYu1nE0udFnOisXcompZi3ha4nIo+6WrQJIYl5q8dPE2ijZG0Tqp9CELJJucdYlU2oBHAeprVyr2HCTCXyPTLeaZMhJdKstD15H7Z7fsx4ez6PkCJ1a1XHlkz9+EJLnveo8U4/JlX2ykJh401masw2fGKNt2uxiPHe3KhgS7MSY95Uk4Ipv1MRcVjyZINDN5rnc2k461myiR+anOV2L102CaJNOI+y95pjo+HaMixKnbiTZBOVkdGNUph5I5156wbr2Fkl2sQNjbcd1LCmbdDxlJZNimje2nGpqeiETsJSf+dsHY7F8pm3brwl8y4nm9C2en5Y22SqmsO8I9uI7r8o9G41ns/7dlgCYfONPExr5V4pIWquEWXeJLGkbZMq/y13etI4t7uhIVc1b/V+UQ+x6lqRsETS5FXZRLcDKtTCYlkgIjdddQoz74msbTJuOHbUeM/WXezZ3MBTScZbiRTQGSm9TwYn6dTK6oIel7Nt1bVhWbrDMqgfzEMDttTuZcomXS+aMt/q+qgFoU6rUjYJ90Fe/DTIUMGibIslad5MvqoTD43TtcPaJp7PUXUs+SD5fjgZFos2Sf6boB9pPNokWfPWjbdtMeyaq8fuDx1SNrGjoYJlHJZquFnX43CdQPNWZZMENhspzRB8XuuDeetx3v3LJsU0b0AcmxoqmC6biP+nRpsQ8+70JIlo93w5iUdkk5RVW8W2IiuWmQTmnTUhJa3mkkC2YNtMFatdL+ZfSyJf48QEG29LxvMCwqu9b3M8lpdSkQk6IyUb5/k81gYt/E1oqGTzYVnbRDHegRHxeNTYpjmMwoJR4U0vxmuLwjoJjsm84ko0nqKV49Q4b4qmUQ1/ktGqOCySIl5xLNDuPB46LIuwDPU7SZNcLM47Fm1iJ0abJBnHvZvreOpMjsNSi2vupySrGm7WVTRv0obV76ggZujaTJ6LRlnmrTFg4TAvK5uEuknHK8a8dca/sNJFVcnEDb+XPRkSM19ue3L2b3U9OYmnxnkHr5WASKirHGLeEVkqS/N2436UJJAtoKqJes7GshLWCAzWUKMfTKzx1mWTubqLfZvrOHJmNRIjKpl38P9YtEnwCenUWcy754WShVrbhBCRTRSmkCabzCaUahXlO0WbsZU2NVANf5NkvJPivNPYftJvQ807nlCUxJAqgSOTHswI8w4ibYBkJq0jL1RQ595xh2Vykk7SuPduyo/1FjIBk2OhYyxTNlQy766PnscjhIFYYKKOHHyNOu8A5SePJNmkLPNWz3CnxwutOij1n3B2pRuTTID8aJOGwrypYFSr68nbIE82ofNK8gugMG9lP/nMu4jxFt8h460/m4sm2iQZ+vJ5rlHB3k0N9HwuoyKAuP5JLIROIl1/YXSyNe+eqnlX7Eh6PKDKJqFBXGr1UjXHJOPd6nqoujaaVTsSVUJjWEiojBc13nScA2jedrbxJhlATWChY/R9HsomJTXvpK/rVRtjsomboHn34g5LQCTqPL3cSXQEp/1WDYEsCtKzWz0v6GEZbo8YYTLzFnBtJu/LpO9lQdWeOecxx14ZcHDZfCN3v1UHiwrzPLvaiSToEEg2SdsmySbLbU860Ns9Xz5PJM14PBq/TiSM/A1Vx5bPTKLDMlPztgrFebcV2QSIG+8wVDB3UyPBxBpvXf6YrQvZBIiGg4lymFakEJUKS2reZMTiF5UMfs8Po01qrjBYqnEhI6JKB4J5Jx/DDJVqDS46hWbVHAt1pR4x5+FNmci8lTHTLF80tpfaR9HxieON6oc6wtKnoRwgwynDE1kI6oo+UTaJRZskyCYJtU2SmO2eTSLi5HCGdNLxvMiERRNUmT6HdK1a3UDzVsZMS/hkzZtkk3Ay7MthqRmN8rJJ+He358caYiTuV5NrFrTU+HB82bIJrTSW2z3ZMKXVDTstUfd5PXNUZ95AyOKpGYdKJrImpMKySUDWqDdomvFOa7M4akys8dYdcnMNV5aDVZ1SxLzTklfoegqjxxPZn1r3l5h3zRW1TSLMu0sNT3lEoyvKvDuecMxUXRvNih2J8yaDkiebyBT3orIJwm5AerQJkCabRHXbim0pKxgx3qJ5IWVrmyQ6LJOYd6LmHb8/dOi/rffDvIPf0/Urz7xFp/mKY5WuSzJVdWVBtLDcQalNyHFw0ryd/DFQc2W6l6gdWXx8FHqZvM2KY6FiWxEJptX1w+cpkqSjjJmMtxs/12qIJkEPOVVRU0r6ZiEmm2irYlWf18c5Dkyu8daY91zdxc7ZGmyLRcIFaRlMrDKpMBUQprQnnVu1y3SrWyBUUIk2WS5hvOn31aBHZKJskuewlGF6mV+TiDLvBM1bqVEt39OcblVXl02SHb+J+9fGokO3mfHCVLas5khIChUElESdMsZbMu/i3VbovBDzUu/V6QzNm47fsUVCSc2xSj/spCkvtcOaGmWZN4HivIto3lNV0eSZ7mHRjixuvJvVbM0bEHq1aghFj1OK3gr7Uialx1eV+HmSvGi1oyLPYVmkS1fosBSyid4jlSYaI5to0C/+XFDUfvdcPZKIQdEbdAL1ixYa7/Q4bzVJRzosgwbEKjOUsoly3X2erj/TTUVeajUMsRmRTXiObBLdH1A8njRR87bihnr7TLScJhDKJhU7ZIiUXVrU5qgTW9J5ihWm0pN0gjFEmiSnaN7zUxXUXCsz4qSjGatBmDcxL5XlTUnjncC8g8OvBLJJPWjvVQZTSqp6r1/mrVyTonHedFx0L6c5LGnyypoQmhVH8wOFzFvtS5kkm9QU5k0TxUyC8c7KgyiqeRPz3hbIJno/1eWOkU0SoTNoWqLt3VyPM2+F5ejLbtKGeRAlwRIeF0cxTHRRa44dY9SROG/l/TTjbVssEuBPoYY1R8SpLivMO1s2UTTvlBK0qVB+mxVtMtdw5Q1Pr3XJvEPNm469aGJIXoalHl2VFG0CREt4pskmjDHs2RTPBVAhfhsa1r4078CAUJyvE4k2cSPfiYwvuPecwGFZ1lkJhA7BpVaYD1A2SUdGCnKeuorRMa3UHmn3PKx2vUTNWzLvjG02KrYWgeVJUrLa9dDz/BjzpmNUVzSZsslQok3E9Z2pO6i5Vpx5t0N7QEiyL6PC5Bpv7eTTjbJvcwOHVYel9iDrDi/6Xybzlo2IeYR56+w2TI/nEaOTZUfVyoJSNnFFnHen58sHkJjK2RzmLUvQFs/RARBEJnhxaYn2a1tMdjt3EzRvpshPfgnqXTZJJ1aYyomX8Gz3vIgBVrF3U3ast36/9Me8if0GXY0SNO8s5k0Oy3rgVymDUDbphjH/JXVzAkf6KkbHtCLXyASdRlK0ST7zblTjzFulrcsdD56XzLyTZZO48c5KYquVdFjWXBuzdTf2bC4naN7jxMQab90Ik762d3MDp5c68sTpkQc0W9PFpmuos2UVjow24WgFbLjqWDFjE8omxZg3EKTIr0Y7hhDzBkS8K+chA87rAF9aNpEsS2HeCaGCNmOYnxYPo+6wrDqWdJB6PoASsknEYZlwnvSiPkklYYFo3fYsg0N1vdO6mnS06Iq+QgVJNgk0z0qC5l1LKgkbwLGF1i3Ob1mHZdgYIWklVQTqtwtnWCr7Jb06SfPOK0wFiAqAasKLmqQDBFnGPLnUbTLzHq1sUnVEM2m9HDVdf7rVTJJOgKQ4byBePS7GvHXZRHG05Tssfdl8mDGWLZsoxiFr2TpbD8vCRph3lYy32CYZo0SHpbL5MnVFxE8D2QjZ0SaWxTA/VYVjxRNYqo4lmT5NXMVlE/XvPqJN3ATZJMPg7N3UwKLCDnXov+0vVJA070A2Kax5i+Ov2OLeqrnlHZbTCQ7LsiVhCZyX17wXWz3JQBNDBWWSTvqYGprm3VHS4wHBaOOddALZRJGa6BmikFwVWYWpyAmuJvslod0V2duMMcG8Y7JJUm2TzE0OFRNrvPWLT1lUMtY7cFp2tKpo8XreisMyJT1eDRVsdX25lE5j3r4fXe5nGbKZWtgKrZ3IvD1wcGmkFla7McMUkU3Khgoq+maW5m0zIZuoD3JNqXUdkU385JDLJFgR3TL+eUw2SYjzBnTZJL0SXlZ1wZ7n49tPno2c334yLLOiTcJQwSTNW8CxQtmkLGj7ZODE9kpaDMp9gKiKWIR5zygNGcKKgv1FmzSrtkzGcW0WMO/QWC+2xMSUdO+o171vhyU5wXOMdytoygKIiSrNYWmiTTSoN+Rcw5UzKSVikFNKzI7pfQjVDMu0VQ1tm5h3LbhB0jRvNUkHyF62qmVhKba1WXVkgoHogRcyb8/naFRs/Lvn75PbUMfxn193OQ5sbeKi7WGn7CxIzRthxIYaHUHG0rYYbrxsG26+cqf87Pr9m7Frtobrz98sM+MWW71YI+cs5DNvTTZJiPMG4g7LNOO9J6Ou93eeEh3Ed8yGkTVVx8Jz987hD95yTdZhJI5pqUMGKBzLtfs24fr9m3FgPn59pObtWLjxsm142SXbykebKAy4b4el8ndh5i1lk67SiCFB8644ePXl23Hd/s2p22ooNcynqg5aAfPWJ6akkrDqpHjDgS14zRU7Eu+FrGOqJUhxSaAicgBizJtzHmvGMO5ok9zu8WsF9SH+/TeHD9bmpggHo951+jI4qQ0akJ0eL5m3LxyWNcm8k2UTilyR+yhovE8viWap81NVnAluBLWSH6Hh2vi/fuBKVGwLf/GVxyMP2wsObMEXfvFlqfvToR6CaO9mRRlN8LdlMdx85c6I8X7NlTvxmuD/j55aAiA61XBeQrbJS9LR/p/UgBgIH7S8CImsRJ17jiwAAH72FWGne8YY/l7pDF8EThA6KZm32hRicwN/+7YbEn8nC1NZDO+86VIAwGOnl0vtmyZRtfpkWc1bgmdLUCqko1SZNJKSdCyL4dYfPZi5raZSz2Wq5uCZpQ44F5LQwmo3kISg1TahaJPwty+7REyASchMjw/YdCunsmCrFzJvUd5ZLTDnywxR3zgso1Af4qjXWZT+PHq2JR5kzXkVL0wlEFbCi+8rIpsEzYeBeERHxGGpMu+caJOVjoeu5+PpJWGwNzcrYUeRtmCylhWOo59Kd3ngPGysrILYTd7zv2tOrHiOnF0tGW2iThRJ48pxWGoNY3vBuU9zWM7WXczUnETmfe/Rc5ifqsiki0FQdSxpvIvWAlejTeR7JfdrWwyNii2MKO/PeNM4wvK4+b8XNd4tGW3CWBg+WBYN5XfNSsC8ETJv8hElMe8ijSOAPM27KPP2ZFMNXd9fVjJEjWyiQWXQ+r25e66OI2dXRbd1Hr2gjixMRUZJYd48Oc7bVph3qxcauJhsImuboJRsAogb8vRSG7N1FxUnbONE6fgMTOnuQvtH5LUfMEXfXOl4kSWrOvY8Db3m2pifquJIEIY3LNmEEhvpe3mySVL/Sh17NzcSNe97jp7D5btmh1Iwv+pYUrctXp5XoEghqCxQfZN+mTc9A0XOZXS/rmwCPlt3+3aUqsx7uubI9oOkXdNKNZoeH2feWciONinWQX5VkU1mYsY7DDTI69Q1Kkyu8VZOvv6w7Zqt4+jZ1cSbL6kBMRDW5Eh6bhljcCwmSsJ2wqVSlmyiIjvaJLwhn17qYH5K6IRhL78wNZgYWcONGthhBP5zDqx2ezFWr8omedi9qY6jC6vgvHjT26Jt0GjS1R+6ijTe4loXMt6bkuu+P3xiEVfsmik07jxUHVs6rIo2xpDMO6HUaRlM1UQrtLLOax3EvIuuHKaDhgxpqfFFoTJvYturXS+Wxamm/SfVNslCpuadEMGUBKoACsSZN0WazNSdCPM2nXQQ5QEucQAAIABJREFUPfk6s9g1V8fJxbZcukRDBaPfjWQGZqz2HVs0G14NOscDccbficgm4RXLjvOmG7KHU0ttbJkSS/aQeQvZBAxx5h2MdjDmHf6dJZsUMQC752o4cmYVfqk47/DvrFBBOoex9Hhtias3EE7C3qDuu3qNHjq+hJ7P8ZxdswVHno2aa0n2Vbx5caB5R2ST8heX+kn2zbxJNinNvB0stbo4m1JRsChU5k0RI62uh4ptoeJYycw7eK0VlU0yS8JSVcj8UMFajvGerrmmAbEO9eTr12HnnIgWeCJwSqlLKb1IT1gSlhyWyRfVsSzZBq2e6rBMTtIpyrxPL7WxVRrvaJw3EG/NNYxJXMZ5c7EvnXmrLdHyQHJVmcJU0dom8c/pPNL1TuoeDyTIJhnMau/mBto9H6cW2/K9e44KZ+UVu4fHvJdkqGA55h1dVZbfN5Vn7V82EZDRRyWY91K7h4WVTmJ2ZVGo0h3Fra92RCPvqaojk9oSMywLhldm3Z9JuQNJaHV9OVmkad7TNWfNok0m1nhH2Il2IXYHzrPHTglPfTTaJEM24TyHefvCeLtW5LeEMOIheqHyok2AUDbZEsgmxICXO57MWJQ1RYbosJRx3hCRNHq3cnpACskmc3W0ez5OL7ULVzXUnc066DySQUsrTEUTJz1webIJEA0XvPfoAqZrjswTGBRV11KMX0nNu9+mkwGom06/DkuC3hKuyH4pSWcQ2aSpdMGh6JlWz5fGO2TeccmtqMMyC+SEzHVY9kKykyqb1NxItMn4RJMJNt5OJNok+hlFPjz2dNx46w8GGQzJllPOrmMxdClUMEfz9rRQwcxok8AJ8/RSGwurXcwHzNuyRJLGaqCbMhY6LCXz1o6hH9AviXnrxptOVxH7szswiqLZQVHmrf6d9BsyQGIg6YWp/Mhr1kOclKhzz5FzuHznzNA0yWrGPZeGsBnDYGOYqroinG7csomqeSeECRaFyrxJNvF8EUzQrDqK5p3AvAs6LLOgE4I0qNEmehbnsqJ5r5FqMsHGOxJtEr05dwZJFo8HMbKZtU0k805P0qH9eUEzhnqK5p3Uw1LdZxLIS03xvMS8AcFAljthXQcyAmG0i3h/kEc9ZN6IOGMJZRyWuwK56sjZ1cLLfZYjm8hSvlayYSNWSNmpRQzOHq2ud8/z8cDxc7hi93D0bgCZiWFpSGLe/cwlJF/0+nRYkpTWLimbULZwWhedolCZd6SoFEOkCmdk1SbreQ9usmgbefVN9CQdFVLzrrpKwxZT2wRAepw3QGFrFWkQsxyWkTZoSJdNbIuh6/lBg+DAeGsPZaQZQ8I+klBzRUfxRwOJh5g3IOSR1SDaRMgm5LCknnxDcFhKzTtZNinjsNwzJ4zi2ZVuCdlEGUtGhmWaw5IxFinhGWre6Qys5trYOl2Vssmjp5fR6vpD07uBfpm3eHUyJMEiaFbtgZg3oYjzVwXJJpwn1zUpimYC8wbE5Nas2jLO20li3gWjTbJQNFRQTY+vu3bEtpDxnlI0b3Wc48DEGm8nh53smqvj8UA2UavExep5K8xblIRNPruuzeQFCeOso98lBxGlx9O28x6e2borMxTnVeZdceTyizHEZBPCMKJNOEQFw9Q47wIGYKbuyNCuolES+aGC0XEkxUxHjHdBg7N3U9i0gzIrhxVpAkRrcReO8w6+ViQpJgtTVReez+X9Wtp4a7JJURlnSmHJcwM4LJsJoYKAeN6aqsMyIcOynxroOoqEClK/2aqS86FOWMvtHuquDcdmJklHh5PgrFCxa7YelmxUZuO4w1L8X9bzTtmfbTGZdEEe5rRnQiSWcMkg8iSH2bqLo0HH+xjzVsphxqJNMrdaDtynZWCyw7IIAxTZrbXg72L7jUSbZDkspWwSvyXVhrGdApo3ECTqBMz73qPnUHMtHJhvFht0ASQlhuUhbMbQf4YlEBpRqu7Xb7JM0XMp96sY2kGYd0NLjycwCBmFJuhBMiyzUIR502dqLRU1UWepLeLSbRb2iDXRJgHUhzjp2SCnJRBdQmcVpuIpDYhpfzrzTpNDiMVTScq8Oh8zyg26RTHejYotC1MJ2URsR8ZiS817AIdlMDaqoRJ3WJaTZijSp3htk/DvrKqCYbRJGvMurnkDIuLk2EILPc/HPUcWcOmOmcIhfUWgEobCDkhG3x9Q89bSyMtWFaRv0zktEypIGMRhWVUaL6sTgsWiksqoHJaVApq32lGLMBsx3j1MVZ2gz234OyObQI82SWDec2FluMw2aGqcN083hLYVyiZpSToEkk9Igsh7dumi11wrkqDQqDjROO9RJOkEr5QNmGa8i2L3pnr+lxQkLX1VEGsJMywTmHcfssmeTXV4PsfRsy3cd/TcUPVuMSZVNinKvAVUY9/PxEwGj0qzlm+DJr5fNtokYrwHYN6MMXkf6rKJysSthNX3MJi3bTG4Nstk3rSqV1eqaunZ5XYPzaroc2s66WhwM2qbACEDBPQlbLLDMis9HhAPIMkmabVN6DPaFt2ARWQTANjSrEa22ZCyiUh6cTXZhDDIZE67WwmyAfUMy7IP/u7AaVlcNgn/TmxArGdYJmretozJLRqbTNUF//W7p7HY7uGKIerdYkzp91wawlDBAeO8AwOXFA9dBmWTdKh/JpBcUbAMiGFPaQ5L9f+JsskQHJaAYNRZcd6SeSv705l3s+KEtYMyOnWNChNrvLNqmwCabJJQ20SG2ZFskjM7OhbDYhBfmhbnDQjDSkWumpJ558gmwUWfn45Ws2tUHJlizZTjqLsUbYLoQfQB+iV1qo9nWJbbHq14+mtAHP/cz4k2AcQDq8smuZp3EC74qXuOAxius1Lff+Fok+A1KYqiDMjALQwom4RVBcsz70E0byCUHFXnJZhmvNVhDVE2AcQ9lVUSVu1fSdAdltM1Rym/QcMcn26yLox3smySbLzd1HreyIw2caxwGZXWSQcQF5MSfuh7RaJNAGC+GfXQNyoiSYfY5ygclnS8JM+kZVgWdbZQM4zCcd7qvjKYd1qcN4DkUMEc471zrgaLAV/57mk4FsPFO4o1rygKNU27KPOV0SbK2PtyWGrGu6zDcpDaJoBYvQ1qRIn4qDHflKRDiBSmCs5UVm/QMqjmMu+4wzLGvKtONI9kzJhY450nm2xpVuRNp7Ig/UaOntzs9HhCvRDzDm+8vIeHtDI10gQQBXpWup4wYAwx2WSYSTqkede1ioWlNW+STQrvP3sS5tCYd6LmbccyLPMMjmtb2DlbR9fjuHj79NAYWzim8o+OjDZRCUY/zFuXTfpcmZVNjyfmPYizktCo2GBMXFuauCk9nqAOi27TomPNg+oET0Kew3KxJYy3msE9buSeCcZYjTH2DcbYnYyxexlj/3UcA8tj3pbFsCvItMx6kKP1vDM0b+WBqmkZjirqFTvIsOSKw7Kg5j0VZd71ikitbXW9xHrehGE4LKXmPaDDctt0Fa4db86chmh6fPxzvbaJ3sMSCB40PcOywENMafLPGVIZ2MiY+og3plM2eHq81rRgwGYMReuL034HlUwAIZcQQaOJkCEaOqgXpnIsNrSIITX8NAlkvKspssm51a6MNgGo3tHkZVi2AbyCc341gOcCuIkx9oLRDksPFUy+OUk6yXqQow2Is4x3+AEtlZIMVN21g9omSE2j1yE1b515V6k4VbQji3SYUrRJ9uazIWWT5GiTsg5Ly2KiB2Rhh2Ue845+lsi8XVsa7Y7nwWLFIjxI9x5mWrwcU1/MW2DQkrBVx4JrMxnnPXAzhoIG0bEt1F17KMa7UbHlhK2SpdRQQbChRJoQqo6VEyqYFOcdjq3nc0xVHbk6INlkokIFucBS8F83+DfyKSarJCxh11w90tk8CfTRH93+iMy0S9xfCdmk3RNNGxxbhBzlyibBRY8xb+ql1xUV1SjzrlGJOiyHUZhquZMcbdLPpnfP1Ut00sk23tA079wMy16xnotAGHEyEubdjyGRzHuwOG/GWGr1vTIom2EJCGY8DNmkWXHkNQ+ZN4s4RW2Nefez2kmDWM1lJenEHZZ6N52mwrzXQvMu1ISOMWYDOATgQgB/xDn/esJ3bgFwCwDs27dP/7g0kspB6njNFTvkjffDz98nE1EA4G0vvQDfeWoBb3/ZBXj4xCJOL7YxVXXw8rSGpcoDleWwfNFFW/FkUPDo+edvQbvn40UXzmcey3N2zuLFF83HOmpHnF6M4YYL5vHkMysxp8wwNO805n31njlcvnMGN1+5o/A2v/+5u3HkbLzNWOL+Vd0ywd7959ddjvf8/d34Dy8+gGbVwVQlfktWHUteW71naRZecek23PnU2ZEw7yt2z+LSHdO4Zt9c4d9cumMGL75oHpfunJbv9Xttp2oOzqwMKJsE57IMOfjBa3fjku3T+V/Mwcsv3Sbv87CWELB9pobnn78Zy50eLtgWOplfcvHWaGRKCn70hvNw3pb8TNq9mxv4zL3HU8kAtSdUjfdzdkbvo6mqjaVAjvR94cTUydEoUch4c849AM9ljM0B+Bhj7ArO+T3ad24FcCsAHDx4cOBpSMQ9M3S99KzIGy/bjhsv2w4A+I0fuDLy2XlbmvjkO14MAPj4//6i3P1FZBMnPc775it34CdedL78/01X5Bu92YaL//8nnh97X33mKo6FGy7Yghsu2CLfo4+L1s5IAi2RKdpE17yv2D2LfwrOU1G8+frik3NeevyVe2bl9fmelEmwERTwAoROW5SBXbF7Fn/61usKj7UMLtg6hU/93EtK/WZzs5J4H/QDEXMtJtBB2qCV1d/f/ZrL+tqXjpuu2CGfnaqy0q04Fj70UzfEvn/zlTtx85U7c7f76993RaH9v/bKnfjIocP40kOn8MrLt8c+Dx2WSrRJw8V7XnsZ/s9/vB+AYN70XPlcJIRdf/7m2LZGhVJrP875WQC3A7hpNMOJgpyIZXXZQfZVcaywTGrCbocZtaDqnYmxwqQDF6ydkQVyWNaGHHWRh7yqgkVQd0Uyk++LYkHDijiYBPR7TtTO7Xafk3u75xWWoEYJkk3G8ZwTXnTRPOYaLj5x19HEz1ta2DBBHaPQvMX/u76P4+dakczvUaNItMnWgHGDMVYH8CoAD4x6YEDIOMdjvMU+1GVP0n6H6TTRmbcO+rjfwkNANFSw7toDbasf5CXpFAGVyG33fHR6/lCvwVqj36uhxkeXruetpMcPmu05DJB80q923w9c28JrrtiJz953Qq7qVCSFCgLRe3hKCRU8sdCG5/NI/smoUeTK7QRwO2PsLgDfBPBZzvltox2WAN1Y47imjl4UCska7TANh8q6shjQIJFl9NPVhP6V44BqV/p9OMN+n71SDsv1gH55yZRSZ2OQHpaTcC6zMppHiddfvRMrHQ+ff+BE7LNWV6zwdLKjnms1SefIWeEH2zU7PuOdq3lzzu8CcM0YxhJDGLw/RuatGDh9v/YQ40yB6KSUNCnQ7gdhJDrzHjdyo00KgMa90vHQ8SbD4Kw1osks/comkyFBhbLJePf7/PO3YNt0FZ+48yhed9WuyGetrpdYR0U15mqct2gNiIlj3muG8TLvhGxNzdgMe7keYd4JDxFp4oMwEtVhqUeajANJHcDLgibUVtcrFW2yHtBvLYy0kLpC+6RokwmZCIl5j1M2of299qqduP3BU7JvJqHdi7cMBOKaNw05NN4TpHmvJdZE866omnf0O8M23ur2k7z+dNiDRJuQbVhu99ZENsmrKlgEoWziCbY4AQZnaOhXNlHrYA8Q5z0JmreM8x6zbAIAr796Fzo9H5+5NyqdqP0rVagTZTPCvFcwXXUwXRs8Br4o1v7KZYAM6liMd5LmHWPewzV+VkTzTt/2YMxbQG2sPE7k1TYpAhr3KjHvDWS8+9a8A+NdtqIgELL9SZkIQ+Y9/n1fs3cOezbV8Yk7o1Enaud4FTRRVmxLRKYpssk4JRNgwo03sYJxTMhUwUw1cPp+h1VLOGn7mdEmgxhvSo9PaD48bgwqm6x2Np5s0i+oBkg/rFtN0hm0zsowII33GjBvxhhef/UufPmR03hmuSPfV5sPq6DTHRalE/8/fGYVO8comQATbryJDY9DC6OCSLVM5j1CzTvDYdkPu5LbCF5X2l6s+fC40e/DSePeiA7Lfq8sxXkPYvDEKmZtJ3Qg9PeshWwCAK+/ahc8n+Of7j4m30uVTah9G02ewZiX2j3DvFWMM0nHtuPGW580hi+bhH9nsclhxHl3vHjz4XHDyCZx9GuwmgPJJgIdzx+4k/0wQNdzrdqJXbZzGhdsbUakk1aKw5Kul2w+rly/3cZ4h3Clw3IM+yLZpJJeR3z4DkuVeSc5LIOVx0CySfj3WssmfRtvKZv00DZJOgCGI5t4Pp8IhyWNoeulF4oaJRhjeMPVu/GNx5/B8YUWAMG8k8gaPYvkc1Dv6Z2zRjaRIOY9juUUseyo5q0x71Fq3naGw3Ig2ST87Zob7z5Pnxpt0ul5Q18BrUdI2WRAZjMJqxgiaZ01Mt4A8Lqrd4Jz4LYgXb7d9VJkE/HalMY7/MzIJgocm40tcN9NkE1GHW2iGtYszXsQcqQewnqVTWqqbLLRNO9+o01qgxjv8DeTwLzpeq4V8wZEobHn7JrBJ+4Suneaw5JpzJsZ2SQZrm2NRe8GwmiTqPGOfmeUcd7Z6fHDOQdrzrz7PA7bEoX4N2K0Sb9JOlMDOCzzopzGDSmb9NZG8ya84epduPOps3jy6RW0etlx3lPayocxUc52nFj7K5cBxyrebmvgfRWK8x6y8Vasd2aG5UAOy/UvmwBi7IvtHnw+GQZnWOj39iaH2cCyyQRMhGuteRNee5UoOfuJu46mxnnT+dZlk61T1bHfl2t/5TLg2tbY2gqRbBKtbRL9zvBlkxDJDkvxOpDDUvm7vsahgoNMxHXXxkLQfGAjGe9+YVksUpK0DKL33dqfy0nQvAFgz6YGnnfeJvzDd45myCbidYrivIM3xq13AxNuvJ0SjW4HRSibZNQ2GbrDUmXe8RuFOisNozAVEG+BNm4MZLwrNs6uiiSKSWCLk4Bm1e7PeCvXYRKSdCoTwrwB4PVX7cSDJxbh83gtbyDOvOlUjrOmCWGinwLHssbnsCxQz3vcmjf1xdsw0SYDXMtGxcHZDci8B+Em/TJvFVlRTuNCKJusreYNADdftVPep0nPO9kE3WE8zlKwcixj32MJuGNl3knRJtHvjLa2SfxSUNLCsOK816IwlYpBQj7rri0b7m4k4z0Ipmpufw5L5W83Qa4bN9wJiDYhbJuuyVaEWVUF9TjvnUY2icKx2Rg176TaJqNl3nlef2m8h5AeD6y9bDII6pVQ895YSTr9X9vpqjNwZ6RJkKCk5t1be+MNiHR5INl4S9mkEpVNdq+BbLK2Hqwc3Hjp9rHV47hyzyxedfl2XLpzRr6nPhdbmhW88KLsLvFlYeVoj0Mx3hOUYTkIKNoEmAyDMywMQk7ecPUu6Qfod5+TsIq5dt8mvOLSbXjXay5d66EAENLJ7Q+exPPO2xT77JId03jV5dvx3H1zAIDLdszglZdtx3X7x9d4mDDRxvvll27Dyy/dNpZ9zU9V8T9/9GDkPdW4/tYbr8K1++IXcxBEMyzTNe9hFeZaa9lkEKirhkkwOMPCIFf2Tdft7W+fOffduFFzbfzZW69b62FIzNRcvO9HDiZ+Nlt3I3ZiU7OCP/mx5O+OGmt/5SYYw+gCU3z7Ccw7MN6D7Vt1WE70XJ0JdeIx6fHDwyRkWBr0B3PlMqAazVEY77xt+oFsMlBJ2AkKFRwEG5Z5r0UN65yyDAbrA+bKZUB1Bg3iWErdfs6D2wuM9zA66TCGxHTf9QJVr99IBmdNYj2UnRrmvX5hrlwGRh1jnrd9fygOyzB+fa2K3Q8DanboJOi0w8JaX5KNFLnzbIO5chmIMN6RPGTZG/WHkWEZvK5nyQQA6sqqYSMx77VAJM57A02EzzaYK5eBUdvuPJtMDsvBeliK1/UcaQJEna0biS2uieadkxxmsD5grlwG8qJBhrn9JHje8OK813OMNxCdfIzBGR4mobaJQX8wT0EGRp2an2u8+RDS40HVEtdvmCCgRZuYpf5AmLSqggb9wVy5DFgjlk2KhgoOlKRDssk6jjQBNm60yVpg0pJ0DPqDuXIZYCNO0snbpjeEDEv65XpO0AGMbDIqmHO5fmGuXAaizHsNNG9/8AxLGSq4QTRvxgZLWjKI3ssm2mT9wly5DKy15j2M2iYbJVSw4YqVQ8W21nW8+iRg0gpTGfQHc+UyMOraJrmyyRDT4zdKtMlGChOcBBjmvX5hrlwGRh3nXdR4D5Yev7Fkk4opSjUwItEmxnivW5grl4FRZ1jmyybidShx3u46d1i6hnmPAkY2Wb8wVy4Do69tUsxhOYzCVPXK+r7UtsVQdSxjbIaB4Kaw2PBqxRuMH+ZJyEBE8x4B9aYtpi1dh9KMQabHr2/mDQjpxCzzBwfdy2YiXN/IvXqMsb2MsdsZY/cxxu5ljL1jHAObBIy6njdNDk5KinJvGOnxwYPaWOfRJoA4BmNwhgfjrFzfKELHegB+kXP+LcbYNIBDjLHPcs7vG/HY1hyMiQbInI/GYcmRHU1iClNFUa8Y4z0M0D1h/AfrG7lXj3N+jHP+reDvRQD3A9g96oFNCgapK5KHbsCs0xjQUOp5B68bxngbtjgw6J4wzHt9o5QQyhjbD+AaAF9P+OwWALcAwL59+4YwtMmAYL18JIkh81MV/PDz9+Et1yefr9/5oavx+59/GNcEnar7wWW7ZvD6q3fhmr39b2NQ/PYbr8Jq1xt4O//2un2obUC2eMtLDuClF28d+37NKmZ9g/FgaZ77RcamAPwzgN/gnP9d1ncPHjzI77jjjiEMb+1xyXs+iXbPx4ffdgOu2795rYdjYDAwvvHYM3jT+76KC7dN4XO/8NK1Ho5BAMbYIc554Vb0haZexpgL4KMAPpBnuDcaSG82AVUGGwW0iDQS1PpGkWgTBuBPAdzPOf+90Q9psmDCYA02Klwjm6xrFLl6LwTwIwBewRj7TvDv5hGPa2Igmbcx4gYbBHQrVw3zXtfIdVhyzr+MZ7FqEBrtZ+0pMNhgoHvadcw9vZ5hpt4cWJZh3gYbE0bzXt8wVy8Ho67pbWAwfoh72sR5r2+Yq5cDclgaE26w0WDivNc3zNXLAZMOS2O+DTYGTKjgxoC5ejkwzNtgo0FWszTMe13DXL0cGM3bYKPCaN7rG+bq5cDEeRtsNJAEaJj3+oa5ejlgUjYx1ttgY8BUFdwYMFcvB4Z5G2xUGOa9vmGuXg5MbRODjYYw2sTc3OsZxnjnwDgsDTYaTA/LjQFz9XIgNW9jww02GIzmvb5hrl4OwnrexnobbAxI2cQw73UNc/VyYGQTg40Kw7zXN8zVy4GRTQw2Kkx6/PqGuXo5MKGCBhsNRjbZGDBXLwdWcIaM5m2wUXDB1im8+bq9uP5801B7PSO3k86zHUbzNthoqLk2fvONV631MAwGhGHeOWBGNjEwMJhAGOOdA1MS1sDAYBJhjHcOjMPSwMBgEmGMdw5MbRMDA4NJhDHeOQjbnxkrbmBgMDkwxjsHlknSMTAwmEAY450D26LaJgYGBgaTA2O8c2DivA0MDCYRxnjnIIzzNkbcwMBgcmCMdw5MnLeBgcEkwhjvHJg4bwMDg0mEMd45MHHeBgYGkwhjvHPATCcdAwODCYQx3jkwcd4GBgaTCGO8c2BCBQ0MDCYRxnjnwBhvAwODSYQx3jkwPSwNDAwmEbnGmzH2Z4yxk4yxe8YxoEmDZZJ0DAwMJhBFmPdfALhpxOOYWJgkHQMDg0lErvHmnH8JwDNjGMtEwmjeBgYGk4ihad6MsVsYY3cwxu44derUsDa75jA9LA0MDCYRQzPenPNbOecHOecHt27dOqzNrjlC2cRYbwMDg8mBiTbJgaltYmBgMIkwxjsHljlDBgYGE4gioYJ/DeCrAC5hjB1mjP3E6Ic1OQhrmxgYGBhMDpy8L3DO3zKOgUwqZFVBY70NDAwmCEYUyIFlqgoaGBhMIIzxzoGJ8zYwMJhEGOOdA1PbxMDAYBJhjHcOLOOwNDAwmEAY452DsBmDMd8GBgaTA2O8c2A0bwMDg0mEMd45MHHeBgYGkwhjvHNgelgaGBhMIozxzoGJ8zYwMJhEGOOdA8vYbAMDgwmEMd45kFEmxogbGBhMEIzxzoEpCWtgYDCJMMY7B3ZwhoztNjAwmCQY450Dk5xjYGAwiTDGOwehbGKMuIGBweTAGO8cWMZfaWBgMIHIbcbwbMf3PmcHej5Hs2pOlYGBweTAMO8c7J9v4qdffuFaD8PAwMAgAmO8DQwMDNYhjPE2MDAwWIcwxtvAwMBgHcIYbwMDA4N1CGO8DQwMDNYhjPE2MDAwWIcwxtvAwMBgHcIYbwMDA4N1CMY5H/5GGTsF4Ik+fz4P4PQQh7MeYI752QFzzM8e9HPc53HOtxb98kiM9yBgjN3BOT+41uMYJ8wxPztgjvnZg3Ect5FNDAwMDNYhjPE2MDAwWIeYRON961oPYA1gjvnZAXPMzx6M/LgnTvM2MDAwMMjHJDJvAwMDA4McGONtYGBgsA4xMcabMXYTY+xBxtgjjLF3rfV4yoIxtpcxdjtj7D7G2L2MsXcE729mjH2WMfZw8LopeJ8xxn4/ON67GGPXKtv6seD7DzPGfkx5/3mMsbuD3/w+m5DGmowxmzH2bcbYbcH/z2eMfT0Y54cYY5Xg/Wrw/0eCz/cr23h38P6DjLHvVd6fuPuCMTbHGPsIY+wBxtj9jLEbNvp1Zoz9fHBf38MY+2vGWG0jXmfG2J8xxk4yxu5R3hv5tU3bRyY452v+D4AN4LsADgCoALgTwOVrPa6Sx7ATwLXB39MAHgJwOYDfBvCu4P13Afit4O+bAXwSoj3mCwB8PXh/M4BHg9dNwd+bgs+/LGkXAAADx0lEQVS+EXyXBb99zVofdzCuXwDwQQC3Bf//WwBvDv7+YwBvD/7+jwD+OPj7zQA+FPx9eXDNqwDOD+4Fe1LvCwB/CeAng78rAOY28nUGsBvAYwDqyvV960a8zgBeAuBaAPco74382qbtI3Osa/0gBIO9AcCnlf+/G8C713pcAx7TxwG8CsCDAHYG7+0E8GDw9/sAvEX5/oPB528B8D7l/fcF7+0E8IDyfuR7a3icewB8HsArANwW3JSnATj6tQXwaQA3BH87wfeYfr3pe5N4XwCYDQwZ097fsNcZwng/FRgjJ7jO37tRrzOA/Yga75Ff27R9ZP2bFNmEbg7C4eC9dYlgmXgNgK8D2M45PxZ8dBzA9uDvtGPOev9wwvtrjfcCeCcAP/j/FgBnOee94P/qOOWxBZ8vBN8vey7WEucDOAXgzwOp6E8YY01s4OvMOT8C4H8AeBLAMYjrdggb+zqrGMe1TdtHKibFeG8YMMamAHwUwM9xzs+pn3ExrW6Y2EzG2OsAnOScH1rrsYwRDsSy+v/jnF8DYBlimSuxAa/zJgDfBzFx7QLQBHDTmg5qjTCOa1t0H5NivI8A2Kv8f0/w3roCY8yFMNwf4Jz/XfD2CcbYzuDznQBOBu+nHXPW+3sS3l9LvBDAGxhjjwP4Gwjp5P8BMMcYc4LvqOOUxxZ8PgvgaZQ/F2uJwwAOc/6/2jd7lgaCIAw/26jYaW2hgWBrYZHCQlBSWKcQhIj6K8TKP2ClpZWFhSJiJ/hRixai4gdGLCwUrLRNMRY7wSN4gha53PE+sMXN7d3u3ns3ZGcmdubHu0RnXmSdp4FnM3s3syawR9S+yDon6YS2aWOk0i3O+xwoe/a6h5jkOMh4Tn/Cs8abwJ2ZrSVOHQCtbPM8MRbestc9Y10BPnzbdAhUQwgD/ounSowHvgKfIYSKj1VP3CsTzGzZzIbMbJio2YmZzQGnQM27ta+59Sxq3t/cPutVCiNAmZjY6br3wszegJcQwqibpoBbCqwzMVxSCSH0+5xaay6szm10Qtu0MdLJKinwQ5Jghlih8QSsZD2ff8x/grjVuQIuvc0QY33HwCNwBAx6/wBs+HqvgfHEvRaBhreFhH0cuPFr1mlLmmW8/km+q01KxI+yAewAvW7v8+OGny8lrl/xdT2QqK7oxvcCGAMuXOt9YkVBoXUGVoF7n9cWsWKkcDoD28S4fpO4y1rqhLZpY/zW9Pd4IYTIId0SNhFCCPEH5LyFECKHyHkLIUQOkfMWQogcIucthBA5RM5bCCFyiJy3EELkkC+KEJnlBBgmAQAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "AazOm8Xeipmf",
        "colab_type": "text"
      },
      "source": [
        "Por padrão o método plotou um gráfico de linhas, o que não é adequado para os dados que estamos analisando.\n",
        "\n",
        "Precisamos mudar o tipo de gráfico para realizar uma análise mais adequada, para fazer isso apenas alteramos o parâmetro **kind** do método ```.plot```. Vamos plotar um [histograma](https://pt.wikipedia.org/wiki/Histograma) rodando a célula a seguir."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "W2s2pBHck1Zt",
        "colab_type": "code",
        "outputId": "5a7aa69d-e704-40ad-a31d-b24fb3a094c1",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 282
        }
      },
      "source": [
        "avaliacoes.query(\"filmeId == 1\")[\"nota\"].plot(kind='hist')"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7ff0839ed320>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 20
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "oYEI1fBOlEdk",
        "colab_type": "text"
      },
      "source": [
        "Legal, agora temos uma visualização muito mais agradavel de analisar. Compare com o gráfico de linhas, qual você acha melhor para análise? \n",
        "\n",
        "P.S: Deixar de usar o gráfico de linhas, não significa que sejá uma visualização ruim. Apenas quer dizer que nossos dados não tem características ideias para serem visualizados como um *line plot*, agora pense em uma [série temporal](https://pt.wikipedia.org/wiki/S%C3%A9rie_temporal). **Você acha que o gráfico de linhas ainda seria uma má ideia?**\n",
        "\n",
        "Antes de analisar o histograms de outros filmes, quero colocar um título na imagem. Vamos ver como podemos fazer isso!\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "C8KHmAMbmPJA",
        "colab_type": "code",
        "outputId": "155753b1-365f-4763-c61d-82bf16c929b4",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 298
        }
      },
      "source": [
        "avaliacoes.query(\"filmeId == 1\")[\"nota\"].plot(kind='hist',\n",
        "                                              title=\"Avaliações do filme Toy Story\")"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7ff08399dc18>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 21
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "yY9xg7-KnFpb",
        "colab_type": "text"
      },
      "source": [
        "Claro que python tem outras ferramentas muito poderosas para manipular gráficos, uma delas é o [matplotlib](https://matplotlib.org/).\n",
        "\n",
        "Que tal experimentar um pouquinho esta poderosa ferramenta? \n",
        "\n",
        "Vamos importar a lib e adicionar título no gráfico usando o matplotlib, veja como fica na célula a seguir."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kSJBUpCmamqK",
        "colab_type": "code",
        "outputId": "3c1c4618-ee72-4a14-949f-fda1c2cd775f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 281
        }
      },
      "source": [
        "import matplotlib.pyplot as plt\n",
        "\n",
        "avaliacoes.query(\"filmeId == 1\")[\"nota\"].plot(kind='hist')\n",
        "plt.title(\"Avaliações do filme Toy Story\")\n",
        "plt.show()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "pagvE8ntpfzv",
        "colab_type": "text"
      },
      "source": [
        "Agora que aprendemos a criar um histograma e manipular os gráficos, vamos plotar informações de outros filmes e realizar uma análise desses gráficos?\n",
        "\n",
        "Vamos plotar o histograma do filme Jumanji e da animação Liga da justiça: Doom."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vEJ5uiDpdKzC",
        "colab_type": "code",
        "outputId": "5ba999ee-5705-4038-ab3c-7dd14c618c25",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 298
        }
      },
      "source": [
        "avaliacoes.query(\"filmeId == 2\")[\"nota\"].plot(kind='hist',\n",
        "                                              title=\"Avaliações do filme Toy Jumanji\")"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7ff083898cc0>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 23
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3qI60nGXdt3W",
        "colab_type": "code",
        "outputId": "a0b0cad0-7ac9-4667-d7e2-ddcf92f9f728",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 298
        }
      },
      "source": [
        "avaliacoes.query(\"filmeId == 102084\")[\"nota\"].plot(kind='hist',\n",
        "                                                   title=\"Avaliações do filme Justice League: Doom\")"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7ff0837ee7f0>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 25
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "wQ1VGLaFqI7z",
        "colab_type": "text"
      },
      "source": [
        "Agora que temos os gráficos, chegou a hora de analisar.\n",
        "\n",
        "A primeira coisa que preciso saber é o que cada eixo do meu gráfico significa. Então, eixo **x** mostra a nota, enquanto eixo **y** a frequência das notas (quantas vezes determinada nota foi dada). \n",
        "\n",
        "Entendido nosso gráfico, vamos contextualizar o cenário que estamos analisando:\n",
        "\n",
        " - Temos 3 filmes, dois muito populares (Toy story e Jumanji) e outro que nenhuma pessoa presente no momento da aula conhecia (animação da liga da justiça). O ponto que chamou a atenção, foi que a animação tinha média de nota maior que dois filmes, aparentemente mais popular, Jumaji e Toy Story. **Será que a animação é um filme tão bom assim?**\n",
        "\n",
        "\n",
        "Dado esse cenário a primeira coisa que me chama a atenção é a animação da liga da justiça ter média de nota igual a 5. Ao analisar o histograma do respectivo filme, verificamos que ele só teve uma avaliação igual a 5, logo, fica evidente que a **quantidade de votos é um aspecto importante na avaliação das médias**. Com apenas uma avaliação, não conseguimos garantir que o filme é realmente bom, tornando a avaliação muito \"volátil\". Imagina que Liga da Justiça receba mais uma avaliação, com nota 0, assim a média seria 2.5. Apenas com mais essa avaliação o filme passaria a ser considerada um \"pior\" que Jumanji e Toy Story.\n",
        "\n",
        "Outro ponto interessante é comparar o histograma de Toy Story e Jumanji, ambos tem médias \"relativamente próximas\". Mas repare que a distribuição de notas são diferentes, Toy Story recebe mais notas 5 e 4 que qualquer outra nota, enquanto Jumanji recebe mais notas 4 e 3, assim concluímos que a **distribuição das notas também é um fator importante na avaliação das médias**.(Se ficar alguma dúvida sobre esse tema reveja o exemplo que o instrutor apresenta no final na aula) \n",
        "\n",
        "\n",
        "Com isso nós fechamos a nossa primeira aula do **#quarentenadados**, viu quanta coisa aprendemos? Que tal colocar isso em prática?\n",
        "\n",
        "\n",
        "**Crie seu próprio notebook, reproduza nossa aula e resolva os desafios que deixamos para vocês**.\n",
        "\n",
        "\n",
        "Até a próxima aula!\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "iJ0nFiyXZu8M",
        "colab_type": "text"
      },
      "source": [
        "## Desafio 1 do [Paulo Silveira](https://twitter.com/paulo_caelum)\n",
        "\n",
        "O Paulo fez uma análise rápida e disse que tem 18 filmes sem avaliações, será que ele acertou?\n",
        "\n",
        "Determine quantos filmes não tem avaliações e quais são esses filmes.\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3e8LMGJAaiXJ",
        "colab_type": "text"
      },
      "source": [
        "## Desafio 2 do [Guilherme Silveira](https://twitter.com/guilhermecaelum)\n",
        "\n",
        "Mudar o nome da coluna nota do dataframe **filmes_com_media** para nota_média após o join."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "cXqu2wt0a2l6",
        "colab_type": "text"
      },
      "source": [
        "## Desafio 3 do [Guilherme Silveira](https://twitter.com/guilhermecaelum)\n",
        "\n",
        "Colocar o número de avaliações por filme, isto é, não só a média mas o TOTAL de votos por filme."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "fZ5F6qwVeVWt",
        "colab_type": "text"
      },
      "source": [
        "## Desafio 4 do [Thiago Gonçalves](https://twitter.com/tgcsantos)\n",
        "\n",
        "\n",
        "Arredondar as médias (coluna de nota média) para duas casas decimais."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "mYzG73cSeigN",
        "colab_type": "text"
      },
      "source": [
        "## Desafio 5 do [Allan Spadini](https://twitter.com/allanspadini)\n",
        "\n",
        "Descobrir os generos dos filmes (quais são eles, únicos). (esse aqui o bicho pega)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "enM3lF2textZ",
        "colab_type": "text"
      },
      "source": [
        "## Desafio 6 da [Thais André](https://twitter.com/thais_tandre)\n",
        "\n",
        "Contar o número de aparições de cada genero."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "SVVAZGGdfA_s",
        "colab_type": "text"
      },
      "source": [
        "## Desafio 7 do [Guilherme Silveira](https://twitter.com/guilhermecaelum)\n",
        "\n",
        "Plotar o gráfico de aparições de cada genero. Pode ser um gráfico de tipo igual a barra."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5uIsDpkAz8oa",
        "colab_type": "text"
      },
      "source": [
        "#Não esqueça de compartilhar a solução dos seus desafios com nossos instrutores, seja no twitter ou linkedin. Boa sorte!"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VzeE2YYNVBxx",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    }
  ]
}