Exercises/PT-BR/Quiz_Pandas_Solutions.ipynb

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   "source": [
    "**Instruções**\n",
    "\n",
    "- A planilha para fazer o exercício está dentro de arquivo compactado está no site do IBGE: Estatísticas > Econômicas > Contas Nacionais > Produto Interno Bruto dos Municípios\n",
    "\n",
    "- Todos os dados necessários estão na planilha \"PIB dos Municípios - base de dados 2010-2017.xls\"\n",
    "\n",
    "- O conteúdo da lista está nos notebooks 14, 15 e 16. Procurar no stackoverflow/documentação/google é essencial também. O conhecimento medido aqui também envolve saber procurar as dúvidas na internet. Até a entrega ainda haverá outra aula de Pandas. De qualquer forma, sugiro fortemente já começar a fazer agora, assim como já consultar o notebook que ainda não foi dado: \"16_Pandas_EDA\".\n",
    "\n",
    "- Pode-se usar qualquer pacote de visualização nos exercícios de gráficos (matplotlib, seaborn, pandas.plot, etc.). É necessário que os gráficos sejam informativos, logo título dos eixos são obrigatórios (algum tipo de variação nas cores e senso estético são bem-vindos também).\n",
    "\n",
    "- Pode-se usar qualquer pacote em qualquer questão. Em caso de dúvidas sobre o enunciado das questões, me enviem um email.\n",
    "\n",
    "- A entrega é para o dia 05/10/2020"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "ArjUo45xa1g5"
   },
   "source": [
    "# Questões"
   ]
  },
  {
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   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mounted at /content/gdrive\n"
     ]
    }
   ],
   "source": [
    "from google.colab import drive\n",
    "import os\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "drive.mount('/content/gdrive')\n",
    "\n",
    "root_path = 'gdrive/My Drive/Monitoria 5'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "WiU9806I9Efc"
   },
   "source": [
    "Definindo o tamanho de todas as figuras desse notebook"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "id": "14YOP3g_9Dy5"
   },
   "outputs": [],
   "source": [
    "plt.rcParams[\"figure.figsize\"] = (10,7)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "id": "AyRjoBkLxQcn"
   },
   "outputs": [],
   "source": [
    "os.chdir(root_path)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "id": "3ibo-hV3xtLb"
   },
   "outputs": [],
   "source": [
    "df = pd.read_excel(os.listdir()[1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "colab": {
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    },
    "id": "3RiH8awUJOt0",
    "outputId": "b29c0c37-c897-4fe4-ebbe-ccb49c37128f"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Ano', 'Código da Grande Região', 'Nome da Grande Região',\n",
       "       'Código da Unidade da Federação', 'Sigla da Unidade da Federação',\n",
       "       'Nome da Unidade da Federação', 'Código do Município',\n",
       "       'Nome do Município', 'Região Metropolitana', 'Código da Mesorregião',\n",
       "       'Nome da Mesorregião', 'Código da Microrregião', 'Nome da Microrregião',\n",
       "       'Código da Região Geográfica Imediata',\n",
       "       'Nome da Região Geográfica Imediata',\n",
       "       'Município da Região Geográfica Imediata',\n",
       "       'Código da Região Geográfica Intermediária',\n",
       "       'Nome da Região Geográfica Intermediária',\n",
       "       'Município da Região Geográfica Intermediária',\n",
       "       'Código Concentração Urbana', 'Nome Concentração Urbana',\n",
       "       'Tipo Concentração Urbana', 'Código Arranjo Populacional',\n",
       "       'Nome Arranjo Populacional', 'Hierarquia Urbana',\n",
       "       'Hierarquia Urbana (principais categorias)', 'Código da Região Rural',\n",
       "       'Nome da Região Rural',\n",
       "       'Região rural (segundo classificação do núcleo)', 'Amazônia Legal',\n",
       "       'Semiárido', 'Cidade-Região de São Paulo',\n",
       "       'Valor adicionado bruto da Agropecuária, \\na preços correntes\\n(R$ 1.000)',\n",
       "       'Valor adicionado bruto da Indústria,\\na preços correntes\\n(R$ 1.000)',\n",
       "       'Valor adicionado bruto dos Serviços,\\na preços correntes \\n- exceto Administração, defesa, educação e saúde públicas e seguridade social\\n(R$ 1.000)',\n",
       "       'Valor adicionado bruto da Administração, defesa, educação e saúde públicas e seguridade social, \\na preços correntes\\n(R$ 1.000)',\n",
       "       'Valor adicionado bruto total, \\na preços correntes\\n(R$ 1.000)',\n",
       "       'Impostos, líquidos de subsídios, sobre produtos, \\na preços correntes\\n(R$ 1.000)',\n",
       "       'Produto Interno Bruto, \\na preços correntes\\n(R$ 1.000)',\n",
       "       'Produto Interno Bruto per capita, \\na preços correntes\\n(R$ 1,00)',\n",
       "       'Atividade com maior valor adicionado bruto',\n",
       "       'Atividade com segundo maior valor adicionado bruto',\n",
       "       'Atividade com terceiro maior valor adicionado bruto'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 4,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "id": "4AYD5prsx1qX"
   },
   "outputs": [],
   "source": [
    "df.rename(columns = {'Produto Interno Bruto, \\na preços correntes\\n(R$ 1.000)':'PIB',\n",
    "                     'Impostos, líquidos de subsídios, sobre produtos, \\na preços correntes\\n(R$ 1.000)':'Impostos',\n",
    "                     'Nome do Município':'Nome', 'Sigla da Unidade da Federação':'UF',\n",
    "                     'Produto Interno Bruto per capita, \\na preços correntes\\n(R$ 1,00)':'PIB_per_capita',\n",
    "                     'Atividade com maior valor adicionado bruto':'ativ1',\n",
    "                     'Nome da Grande Região':'Grande Região'}, inplace = True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "axjfnTf0YvPO"
   },
   "source": [
    "### 1) Crie uma coluna no DataFrame gerado pela leitura do csv com a proporção Impostos/PIB. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "id": "phPVN4mdJgO6"
   },
   "outputs": [],
   "source": [
    "df['carga_trib'] = df['Impostos']/df['PIB']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "LePf5zRHYz13"
   },
   "source": [
    "### 2) Use um método de pd.Series que crie uma série com a quantidade de municípios por UF. [Considere apenas o ano de 2017]."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
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    "id": "jJU3b-XwOQXu",
    "outputId": "8cf44ac7-ba88-496f-cfc9-3dfca0835ba0"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "MG    853\n",
       "SP    645\n",
       "RS    497\n",
       "BA    417\n",
       "PR    399\n",
       "SC    295\n",
       "GO    246\n",
       "PI    224\n",
       "PB    223\n",
       "MA    217\n",
       "PE    185\n",
       "CE    184\n",
       "RN    167\n",
       "PA    144\n",
       "MT    141\n",
       "TO    139\n",
       "AL    102\n",
       "RJ     92\n",
       "MS     79\n",
       "ES     78\n",
       "SE     75\n",
       "AM     62\n",
       "RO     52\n",
       "AC     22\n",
       "AP     16\n",
       "RR     15\n",
       "DF      1\n",
       "Name: UF, dtype: int64"
      ]
     },
     "execution_count": 7,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df.Ano == 2017].UF.value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "ITz4XA95Yzre"
   },
   "source": [
    "### 3) Existe alguma relação entre PIB per capita e atividade com maior valor adicionado bruto? Faça uma média de PIB per capita por atividade com maior valor adicionado bruto. [Considere todos os anos]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
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     "base_uri": "https://localhost:8080/"
    },
    "id": "uoQa0UwJOine",
    "outputId": "64a0639c-382c-4cf5-8e0f-a044a1816ff9"
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   "outputs": [
    {
     "data": {
      "text/plain": [
       "ativ1\n",
       "Indústrias extrativas                                                                   59406.835633\n",
       "Eletricidade e gás, água, esgoto, atividades de gestão de resíduos e descontaminação    52083.287533\n",
       "Comércio e reparação de veículos automotores e motocicletas                             44116.076822\n",
       "Indústrias de transformação                                                             38614.117626\n",
       "Agricultura, inclusive apoio à agricultura e a pós colheita                             25477.872595\n",
       "Construção                                                                              25213.240183\n",
       "Produção florestal, pesca e aquicultura                                                 23190.935992\n",
       "Demais serviços                                                                         22121.604018\n",
       "Pecuária, inclusive apoio à pecuária                                                    19808.056472\n",
       "Administração, defesa, educação e saúde públicas e seguridade social                     8609.703235\n",
       "Name: PIB_per_capita, dtype: float64"
      ]
     },
     "execution_count": 8,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby('ativ1')['PIB_per_capita'].mean().sort_values(ascending = False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "JZ9EwDQZYzhu"
   },
   "source": [
    "### 4) Faça um gráfico de barras com a resposta da questão 3."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "id": "CNc9tF5uPyQ5"
   },
   "outputs": [],
   "source": [
    "dict_atividade = {'Eletricidade e gás, água, esgoto, atividades de gestão de resíduos e descontaminação':'Utilidade Pública',\n",
    "                  'Comércio e reparação de veículos automotores e motocicletas':'Automóveis',\n",
    "                  'Agricultura, inclusive apoio à agricultura e a pós colheita':'Agro',\n",
    "                  'Produção florestal, pesca e aquicultura':'Prod. Florestal',\n",
    "                  'Administração, defesa, educação e saúde públicas e seguridade social':'Adm Pública',\n",
    "                  'Pecuária, inclusive apoio à pecuária':'Pecuária',\n",
    "                  'Indústrias de transformação':'Indústria'}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "id": "75_LPeBgPkDf"
   },
   "outputs": [],
   "source": [
    "df['ativ1'].replace(dict_atividade, inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
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    "id": "a04RrSeTPJOr",
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    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light",
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = df.groupby('ativ1')['PIB_per_capita'].mean().sort_values(ascending = False).plot.bar()\n",
    "ax.set_xlabel('Atividade com Maior VA')\n",
    "ax.set_ylabel('PIB per capita do Município')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Uul80Ei4YzWS"
   },
   "source": [
    "### 5) Crie um dicionário com 'UF:Cidade com maior PIB per capita em 2017' para todas as UFs (excluindo DF)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "id": "me0b3gpjQ2Id"
   },
   "outputs": [],
   "source": [
    "dict_q5 = {}\n",
    "\n",
    "for i in df.UF.unique():\n",
    "\n",
    "  df_uf = df[(df.UF == i)&(df.Ano == 2017)].copy()\n",
    "  max_pib_per_capita = df_uf.PIB_per_capita.max()\n",
    "  nome_mun = df_uf[df_uf.PIB_per_capita == max_pib_per_capita].Nome.values[0]\n",
    "  dict_q5[i] = nome_mun"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "0j7lhc49Sq7B",
    "outputId": "988bf7c5-1d07-4805-bd84-ed4657690893"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'AC': 'Epitaciolândia',\n",
       " 'AL': 'Santana do Mundaú',\n",
       " 'AM': 'Manaus',\n",
       " 'AP': 'Ferreira Gomes',\n",
       " 'BA': 'São Francisco do Conde',\n",
       " 'CE': 'São Gonçalo do Amarante',\n",
       " 'DF': 'Brasília',\n",
       " 'ES': 'Presidente Kennedy',\n",
       " 'GO': 'Davinópolis',\n",
       " 'MA': 'Tasso Fragoso',\n",
       " 'MG': 'São Gonçalo do Rio Abaixo',\n",
       " 'MS': 'Selvíria',\n",
       " 'MT': 'Campos de Júlio',\n",
       " 'PA': 'Vitória do Xingu',\n",
       " 'PB': 'Alhandra',\n",
       " 'PE': 'Goiana',\n",
       " 'PI': 'Baixa Grande do Ribeiro',\n",
       " 'PR': 'Saudade do Iguaçu',\n",
       " 'RJ': 'Porto Real',\n",
       " 'RN': 'Guamaré',\n",
       " 'RO': 'Pimenteiras do Oeste',\n",
       " 'RR': 'Boa Vista',\n",
       " 'RS': 'Triunfo',\n",
       " 'SC': 'Piratuba',\n",
       " 'SE': 'Canindé de São Francisco',\n",
       " 'SP': 'Paulínia',\n",
       " 'TO': 'Cariri do Tocantins'}"
      ]
     },
     "execution_count": 13,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dict_q5"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "K7nGfXa5YzMT"
   },
   "source": [
    "### 6) Qual (grande) região mais cresceu entre 2010 e 2017?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "id": "KHE9w6pITMEj"
   },
   "outputs": [],
   "source": [
    "df_q6 = pd.DataFrame(df.groupby(['Ano','Grande Região'])['PIB'].sum()).reset_index()\n",
    "df_q6 = df_q6[df_q6.Ano.isin([2010,2017])]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "id": "O5pnI3RcUJVF"
   },
   "outputs": [],
   "source": [
    "df_q6.set_index('Grande Região', inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "lVGhK6XvUXKC",
    "outputId": "cf58c106-0e70-4379-bb84-7df467a6967e"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Grande Região\n",
       "Centro-oeste    0.859440\n",
       "Nordeste        0.823392\n",
       "Norte           0.776307\n",
       "Sudeste         0.595959\n",
       "Sul             0.808696\n",
       "Name: PIB, dtype: float64"
      ]
     },
     "execution_count": 16,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_q6.groupby('Grande Região')['PIB'].pct_change().dropna()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "B0XDRMlVYzBr"
   },
   "source": [
    "### 7) Quais os 10 municípios que mais perderam PIB no triênio 2014-2015-2016?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "id": "JD9Mr5KDVLoO"
   },
   "outputs": [],
   "source": [
    "df_aux = df[df.Ano.isin([2013,2016])][['Ano','Nome','PIB','UF']].copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "id": "68g9OISBbN1s"
   },
   "outputs": [],
   "source": [
    "df_aux['Nome_uf'] = df_aux['Nome'] + '_' + df_aux['UF']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "id": "Ee13Fp7aZVCP"
   },
   "outputs": [],
   "source": [
    "df_aux.set_index('Nome_uf', inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "id": "XP5ZRnTpZMoI"
   },
   "outputs": [],
   "source": [
    "s_q7 = df_aux.groupby(['Nome_uf'])['PIB'].pct_change().dropna()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "_dgewvcTaqvM",
    "outputId": "84ca7fb8-d5e6-4224-b199-396649a8085c"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Nome_uf\n",
       "Cairu_BA                -0.833563\n",
       "Anchieta_ES             -0.825449\n",
       "Quissamã_RJ             -0.794842\n",
       "Reserva do Cabaçal_MT   -0.776156\n",
       "Ilha Comprida_SP        -0.770627\n",
       "Presidente Kennedy_ES   -0.758609\n",
       "Marataízes_ES           -0.752031\n",
       "Coari_AM                -0.743299\n",
       "Casimiro de Abreu_RJ    -0.718520\n",
       "Araçás_BA               -0.712247\n",
       "Name: PIB, dtype: float64"
      ]
     },
     "execution_count": 21,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s_q7.sort_values()[:10]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "UzqHHZddYy2c"
   },
   "source": [
    "### 8) Quais os 10 municípios do RJ que mais cresceram entre 2010-2017"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "id": "0uj3OaC4b23s"
   },
   "outputs": [],
   "source": [
    "df_q8 = df[(df.UF == 'RJ')&(df.Ano.isin([2010,2017]))].copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "id": "1M6u5_2vchIF"
   },
   "outputs": [],
   "source": [
    "df_q8.set_index('Nome', inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "Xj7c4pGEb5I1",
    "outputId": "9a7db36d-f4b4-4f0d-cc46-9a132628d354"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Nome\n",
       "Maricá                 5.106359\n",
       "Seropédica             4.008024\n",
       "Itatiaia               1.917093\n",
       "Cardoso Moreira        1.753848\n",
       "Japeri                 1.331060\n",
       "São Pedro da Aldeia    1.323124\n",
       "São José de Ubá        1.265320\n",
       "Varre-Sai              1.226160\n",
       "Cordeiro               1.193747\n",
       "Niterói                1.182359\n",
       "Name: PIB, dtype: float64"
      ]
     },
     "execution_count": 24,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_q8.groupby('Nome')['PIB'].pct_change().dropna().sort_values(ascending = False)[:10]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "v0zcly7_Yymy"
   },
   "source": [
    "### 9) Faça um heatmap com as UFs no eixo y e as taxas de crescimento dos anos 2011 até 2017 no eixo x.\n",
    "\n",
    "Questão anulada, mas ficou valendo ponto extra com enunciado novo.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "id": "NTbxFn9z7z6R"
   },
   "outputs": [],
   "source": [
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "id": "JkkSDkh75RUv"
   },
   "outputs": [],
   "source": [
    "df_q9 = df[['UF','Ano','PIB']].copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "id": "baZUOgHRn91a"
   },
   "outputs": [],
   "source": [
    "# Agrupando por UF e Ano os municípios, tirando a média e fazendo a variação percentual dessa média\n",
    "df_q9_heat = pd.DataFrame(df.groupby(['UF','Ano'])['PIB'].mean()).pct_change().reset_index()\n",
    "# Excluindo 2010 e pivotando o dataframe\n",
    "df_q9_heat = df_q9_heat[df_q9_heat.Ano != 2010].pivot('UF','Ano','PIB')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 225
    },
    "id": "MVFSJQfw9cwD",
    "outputId": "14d884a0-ea21-48d1-855e-a3079153a0cf"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>Ano</th>\n",
       "      <th>2011</th>\n",
       "      <th>2012</th>\n",
       "      <th>2013</th>\n",
       "      <th>2014</th>\n",
       "      <th>2015</th>\n",
       "      <th>2016</th>\n",
       "      <th>2017</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>UF</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>AC</th>\n",
       "      <td>0.072771</td>\n",
       "      <td>0.132801</td>\n",
       "      <td>0.131783</td>\n",
       "      <td>0.172981</td>\n",
       "      <td>0.012193</td>\n",
       "      <td>0.009648</td>\n",
       "      <td>0.037576</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AL</th>\n",
       "      <td>0.166744</td>\n",
       "      <td>0.094546</td>\n",
       "      <td>0.075963</td>\n",
       "      <td>0.099040</td>\n",
       "      <td>0.131598</td>\n",
       "      <td>0.066891</td>\n",
       "      <td>0.068219</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AM</th>\n",
       "      <td>0.161921</td>\n",
       "      <td>0.021323</td>\n",
       "      <td>0.149614</td>\n",
       "      <td>0.043556</td>\n",
       "      <td>-0.001159</td>\n",
       "      <td>0.028551</td>\n",
       "      <td>0.046770</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AP</th>\n",
       "      <td>0.142202</td>\n",
       "      <td>0.182974</td>\n",
       "      <td>0.146675</td>\n",
       "      <td>0.049892</td>\n",
       "      <td>0.034403</td>\n",
       "      <td>0.034690</td>\n",
       "      <td>0.079329</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BA</th>\n",
       "      <td>0.078897</td>\n",
       "      <td>0.095855</td>\n",
       "      <td>0.121988</td>\n",
       "      <td>0.093172</td>\n",
       "      <td>0.094287</td>\n",
       "      <td>0.055889</td>\n",
       "      <td>0.038347</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Ano      2011      2012      2013      2014      2015      2016      2017\n",
       "UF                                                                       \n",
       "AC   0.072771  0.132801  0.131783  0.172981  0.012193  0.009648  0.037576\n",
       "AL   0.166744  0.094546  0.075963  0.099040  0.131598  0.066891  0.068219\n",
       "AM   0.161921  0.021323  0.149614  0.043556 -0.001159  0.028551  0.046770\n",
       "AP   0.142202  0.182974  0.146675  0.049892  0.034403  0.034690  0.079329\n",
       "BA   0.078897  0.095855  0.121988  0.093172  0.094287  0.055889  0.038347"
      ]
     },
     "execution_count": 40,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_q9_heat.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 442
    },
    "id": "kpdBjzqR7MsR",
    "outputId": "d514b6ed-1d39-4ecc-fb02-eed3675554aa"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x504 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light",
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = sns.heatmap(df_q9_heat)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "BOIADKCkZUBi"
   },
   "source": [
    "### 10) Crie uma função que receba a 'UF' (sigla) e plote um gráfico de linha do  PIB (eixo y) e os anos no eixo x."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {
    "id": "S9hrnYIy9xIE"
   },
   "outputs": [],
   "source": [
    "def plota_UF(string_UF):\n",
    "\n",
    "  ax, fig = plt.subplots()\n",
    "  ax = df[df.UF == string_UF].groupby('Ano')['PIB'].mean().plot()\n",
    "  ax.set_title(f'Série temporal do PIB de {string_UF}', fontsize = 18)\n",
    "  ax.set_ylabel(f'PIB {string_UF}', fontsize = 14)\n",
    "  ax.set_xlabel(f'Ano', fontsize = 14)\n",
    "  plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 467
    },
    "id": "YSatIhpXA8Z-",
    "outputId": "eaa52083-390b-46c0-9204-77b013ea1555"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light",
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plota_UF('RJ')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "fgwn38Y2BmSx"
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "colab": {
   "collapsed_sections": [],
   "name": "4th Assignment.ipynb",
   "provenance": []
  },
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.10"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}