Untitled0.ipynb

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      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
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        "<a href=\"https://colab.research.google.com/github/Allanjs1/python.unopar/blob/main/Untitled0.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": "code",
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      "metadata": {
        "id": "kmwhjIEfrIol"
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      "outputs": [],
      "source": []
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    {
      "cell_type": "code",
      "source": [
        "import sqlite3\n",
        "\n",
        "# CREATE (Criação da tabela e inserção de dados de exemplo)\n",
        "\n",
        "conn = sqlite3.connect('contatos.db')\n",
        "\n",
        "cursor = conn.cursor()\n",
        "\n",
        "cursor.execute('''\n",
        "\n",
        "    CREATE TABLE IF NOT EXISTS Contatos (\n",
        "\n",
        "        id INTEGER PRIMARY KEY AUTOINCREMENT,\n",
        "\n",
        "        nome TEXT,\n",
        "\n",
        "        email TEXT,\n",
        "\n",
        "        telefone TEXT\n",
        "\n",
        "    )\n",
        "\n",
        "''')\n",
        "\n",
        "dados_exemplo = [\n",
        "\n",
        "    ('João', 'joao@email.com', '123-456-7890'),\n",
        "\n",
        "    ('Maria', 'maria@email.com', '987-654-3210'),\n",
        "\n",
        "    ('Carlos', 'carlos@email.com', '555-555-5555')\n",
        "\n",
        "]\n",
        "\n",
        "cursor.executemany('INSERT INTO Contatos (nome, email, telefone) VALUES (?, ?, ?)', dados_exemplo)\n",
        "\n",
        "conn.commit()\n",
        "\n",
        "# READ (Leitura e exibição dos contatos)\n",
        "\n",
        "cursor.execute('SELECT * FROM Contatos')\n",
        "\n",
        "contatos = cursor.fetchall()\n",
        "\n",
        "print(\"Contatos:\")\n",
        "\n",
        "for contato in contatos:\n",
        "\n",
        "    print(contato)\n",
        "\n",
        "# UPDATE (Atualização do número de telefone do contato com ID 2)\n",
        "\n",
        "novo_telefone = '999-999-9999'\n",
        "\n",
        "contato_id = 2\n",
        "\n",
        "\n",
        "\n",
        "cursor.execute('UPDATE Contatos SET telefone = ? WHERE id = ?', (novo_telefone, contato_id))\n",
        "\n",
        "conn.commit()\n",
        "\n",
        "# DELETE (Exclusão do contato com ID 1)\n",
        "\n",
        "contato_id_para_excluir = 1\n",
        "\n",
        "\n",
        "\n",
        "cursor.execute('DELETE FROM Contatos WHERE id = ?', (contato_id_para_excluir,))\n",
        "\n",
        "conn.commit()\n",
        "\n",
        "# Fechando a conexão\n",
        "\n",
        "conn.close()"
      ],
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          "text": [
            "Contatos:\n",
            "(2, 'Maria', 'maria@email.com', '999-999-9999')\n",
            "(3, 'Carlos', 'carlos@email.com', '555-555-5555')\n",
            "(4, 'João', 'joao@email.com', '123-456-7890')\n",
            "(5, 'Maria', 'maria@email.com', '987-654-3210')\n",
            "(6, 'Carlos', 'carlos@email.com', '555-555-5555')\n",
            "(7, 'João', 'joao@email.com', '123-456-7890')\n",
            "(8, 'Maria', 'maria@email.com', '987-654-3210')\n",
            "(9, 'Carlos', 'carlos@email.com', '555-555-5555')\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "\n",
        "\n",
        "# Criando uma lista de valores\n",
        "\n",
        "data = [10, 20, 30, 40, 50]\n",
        "\n",
        "# Criando uma Series a partir da lista\n",
        "\n",
        "series1 = pd.Series(data)\n",
        "\n",
        "print(data)\n",
        "\n",
        "print(series1)"
      ],
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        },
        "id": "ZU56RZZers7_",
        "outputId": "c5ada2bb-7378-4695-8616-dbad1bc79d61"
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      "execution_count": 14,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[10, 20, 30, 40, 50]\n",
            "0    10\n",
            "1    20\n",
            "2    30\n",
            "3    40\n",
            "4    50\n",
            "dtype: int64\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "\n",
        "\n",
        "\n",
        "# Criando um dicionário com pares chave-valor\n",
        "\n",
        "data = {'A': 100, 'B': 200, 'C': 300, 'D': 400, 'E': 500}\n",
        "\n",
        "\n",
        "# Criando uma Series a partir do dicionário\n",
        "\n",
        "series2 = pd.Series(data)\n",
        "\n",
        "\n",
        "print(data)\n",
        "print(series2)\n",
        "\n"
      ],
      "metadata": {
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        },
        "id": "EuJQsx9J4VDc",
        "outputId": "83ca96ea-2562-444f-ecb6-7df0f3a38081"
      },
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "{'A': 100, 'B': 200, 'C': 300, 'D': 400, 'E': 500}\n",
            "A    100\n",
            "B    200\n",
            "C    300\n",
            "D    400\n",
            "E    500\n",
            "dtype: int64\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "\n",
        "url = 'https://www.fdic.gov/resources/resolutions/bank-failures/failed-bank-list/'\n",
        "\n",
        "dfs = pd.read_html(url)\n",
        "\n",
        "\n",
        "print(type(dfs))\n",
        "print(len(dfs))"
      ],
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        },
        "id": "GosnJ-mj6lTT",
        "outputId": "aa764b0a-2ea7-4b64-969b-fcf52fc931f3"
      },
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'list'>\n",
            "1\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df_bancos = dfs[0]"
      ],
      "metadata": {
        "id": "7C8GVXF86xak"
      },
      "execution_count": 21,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(df_bancos.shape)"
      ],
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          "base_uri": "https://localhost:8080/"
        },
        "id": "1ndBpG7N646x",
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      },
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(10, 7)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(df_bancos.dtypes)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2Ly00fUI7LIu",
        "outputId": "12041e2f-6861-4f03-a286-6eb844806bcd"
      },
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Bank Name               object\n",
            "City                    object\n",
            "State                   object\n",
            "Cert                     int64\n",
            "Aquiring Institution    object\n",
            "Closing Date            object\n",
            "Fund  Sort ascending     int64\n",
            "dtype: object\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df_bancos.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "1z-golBG7awP",
        "outputId": "716e81c7-c969-4dd8-f4dd-207b5a7f7dbd"
      },
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                               Bank Name           City         State   Cert  \\\n",
              "0  Republic First Bank dba Republic Bank   Philadelphia  Pennsylvania  27332   \n",
              "1                          Citizens Bank       Sac City          Iowa   8758   \n",
              "2               Heartland Tri-State Bank        Elkhart        Kansas  25851   \n",
              "3                    First Republic Bank  San Francisco    California  59017   \n",
              "4                         Signature Bank       New York      New York  57053   \n",
              "\n",
              "                Aquiring Institution      Closing Date  Fund  Sort ascending  \n",
              "0  Fulton Bank, National Association    April 26, 2024                 10546  \n",
              "1          Iowa Trust & Savings Bank  November 3, 2023                 10545  \n",
              "2             Dream First Bank, N.A.     July 28, 2023                 10544  \n",
              "3          JPMorgan Chase Bank, N.A.       May 1, 2023                 10543  \n",
              "4                Flagstar Bank, N.A.    March 12, 2023                 10540  "
            ],
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              "      <th>Bank Name</th>\n",
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              "      <td>Republic First Bank dba Republic Bank</td>\n",
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              "      <td>Pennsylvania</td>\n",
              "      <td>27332</td>\n",
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              "      <td>April 26, 2024</td>\n",
              "      <td>10546</td>\n",
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              "      <th>1</th>\n",
              "      <td>Citizens Bank</td>\n",
              "      <td>Sac City</td>\n",
              "      <td>Iowa</td>\n",
              "      <td>8758</td>\n",
              "      <td>Iowa Trust &amp; Savings Bank</td>\n",
              "      <td>November 3, 2023</td>\n",
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              "      <td>Elkhart</td>\n",
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              "      <td>July 28, 2023</td>\n",
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              "      <td>59017</td>\n",
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              "      <td>May 1, 2023</td>\n",
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              "    <tr>\n",
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              "      <td>Signature Bank</td>\n",
              "      <td>New York</td>\n",
              "      <td>New York</td>\n",
              "      <td>57053</td>\n",
              "      <td>Flagstar Bank, N.A.</td>\n",
              "      <td>March 12, 2023</td>\n",
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              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-29e27a71-2252-43f6-8d6f-262dc9c94736 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "df_bancos",
              "summary": "{\n  \"name\": \"df_bancos\",\n  \"rows\": 10,\n  \"fields\": [\n    {\n      \"column\": \"Bank Name\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 10,\n        \"samples\": [\n          \"The First State Bank\",\n          \"Citizens Bank\",\n          \"Silicon Valley Bank\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"City\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 10,\n        \"samples\": [\n          \"Barboursville\",\n          \"Sac City\",\n          \"Santa Clara\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"State\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 8,\n        \"samples\": [\n          \"Iowa\",\n          \"Florida\",\n          \"Pennsylvania\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Cert\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 17455,\n        \"min\": 8758,\n        \"max\": 59017,\n        \"num_unique_values\": 10,\n        \"samples\": [\n          14361,\n          8758,\n          24735\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Aquiring Institution\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 10,\n        \"samples\": [\n          \"MVB Bank, Inc.\",\n          \"Iowa Trust & Savings Bank\",\n          \"First Citizens Bank & Trust Company\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Closing Date\",\n      \"properties\": {\n        \"dtype\": \"object\",\n        \"num_unique_values\": 10,\n        \"samples\": [\n          \"April 3, 2020\",\n          \"November 3, 2023\",\n          \"March 10, 2023\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Fund  Sort ascending\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 3,\n        \"min\": 10535,\n        \"max\": 10546,\n        \"num_unique_values\": 10,\n        \"samples\": [\n          10536,\n          10545,\n          10539\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 24
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "\n",
        "\n",
        "# Criar um dicionário com nomes e idades\n",
        "\n",
        "dados = {\n",
        "\n",
        "    'Nome': ['Alice', 'Bob', 'Carol', 'David', 'Eve'],\n",
        "\n",
        "    'Idade': [25, 30, 22, 35, 28]\n",
        "}\n",
        "\n",
        "# Criar uma série a partir do dicionário\n",
        "\n",
        "serie_idades = pd.Series(dados['Idade'], index=dados['Nome'])\n",
        "\n",
        "# Exibir a série de idades\n",
        "print(\"Série de Idades:\")\n",
        "print(serie_idades)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "HmVFHrw28Lzt",
        "outputId": "8a40783b-39e5-4af8-8cbb-913d5c037be5"
      },
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Série de Idades:\n",
            "Alice    25\n",
            "Bob      30\n",
            "Carol    22\n",
            "David    35\n",
            "Eve      28\n",
            "dtype: int64\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Calcular a média das idades\n",
        "media_idades = serie_idades.mean()\n",
        "print(\"\\nMédia de Idades:\", media_idades)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "vzzmSv99874u",
        "outputId": "50f1b9d6-c323-4cce-ae5f-4f30a6469b0e"
      },
      "execution_count": 30,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "Média de Idades: 28.0\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [],
      "metadata": {
        "id": "7myLmhZHrrxL"
      }
    }
  ]
}