17_ntb_lendo_Url.ipynb

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          "text": [
            "   emp_id emp_name  dept_id  salary\n",
            "0       1    Alice      100   60000\n",
            "1       2      Bob      101   55000\n",
            "2       3  Charlie      100   70000\n",
            "3       4    David      102   48000\n",
            "4       5      Eve      101   65000\n"
          ]
        }
      ],
      "source": [
        "import pandas as pd\n",
        "\n",
        "# URL para o conteúdo bruto do arquivo CSV\n",
        "url = 'https://raw.githubusercontent.com/luasampaio/datasets/main/employee.csv'\n",
        "\n",
        "# Lê o arquivo CSV diretamente do URL\n",
        "df = pd.read_csv(url)\n",
        "\n",
        "# Exibe as primeiras linhas do DataFrame\n",
        "print(df.head())\n"
      ]
    },
    {
      "source": [
        "df.columns"
      ],
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      "outputs": [
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          "data": {
            "text/plain": [
              "Index(['emp_id', 'emp_name', 'dept_id', 'salary'], dtype='object')"
            ]
          },
          "metadata": {},
          "execution_count": 6
        }
      ]
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    {
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      "source": [
        "df = df.add_prefix('employee_')"
      ],
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        "id": "5OLAu9U2rFl3"
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      "execution_count": null,
      "outputs": []
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    {
      "cell_type": "markdown",
      "source": [
        "Adiciona Prefixo"
      ],
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        "id": "O14VDm9J9lik"
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        "df"
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              "   employee_emp_id employee_emp_name  employee_dept_id  employee_salary\n",
              "0                1             Alice               100            60000\n",
              "1                2               Bob               101            55000\n",
              "2                3           Charlie               100            70000\n",
              "3                4             David               102            48000\n",
              "4                5               Eve               101            65000"
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