10-ntb_pandas_salario.ipynb

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  "metadata": {
    "colab": {
      "provenance": [],
      "authorship_tag": "ABX9TyOH+TvKEwYzbuf/gC1DrrKe",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/luasampaio/CienciaDados/blob/main/10-ntb_pandas_salario.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "_nWRahG3IJ7j"
      },
      "outputs": [],
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data = {\n",
        "    'Nome': ['Ana', 'Bruno', 'Carlos', 'Daniela'],\n",
        "    'Idade': [23, 34, 45, 29],\n",
        "    'Cidade': ['São Paulo', 'Rio de Janeiro', 'Belo Horizonte', 'Curitiba'],\n",
        "    'Salário': [3500, 4200, 5000, 3800]\n",
        "}\n",
        "\n",
        "df = pd.DataFrame(data)\n",
        "print(df)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "k0G2Vv6zIORO",
        "outputId": "c00d1ff8-6b62-47fd-e8f8-d28121394584"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "      Nome  Idade          Cidade  Salário\n",
            "0      Ana     23       São Paulo     3500\n",
            "1    Bruno     34  Rio de Janeiro     4200\n",
            "2   Carlos     45  Belo Horizonte     5000\n",
            "3  Daniela     29        Curitiba     3800\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(df.head())\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "N8eoJnDLIQhX",
        "outputId": "3474556f-312c-4c41-92c2-59cde08fc2d5"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "      Nome  Idade          Cidade  Salário\n",
            "0      Ana     23       São Paulo     3500\n",
            "1    Bruno     34  Rio de Janeiro     4200\n",
            "2   Carlos     45  Belo Horizonte     5000\n",
            "3  Daniela     29        Curitiba     3800\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(df.describe())\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Y-mh6RqjISgp",
        "outputId": "29c766bb-70ca-4fb0-d323-0d324f7b0367"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "           Idade  Salário\n",
            "count   4.000000      4.0\n",
            "mean   32.750000   4125.0\n",
            "std     9.322911    650.0\n",
            "min    23.000000   3500.0\n",
            "25%    27.500000   3725.0\n",
            "50%    31.500000   4000.0\n",
            "75%    36.750000   4400.0\n",
            "max    45.000000   5000.0\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df_filtrado = df[df['Idade'] > 30]\n",
        "print(df_filtrado)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "q0t3n5izIhMY",
        "outputId": "965611fc-64ba-4704-e75a-c5a0201707c0"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "     Nome  Idade          Cidade  Salário\n",
            "1   Bruno     34  Rio de Janeiro     4200\n",
            "2  Carlos     45  Belo Horizonte     5000\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df['Bonus'] = df['Salário'] * 0.10\n",
        "print(df)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Fj0hPH6SIj4o",
        "outputId": "bc1e048f-28fe-4643-d23a-0fd6e42f092b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "      Nome  Idade          Cidade  Salário  Bonus\n",
            "0      Ana     23       São Paulo     3500  350.0\n",
            "1    Bruno     34  Rio de Janeiro     4200  420.0\n",
            "2   Carlos     45  Belo Horizonte     5000  500.0\n",
            "3  Daniela     29        Curitiba     3800  380.0\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.to_csv('dados.csv', index=False)\n"
      ],
      "metadata": {
        "id": "e4TRRltGImfX"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt\n",
        "\n",
        "df.plot(x='Nome', y='Salário', kind='bar', title='Salário por Pessoa')\n",
        "plt.show()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 511
        },
        "id": "_Cr7w5n9Ip4f",
        "outputId": "5d5be09b-b6fe-45a6-989c-c262141bd137"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "PPeETQALIwp9"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "sRgfq3G78MYT"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}