grafico_barras_vertical.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "grafico_barras_vertical.ipynb",
      "provenance": [],
      "authorship_tag": "ABX9TyNN5rQchSyToDVZq7ACRf1w",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/gilissantos/Matplotlib---Python/blob/master/grafico_barras_vertical.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "XSw1CKkqVNxr",
        "colab_type": "text"
      },
      "source": [
        "<h1 align=\"center\">Gráfico de barras vertical - Python/Matplotlib</h1>\n",
        "\n",
        "<p>\n",
        "O gráfigo de barras veritical possui as mesmas características de barras do tipo horizontal, devemos nos atentar apenas com os eixos X e Y, pois invertem de acordo com a orientação das Barras, criar este tipo de visualização com a biblioteca Matplotlib é simples e prática, porém tem vários parâmetros avançados que não serão abordados neste exemplo. \n",
        "</p>\n",
        "<p>\n",
        "Mais informações acesse o <a href=\"https://matplotlib.org/api/_as_gen/matplotlib.pyplot.bar.html#matplotlib.pyplot.bar\">portal oficial da Matplotlib</a>, neste link.\n",
        "</p>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "i-iZAqZ1XU6U",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Importando as bibliotecas Pandas, Matplotlib e a função pyplot:\n",
        "\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "t0W169yjXdMV",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 297
        },
        "outputId": "e0087adf-f2ef-403d-bace-7a477839b3da"
      },
      "source": [
        "# Criando o Data Frame de títulos da Copa do Mundo de futebol:\n",
        "\n",
        "copas = {\n",
        "            \"selecao\": [\"Brasil\", \"Itália\", \"Alemanha\", \"Argentina\", \"Uruguai\", \"França\", \"Espanha\", \"Inglaterra\"], \n",
        "            \"titulo\": [5, 4, 4, 2, 2, 2, 1, 1]\n",
        "        } \n",
        "\n",
        "df = pd.DataFrame(copas) # df é o codinome de Data Frame\n",
        "\n",
        "df\n",
        "\n"
      ],
      "execution_count": 2,
      "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>selecao</th>\n",
              "      <th>titulo</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Brasil</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Itália</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Alemanha</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Argentina</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Uruguai</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>França</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>Espanha</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>Inglaterra</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "      selecao  titulo\n",
              "0      Brasil       5\n",
              "1      Itália       4\n",
              "2    Alemanha       4\n",
              "3   Argentina       2\n",
              "4     Uruguai       2\n",
              "5      França       2\n",
              "6     Espanha       1\n",
              "7  Inglaterra       1"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 2
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "79YPd1ocXknV",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 366
        },
        "outputId": "645992ca-aa74-4a73-b59b-909081fb990c"
      },
      "source": [
        "# Criando o gráfico de Barras Verical:\n",
        "\n",
        "fig = plt.figure(figsize=(8,4))\n",
        "ax = fig.add_axes([0,0,1,1])\n",
        "ax.bar(df[\"selecao\"], df[\"titulo\"], color=\"lightseagreen\")\n",
        "ax.set_title(\"Campeões da Copa do Mundo de Futebol\")\n",
        "ax.set_xlabel(\"Seleções\")\n",
        "ax.set_ylabel(\"Qtde - Títulos\")\n",
        "plt.show()"
      ],
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 576x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    }
  ]
}