grafico_area.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "grafico_area.ipynb",
      "provenance": [],
      "authorship_tag": "ABX9TyOcQeTlWbQh36Oqbmb+CDJ4",
      "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_area.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "TbZo95Kblpef",
        "colab_type": "text"
      },
      "source": [
        "<h1 align=\"center\">Gráfico de Área - Python/Matplotlib</h1>\n",
        "\n",
        "<p>\n",
        "O gráfico de <strong>Área</strong> é a <strong>representação acumulada de um gráfico de linhas ao longo de um determinado período</strong>, ou seja, a área possui o total de <strong>X</strong> valores no período <strong>Y</strong>, por exemplo no dia 01 o valor máximo de uma ação na bolsa de valores atingiu 60 mil pontos e no dia 02 o máximo é de 90 mil pontos, em ambos a área terá uma cor uniforme para representar a alta e a baxia de acordo com a data, sendo assim o comportamento das ações em um período. \n",
        "Para maiores informações sobre gráfico de área consulte o portal oficial da biblioteca Matplotlib. Vamos ao código.\n",
        "</p>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "EYSa7oxdoCDl",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Importando a biblioteca Maptplotlib e a função pyplot:\n",
        "\n",
        "import matplotlib.pyplot as plt"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4kJuOeLcojw6",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Vamos criar as variáveis vendas e ano, onde vendas será o nosso eixo Y e ano será o nosso eixo X, caso queira pode usar data frame:\n",
        "\n",
        "vendas = [10, 5, 15, 5, 15, 10, 5, 5, 10, 5]\n",
        "ano = [\"2000\", \"2002\", \"2004\", \"2006\", \"2008\", \"2010\", \"2012\", \"2014\", \"2016\", \"2018\"] "
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DMblLoA2r7vH",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 366
        },
        "outputId": "94fb0d76-3c8f-4a6d-9385-7acad6d82c82"
      },
      "source": [
        "# Criando o gráfico de Área:\n",
        "\n",
        "fig = plt.figure()\n",
        "ax = fig.add_axes([0,0,1,1])\n",
        "ax.fill_between(ano, vendas)\n",
        "ax.set_title(\"Vendas - De 2000 a 2018\")\n",
        "ax.set_xlabel(\"Anos - Bienal\")\n",
        "ax.set_ylabel(\"Vendas\")\n",
        "ax.set_ylim(0, 20)\n",
        "ax.set_yticks([0, 5, 10, 15, 20])\n",
        "plt.grid(True)\n",
        "plt.show()"
      ],
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    }
  ]
}