Histograma21.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": [],
      "authorship_tag": "ABX9TyNFePPDIk8BV00gjvor5UMo",
      "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/MontoniSoftwares/aulas_Estacio_python/blob/main/Histograma21.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "# Gera os 1000 pontos seguindo a distribuição normal\n",
        "media = 20\n",
        "desvio_padrao = 2\n",
        "pontos = np.random.normal(media, desvio_padrao, 1000)\n",
        "\n",
        "# Cria o histograma\n",
        "plt.hist(pontos, bins=50)\n",
        "\n",
        "# Define o título do gráfico\n",
        "plt.title('Histograma de pontos seguindo distribuição normal')\n",
        "\n",
        "# Define o rótulo do eixo x\n",
        "plt.xlabel('Valores')\n",
        "\n",
        "# Define o rótulo do eixo y\n",
        "plt.ylabel('Frequência')\n",
        "\n",
        "# Exibe o gráfico\n",
        "plt.show()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 472
        },
        "id": "aVQTr58eHkmU",
        "outputId": "f0d20750-9d5b-4a67-d2e4-dba3cdcf7a4d"
      },
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    }
  ]
}