Solution/Day_20_Solution.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "kernelspec": {
      "display_name": "Python 3",
      "language": "python",
      "name": "python3"
    },
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.7.6"
    },
    "colab": {
      "name": "Data Visualization_Day-3_作業解答.ipynb",
      "provenance": [],
      "collapsed_sections": []
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "2X0xWGxdzgeg",
        "colab_type": "text"
      },
      "source": [
        "**主要目標:**\n",
        "    作業目標(1): 繪製模型殘差 圖型\n",
        "\n",
        "    更改初始值設定\n",
        "溫馨叮嚀: 不熟悉使用沒關係, 提出來, 有專家陪跑"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "hQS6nhFj3DmX",
        "colab_type": "text"
      },
      "source": [
        "# Seaborn圖樣式\n",
        "有五種預設seaborn主題：darkgrid，whitegrid，dark，white，和ticks。它們各自適合不同的應用和個人喜好。默認主題是darkgrid"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xSdq9M6N3DmY",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 285
        },
        "outputId": "3e44559f-9247-4e2f-d8ec-915f8c58920a"
      },
      "source": [
        "import numpy as np\n",
        "import seaborn as sns\n",
        "\n",
        "#設定圖形樣式 - whitegrid\n",
        "# use sns.set\n",
        "sns.set(style=\"whitegrid\")\n",
        "\n",
        "# 利用 NUMPY 去建立資料集\n",
        "# np.random.RandomState 設定數學式\n",
        "'''\n",
        "x = rs.normal(2, 1, 75)\n",
        "y = 2 + 1.5 * x + rs.normal(0, 2, 75)\n",
        "'''\n",
        "rs = np.random.RandomState(7)\n",
        "x = rs.normal(2, 1, 75)\n",
        "y = 2 + 1.5 * x + rs.normal(0, 2, 75)\n",
        "\n",
        "# 畫圖\n",
        "# sns.residplot\n",
        "\n",
        "sns.residplot(x, y, lowess=True, color=\"g\")"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f3e145c09e8>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 7
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "W4wzEsUrkegZ",
        "colab_type": "text"
      },
      "source": [
        "繪製單變數分佈: \n",
        "快速檢視海出生單變數分佈的最便捷方式是distplot() 函數。默認情況下,這將繪製直方圖並適合內核密度估計值(KDE)。"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "gj86RGc6k0fV",
        "colab_type": "text"
      },
      "source": [
        "作業目標(2)\n",
        "(1)使用 distplot()使用簡單的規則來正確猜測預設情況下正確的數位,但嘗試更多或更少的 bin 可能會顯示資料中的其他特徵:\n",
        "(2)有無kde對圖形分布的影響"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DunieTjAk3Ly",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 268
        },
        "outputId": "825190b9-3ab3-4e84-e96b-b5a7b415f49f"
      },
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "import seaborn as sns\n",
        "import matplotlib.pyplot as plt\n",
        "from scipy import stats\n",
        "sns.set(color_codes=True)\n",
        "\n",
        "# bin: 指的是特徵值, \n",
        "# kde: on/off\n",
        "# sns.distplot();\n",
        "\n",
        "sns.distplot(x, bins=15, kde=False, rug=True);"
      ],
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7_5Y7BWEqTXu",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 271
        },
        "outputId": "8cd29c64-e063-40db-fbaf-11b9b4873883"
      },
      "source": [
        "sns.distplot(x, bins=15, kde=True, rug=True);"
      ],
      "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": []
          }
        }
      ]
    }
  ]
}