D20 使用 Seaborn 進行資料視覺化 Data_Visualization_Day_3_作業.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "2X0xWGxdzgeg"
   },
   "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": {
    "colab_type": "text",
    "id": "hQS6nhFj3DmX"
   },
   "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",
   "execution_count": 6,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 34
    },
    "colab_type": "code",
    "id": "xSdq9M6N3DmY",
    "outputId": "85579a75-a87f-4b28-a8ee-76d05d758645"
   },
   "outputs": [
    {
     "data": {
      "application/vnd.google.colaboratory.intrinsic+json": {
       "type": "string"
      },
      "text/plain": [
       "'\\nx = rs.normal(2, 1, 75)\\ny = 2 + 1.5 * x + rs.normal(0, 2, 75)\\n'"
      ]
     },
     "execution_count": 6,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## 不要跑\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "\n",
    "#設定圖形樣式 - whitegrid\n",
    "# use sns.set\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",
    "\n",
    "# 畫圖\n",
    "# sns.residplot(x, y, lowess=True, color=\"g\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'rs' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-1-78dd7b9b266f>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m      9\u001b[0m \u001b[1;31m# np.random.RandomState 設定數學式\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     10\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 11\u001b[1;33m \u001b[0mx\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mrs\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mnormal\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m2\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m1\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m75\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     12\u001b[0m \u001b[0my\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;36m2\u001b[0m \u001b[1;33m+\u001b[0m \u001b[1;36m1.5\u001b[0m \u001b[1;33m*\u001b[0m \u001b[0mx\u001b[0m \u001b[1;33m+\u001b[0m \u001b[0mrs\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mnormal\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m2\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m75\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     13\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mNameError\u001b[0m: name 'rs' is not defined"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "import seaborn as sns\n",
    "\n",
    "#設定圖形樣式 - whitegrid\n",
    "sns.set(style=\"whitegrid\")\n",
    "\n",
    "# 利用 NUMPY 去建立資料集...資料集??\n",
    "\n",
    "# np.random.RandomState 設定數學式\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(x, y, lowess=True, color=\"g\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "gj86RGc6k0fV"
   },
   "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",
   "execution_count": 5,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 268
    },
    "colab_type": "code",
    "id": "DunieTjAk3Ly",
    "outputId": "f857c268-3e0d-4aa2-db01-8bf87de71cf8"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light",
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# bin: 指的是特徵值, \n",
    "# kde: on/off\n",
    "# sns.distplot();\n",
    "# sns.distplot(x, bins=, kde=, rug=);\n",
    "\n",
    "\n",
    "## 不懂"
   ]
  }
 ],
 "metadata": {
  "colab": {
   "collapsed_sections": [],
   "name": "Data Visualization_Day-3_作業.ipynb",
   "provenance": []
  },
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
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   "file_extension": ".py",
   "mimetype": "text/x-python",
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 },
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 "nbformat_minor": 1
}