Homework/Day_20_HW.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": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "#pip install seaborn\n",
    "import seaborn as sns\n",
    "#!pip install statsmodels\n",
    "import statsmodels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "#設定圖形樣式 - whitegrid\n",
    "sns.set_style( 'whitegrid' )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "x = \n",
      " [ 2.49671415  1.8617357   2.64768854  3.52302986  1.76584663  1.76586304\n",
      "  3.57921282  2.76743473  1.53052561  2.54256004  1.53658231  1.53427025\n",
      "  2.24196227  0.08671976  0.27508217  1.43771247  0.98716888  2.31424733\n",
      "  1.09197592  0.5876963   3.46564877  1.7742237   2.0675282   0.57525181\n",
      "  1.45561728  2.11092259  0.84900642  2.37569802  1.39936131  1.70830625\n",
      "  1.39829339  3.85227818  1.98650278  0.94228907  2.82254491  0.77915635\n",
      "  2.2088636   0.04032988  0.67181395  2.19686124  2.73846658  2.17136828\n",
      "  1.88435172  1.6988963   0.52147801  1.28015579  1.53936123  3.05712223\n",
      "  2.34361829  0.23695984  2.32408397  1.61491772  1.323078    2.61167629\n",
      "  3.03099952  2.93128012  1.16078248  1.69078762  2.33126343  2.97554513\n",
      "  1.52082576  1.81434102  0.89366503  0.80379338  2.81252582  3.35624003\n",
      "  1.92798988  3.0035329   2.36163603  1.35488025  2.36139561  3.53803657\n",
      "  1.96417396  3.56464366 -0.6197451 ] \n",
      "\n",
      "y = \n",
      " [ 7.38887624  4.96669768  5.37351811  7.46806634  0.67363211  4.20945079\n",
      "  8.08304437  9.10694018  3.25924798  4.19685286  3.30135937  6.13220961\n",
      "  6.02044563  1.07055923  3.43915812  4.3507238   5.4180433   4.06726481\n",
      "  2.98263959  2.09732814  4.27144326  5.2535761   5.62340285  2.87310463\n",
      "  3.71425165  2.3356424   2.43221899  4.87811799  2.49448743  4.23988795\n",
      "  4.9055418  11.55078908  5.32890979  3.92853439  6.08492554 -0.66880791\n",
      "  5.26026764  2.18095523  7.93420515  4.91056992  6.71079455  5.18762888\n",
      "  2.4891715   6.83399009  4.28608308  5.50229758  2.49026693  9.39127196\n",
      "  2.71172531  3.52915395  9.86703721  2.44130393  2.85202154  6.11681716\n",
      "  5.53954798  3.29559332  3.87829966  2.41157401  6.44408001  4.62446922\n",
      "  7.38110745  3.15500495  2.69637451  4.8327245   3.7570601   7.48927991\n",
      "  7.50627033  3.29033288  5.91172175  4.55208596  7.10573915  4.83315343\n",
      "  2.30534772  8.39084861  1.66435169]\n"
     ]
    }
   ],
   "source": [
    "# 利用 NUMPY 去建立資料集\n",
    "# np.random.RandomState 設定數學式\n",
    "np.random.seed(42)\n",
    "x = np.random.normal( 2, 1, 75 )\n",
    "y = 2 + 1.5 * x + np.random.normal( 0, 2, 75 )\n",
    "\n",
    "print( 'x = \\n', x, '\\n' )\n",
    "print( 'y = \\n', y )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 34
    },
    "colab_type": "code",
    "id": "xSdq9M6N3DmY",
    "outputId": "85579a75-a87f-4b28-a8ee-76d05d758645"
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 576x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 畫圖\n",
    "plt.figure(figsize=(8,5))\n",
    "sns.residplot( x=x, y=y, lowess=True, color='g' )\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "W4wzEsUrkegZ"
   },
   "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": {
    "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": [],
   "source": [
    "# bin: 指的是特徵值, \n",
    "# kde: on/off\n",
    "# sns.distplot();"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\YenLin\\anaconda3\\envs\\TF_python3.7\\lib\\site-packages\\seaborn\\distributions.py:2557: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(x, kde=False, bins=20 )\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.histplot( x, bins=20, kde=False )\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.histplot( x, bins=20, kde=True )\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.histplot( x, bins=40, kde=True )\n",
    "plt.show()"
   ]
  }
 ],
 "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
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}