D20_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": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "In C:\\Users\\user\\Anaconda3\\lib\\site-packages\\matplotlib\\mpl-data\\stylelib\\_classic_test.mplstyle: \n",
      "The text.latex.preview rcparam was deprecated in Matplotlib 3.3 and will be removed two minor releases later.\n",
      "In C:\\Users\\user\\Anaconda3\\lib\\site-packages\\matplotlib\\mpl-data\\stylelib\\_classic_test.mplstyle: \n",
      "The mathtext.fallback_to_cm rcparam was deprecated in Matplotlib 3.3 and will be removed two minor releases later.\n",
      "In C:\\Users\\user\\Anaconda3\\lib\\site-packages\\matplotlib\\mpl-data\\stylelib\\_classic_test.mplstyle: Support for setting the 'mathtext.fallback_to_cm' rcParam is deprecated since 3.3 and will be removed two minor releases later; use 'mathtext.fallback : 'cm' instead.\n",
      "In C:\\Users\\user\\Anaconda3\\lib\\site-packages\\matplotlib\\mpl-data\\stylelib\\_classic_test.mplstyle: \n",
      "The validate_bool_maybe_none function was deprecated in Matplotlib 3.3 and will be removed two minor releases later.\n",
      "In C:\\Users\\user\\Anaconda3\\lib\\site-packages\\matplotlib\\mpl-data\\stylelib\\_classic_test.mplstyle: \n",
      "The savefig.jpeg_quality rcparam was deprecated in Matplotlib 3.3 and will be removed two minor releases later.\n",
      "In C:\\Users\\user\\Anaconda3\\lib\\site-packages\\matplotlib\\mpl-data\\stylelib\\_classic_test.mplstyle: \n",
      "The keymap.all_axes rcparam was deprecated in Matplotlib 3.3 and will be removed two minor releases later.\n",
      "In C:\\Users\\user\\Anaconda3\\lib\\site-packages\\matplotlib\\mpl-data\\stylelib\\_classic_test.mplstyle: \n",
      "The animation.avconv_path rcparam was deprecated in Matplotlib 3.3 and will be removed two minor releases later.\n",
      "In C:\\Users\\user\\Anaconda3\\lib\\site-packages\\matplotlib\\mpl-data\\stylelib\\_classic_test.mplstyle: \n",
      "The animation.avconv_args rcparam was deprecated in Matplotlib 3.3 and will be removed two minor releases later.\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 34
    },
    "colab_type": "code",
    "id": "xSdq9M6N3DmY",
    "outputId": "85579a75-a87f-4b28-a8ee-76d05d758645"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\user\\Anaconda3\\lib\\site-packages\\seaborn\\_decorators.py:43: FutureWarning: Pass the following variables as keyword args: x, y. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
      "  FutureWarning\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#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(20) \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": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x1958cae2ef0>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "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",
    "sns.displot(x, bins=30, kde=False, rug=True)"
   ]
  }
 ],
 "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": "3.7.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}