作業題目Day19(Data Visualization_Day-2_HW).ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "O-YfjN4WJ_SE"
   },
   "source": [
    "# 教學目標 \n",
    "\n",
    "主要說明matplotlib 的基礎操作\n",
    "\n",
    "1. 使用常見的子圖與軸圖來做畫面配置\n",
    "\n",
    "2. 長條圖\n",
    "\n",
    "# 範例重點\n",
    "\n",
    "如何使用亂數, 資料集來操作\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "9vBZOrx9J_SN"
   },
   "source": [
    "# 軸圖進階\n",
    "\n",
    "但是可以將圖放置在圖中的任何位置。因此，如果要在較大的圖中放置較小的圖，則可以使用軸。\n",
    "\n",
    "#特別提醒: tick 刻度線定位器\n",
    "\n",
    "格式正確的刻度線是準備發布的數據的重要組成部分。Matplotlib為滴答提供了一個完全可配置的系統。有刻度線定位器可以指定刻度線應出現的位置，刻度線格式化程序可以為刻度線提供所需的外觀。主刻度線和次刻度線可以相互獨立地定位和格式化。\n",
    "\n",
    "# 問題: 繪製如下圖示:\n",
    "\n",
    "![image.png](attachment:image.png) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "9RiHsXzdJ_SO",
    "outputId": "739957db-8ac9-43d5-a1dc-03208200a58d"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 4 Axes>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#import matplotlib.pyplot as plt\n",
    "\n",
    "#決定底框\n",
    "plt.axes([0.1,0.1,.5,.5])\n",
    "#給定刻度\n",
    "plt.xticks([]), plt.yticks([])\n",
    "plt.text(0.1,0.1, 'axes([0.1,0.1,.5,.5])',ha='left',va='center',size=16,alpha=.5)\n",
    "\n",
    "'''\n",
    "#決定第二層框\n",
    "\n",
    "\n",
    "#決定第三層框\n",
    "\n",
    "\n",
    "#決定第四層框\n",
    "\n",
    "'''\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "#決定最外框\n",
    "plt.axes([0.1,0.1,.5,.5])\n",
    "plt.xticks([]), plt.yticks([])\n",
    "plt.text(0.2,0.1, 'axes([0.1,0.1,.5,.5])',ha='center',va='center',size=16,alpha=.5)\n",
    "\n",
    "#決定內框\n",
    "plt.axes([0.2,0.2,.5,.5])\n",
    "plt.xticks([]), plt.yticks([])\n",
    "plt.text(0.2,0.2, 'axes([0.2,0.2,.5,.5])',ha='center',va='center',size=16,alpha=.5)\n",
    "\n",
    "plt.axes([0.3,0.3,.5,.5])\n",
    "plt.xticks([]), plt.yticks([])\n",
    "plt.text(0.2,0.2, 'axes([0.3,0.3,.5,.5])',ha='center',va='center',size=16,alpha=.5)\n",
    "\n",
    "plt.axes([0.4,0.4,.5,.5])\n",
    "plt.xticks([]), plt.yticks([])\n",
    "plt.text(0.2,0.2, 'axes([0.4,0.4,.5,.5])',ha='center',va='center',size=16,alpha=.5)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "vZcTI6VMJ_SQ"
   },
   "source": [
    "# 條型圖: Bar Plots\n",
    "    \n",
    "長條圖主要用來呈現兩個維度的資料，一個為X軸另一個則為Y軸(當然這邊指的是二維的狀況，較為常見)\n",
    "\n",
    "主要用來呈現兩個維度的資料\n",
    "\n",
    "# 問題: 嘗試通過添加紅色條形標籤重現右側的圖形。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "MCRYCu7jJ_SQ",
    "outputId": "87ec11d3-3af8-4831-c0dd-2cc86c8a8be0"
   },
   "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": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    " #配置12 組 Bar\n",
    "n = 12 \n",
    "X = np.arange(n)\n",
    "\n",
    " #給定數學運算式\n",
    "Y1 = (1-X/float(n)) * np.random.uniform(0.5,1.0,n)\n",
    "Y2 = (1-X/float(n)) * np.random.uniform(0.5,1.0,n)\n",
    "\n",
    "#指定上半部繪製區域, 給定 Bar 顏色, 邊界顏色\n",
    "plt.bar(X, +Y1, facecolor='#9999ff', edgecolor='white')\n",
    "# +Y 指的是 XY 四象限的第一象限\n",
    "\n",
    "'''\n",
    "#指定上半部繪製區域, 給定 Bar 顏色, 邊界顏色\n",
    "'''\n",
    "\n",
    " #設定繪圖圖示區間\n",
    "for x,y in zip(X,Y1):\n",
    "    plt.text(x+0.4, y+0.05, '%.2f' % y, ha='center', va= 'bottom')\n",
    "\n",
    " #設定Y軸區間\n",
    "plt.ylim(-1.25,+1.25)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "id": "YvD2FcAdJ_SR"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    " #配置12 組 Bar\n",
    "n = 12 \n",
    "X = np.arange(n)\n",
    "\n",
    " #給定數學運算式\n",
    "Y1 = (1-X/float(n)) * np.random.uniform(0.5,1.0,n)\n",
    "Y2 = (1-X/float(n)) * np.random.uniform(0.5,1.0,n)\n",
    "\n",
    "#指定上半部繪製區域, 給定 Bar 顏色, 邊界顏色\n",
    "plt.bar(X, +Y1, facecolor='#9999ff', edgecolor='white')\n",
    "# +Y 指的是 XY 四象限的第一象限\n",
    "plt.bar(X, -Y1, facecolor='r', edgecolor='white')\n",
    "'''\n",
    "#指定上半部繪製區域, 給定 Bar 顏色, 邊界顏色\n",
    "'''\n",
    "\n",
    " #設定繪圖圖示區間\n",
    "for x,y in zip(X,Y1):\n",
    "    plt.text(x+0.4, y+0.05, '%.2f' % y, ha='center', va= 'bottom')\n",
    "\n",
    " #設定Y軸區間\n",
    "plt.ylim(-1.25,+1.25)\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "colab": {
   "name": "Data Visualization_Day-2_HW.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": "3.8.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}