D19 使用 Matplotlib 繪製各種常用圖表 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": [
    "## 這個不要跑\n",
    "#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": 26,
   "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",
    "\n",
    "#決定底框\n",
    "plt.axes([0.1,0.1,.5,.5])\n",
    "\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",
    "plt.axes([0.2,0.2,.5,.5])\n",
    "plt.xticks([]), plt.yticks([])\n",
    "plt.text(0.1,0.1, 'axes([0.2,0.2,.5,.5])',ha='left',va='center',size=16,alpha=.5)\n",
    "\n",
    "plt.axes([0.3,0.3,.5,.5])\n",
    "plt.xticks([]), plt.yticks([])\n",
    "plt.text(0.1,0.1, 'axes([0.3,0.3,.5,.5])',ha='left',va='center',size=16,alpha=.5)\n",
    "\n",
    "plt.axes([0.4,0.4,.5,.5])\n",
    "plt.xticks([]), plt.yticks([])\n",
    "plt.text(0.1,0.1, 'axes([0.4,0.4,.5,.5])',ha='left',va='center',size=16,alpha=.5)\n",
    "\n",
    "plt.show()\n",
    "\n",
    "# va: supported values are 'top', 'bottom', 'center', 'baseline', 'center_baseline'"
   ]
  },
  {
   "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": [
    "## 這個不要跑\n",
    "\n",
    "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": 33,
   "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",
    "\n",
    "plt.bar(X, -Y2, facecolor='#6666ff', edgecolor='black')\n",
    "# +Y 指的是 XY 四象限的第一象限\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": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "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": "3.8.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}