D19_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": 3,
   "metadata": {
    "id": "9RiHsXzdJ_SO",
    "outputId": "739957db-8ac9-43d5-a1dc-03208200a58d",
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 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",
    "#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.subplot(2,1,1)\n",
    "# plt.subplot(2,1,2)\n",
    "# plt.show()\n",
    "# plt.subplot(1,2,1)\n",
    "# plt.subplot(1,2,2)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "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",
    "#first\n",
    "plt.axes([0.1,0.1,.5,.5])\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",
    "#second\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",
    "#third\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",
    "#fourth\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",
    "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": 6,
   "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"
     },
     "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": 10,
   "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",
    "plt.bar(X, -Y2, facecolor='#0066CC', 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, y+0.05, '%.2f' % y, ha='center', va= 'bottom')\n",
    "for x,y in zip(X,Y2):\n",
    "    plt.text(x, -(y+0.05), '%.2f' % y, ha='center', va= 'top')\n",
    " #設定Y軸區間\n",
    "plt.ylim(-1.25,+1.25)\n",
    "\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": "3.7.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}