03-matplotlib.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 資料探索\n",
    "\n",
    "我們先載入這個章節範例程式碼中會使用到的第三方套件、模組或者其中的部分類別、函式。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import numpy as np\n",
    "import matplotlib as mpl\n",
    "import matplotlib.pyplot as plt\n",
    "from matplotlib.font_manager import FontProperties\n",
    "from tensorflow.keras import datasets"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 關於 Matplotlib\n",
    "\n",
    "Matplotlib 是 Python 實踐資料視覺化的核心套件，建構於 NumPy 所提供的 `ndarray` 陣列類別與 SciPy 框架之上，於 2002 年由 John Hunter 構思出作為 IPython 的一環，以類似 Matlab 的語法和樣式來作圖，Matplotlib 套件在 2003 年發佈 0.1.0 版，它相當幸運地在早期階段就被哈伯太空望遠鏡團隊採用作為繪圖軟體，也因為獲得了強而有力的財務支援，在功能和規模開展的過程顯得相當順遂。Matplotlib 主打強項是支援多種主流作業系統、繪圖後端引擎與輸出格式，這使得它成為軟體工程師或資料分析師的生態圈中不可或缺的第三方套件。我們可以執行 Python 的載入指令（`import`）後印出 Matplotlib 的版本號來確認環境中是否安裝了 Matplotlib 可供使用。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3.2.0\n"
     ]
    }
   ],
   "source": [
    "print(mpl.__version__)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "假若得到的回應是：\n",
    "\n",
    "```\n",
    "Traceback (most recent call last):\n",
    "  File \"<stdin>\", line 1, in <module>\n",
    "ModuleNotFoundError: No module named 'matplotlib'\n",
    "```\n",
    "\n",
    "表示目前所處的 Python 環境沒有安裝 Matplotlib，這時要切換回命令列安裝。\n",
    "\n",
    "```bash\n",
    "# 在命令列執行\n",
    "pip install matplotlib\n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 為何 Matplotlib\n",
    "\n",
    "近幾年在資料視覺化（Data Visualization）、商業智慧（Business Intelligence）與資訊視覺圖表（Infographics）等熱門應用領域的推波助瀾之下，視覺化套件的介面設計有了長足進展，新興競爭者如 R 語言的 ggplot2、建構於網頁的 D3.js 與不需寫程式的 Tableau/PowerBI 讓 Matplotlib 顯得有些老態龍鍾，不過在其龐大的使用者社群中，仍然有依賴於它的套件如雨後春筍般誕生，像廣受歡迎於 ggplot2 設計理念相似的 Seaborn，持續為 Matplotlib 的繪圖生態系注入生命力。\n",
    "\n",
    "固然新興套件在外觀、介面友善程度上有很大的優勢，不過諸如 ggplot2、Seaborn 或 plotly 在作圖時的出發點都採用資料框（`DataFrame`）物件，這點與 Matplotlib 只要從數值陣列就可以作圖，相較之下顯得略微遲緩一些，再者由於新興套件的定位屬於更為高階，在客製化使用程度上略遜定位於低階的 Matplotlib，因此本書依舊選擇 Matplotlib 作為入門機器學習的輔助，這點也與多數的機器學習工程師、資料科學家符合。\n",
    "\n",
    "## 使用 Matplotlib 的兩種方式\n",
    "\n",
    "對一開始接觸 Matplotlib 使用者最大挑戰是它的兩種作圖語法風格：\n",
    "\n",
    "1. Matlab 風格\n",
    "2. 物件導向風格\n",
    "\n",
    "我們繪製一個排版外觀 2x1 的畫布呈現這兩種不同的作圖語法風格。首先是 Matlab 風格，對本來就利用 Matlab 作資料分析的使用者非常熟稔，它採用靜態模式介面（Stateful interface），`plt` 物件可以指向當下建構出的圖形（gcf, get current figure）與軸（gca, get current axis），如以下範例所示。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Matlab 風格\n",
    "x = np.linspace(0, np.pi*4, 100)\n",
    "plt.figure()\n",
    "plt.subplot(2, 1, 1)\n",
    "plt.plot(x, np.sin(x))\n",
    "plt.subplot(2, 1, 2)\n",
    "plt.plot(x, np.cos(x))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "第二種是物件導向風格，我們在作圖之前必須分別宣告 `fig` 與 `ax` 兩個物件，然後再呼叫個別方法，繪製出與上述相同排版外觀的作圖結果。由於在未來將常遭遇繪製排版外觀 `m x n` 的畫布，因此我們將主要使用「物件導向風格」來繪製 Matplotlib 的圖形。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 物件導向風格\n",
    "fig, axes = plt.subplots(2, 1)\n",
    "axes[0].plot(x, np.sin(x))\n",
    "axes[1].plot(x, np.cos(x))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 輸出 Matplotlib 作圖\n",
    "\n",
    "當我們希望將作圖結果輸出為圖片，可以使用 `fig.savefig()` 存為圖檔，Matplotlib 支援的格式可以經由 `fig.canvas.get_supported_filetypes()` 得知。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'ps': 'Postscript',\n",
       " 'eps': 'Encapsulated Postscript',\n",
       " 'pdf': 'Portable Document Format',\n",
       " 'pgf': 'PGF code for LaTeX',\n",
       " 'png': 'Portable Network Graphics',\n",
       " 'raw': 'Raw RGBA bitmap',\n",
       " 'rgba': 'Raw RGBA bitmap',\n",
       " 'svg': 'Scalable Vector Graphics',\n",
       " 'svgz': 'Scalable Vector Graphics',\n",
       " 'jpg': 'Joint Photographic Experts Group',\n",
       " 'jpeg': 'Joint Photographic Experts Group',\n",
       " 'tif': 'Tagged Image File Format',\n",
       " 'tiff': 'Tagged Image File Format'}"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure()\n",
    "fig.canvas.get_supported_filetypes()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "將剛才以物件導向風格繪製的圖以常見的 png 格式輸出，讀者可以在 Jupyter Notebook 的工作目錄找到 my_figure.png。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axes = plt.subplots(2, 1)\n",
    "axes[0].plot(x, np.sin(x))\n",
    "axes[1].plot(x, np.cos(x))\n",
    "fig.savefig('my_figure.png')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "my_figure.png\n"
     ]
    }
   ],
   "source": [
    " # 讀者可以在 Jupyter Notebook 的工作目錄找到 my_figure.png\n",
    "for file in os.listdir():\n",
    "    if '.png' in file:\n",
    "        print(file)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 常見的探索性資料分析\n",
    "\n",
    "在物件導向風格的作圖語法中，不論繪製任何類型的圖形，都要先宣告兩個物件：「畫布」與「軸物件」，其中「畫布」以 `plt.figure()` 創建，「軸物件」以 `plt.axes()` 創建；如果要在一個畫布上面繪製多個圖形，那麼這兩個物件可以同時用 `plt.subplots()` 創建；接著再依照圖形種類，呼叫座標物件不同的作圖方法。這裡將作圖流程摘要：\n",
    "\n",
    "- 將資料整理為 `ndarray` 格式\n",
    "- 展開「畫布物件」與「軸物件」\n",
    "- 依照探索需求呼叫「軸物件」的作圖方法\n",
    "- 依照設計需求添加「軸物件」的元素\n",
    "- 呼叫 `plt.show()` 顯示圖形\n",
    "\n",
    "接著我們會從常見的探索性資料分析需求著手，檢視該如何用物件導向風格繪製。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 觀察數值資料相關性的需求\n",
    "\n",
    "我們使用散佈圖（Scatter plot）用作探索兩組數值資料相關情況，藉著圖形可以觀察兩組數值資料之間是否有負相關、正相關或者無相關之特徵。呼叫 `ax.scatter()` 再依序傳入 X 軸、Y 軸的數列就完成繪圖。"
   ]
  },
  {
   "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": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "x = np.linspace(-2*np.pi, 2*np.pi)\n",
    "f = np.sin(x)\n",
    "fig = plt.figure()\n",
    "ax = plt.axes()\n",
    "ax.scatter(x, f)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 觀察類別資料排序的需求\n",
    "\n",
    "我們使用長條圖（Bar plot）用作探索類別資料排序情況之圖形，藉著圖形可以一目瞭然哪個類別的摘要數值最高、最低。呼叫 `ax.bar()` 傳入和摘要相同長度的 X 軸位置數列與摘要數列就能完成繪圖。"
   ]
  },
  {
   "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": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "np.random.seed(42)\n",
    "random_integers = np.random.randint(1, 100, size=100)\n",
    "n_odds = np.sum(random_integers % 2 == 0)\n",
    "n_evens = np.sum(random_integers % 2 == 1)\n",
    "y = np.array([n_odds, n_evens])\n",
    "x = np.array([1, 2])\n",
    "fig = plt.figure()\n",
    "ax = plt.axes()\n",
    "ax.barh(x, y)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 觀察數值資料分佈的需求\n",
    "\n",
    "我們使用直方圖（Histogram plot）用作探索數值資料分佈情況之圖形，藉著圖形可以一目瞭然數值是左傾（Left-skewed）、右傾（Right-skewed）或者其餘的外觀。呼叫 `ax.hist()` 傳入數列就能完成繪圖。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "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": [
    "arr = np.random.normal(size=10000)\n",
    "fig = plt.figure()\n",
    "ax = plt.axes()\n",
    "ax.hist(arr, bins=30)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 觀察數學函式外觀的需求\n",
    "\n",
    "我們使用線圖（Line plot）用作探索數學函式外觀之圖形，藉著圖形可以一目瞭然函式在某個區間的外觀，遠比在腦海中想像公式還要具體。呼叫 `ax.plot()` 依序放置輸入數學函式的數列、數學函式輸出的數列就能完成繪圖。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "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": [
    "x = np.linspace(-2*np.pi, 2*np.pi)\n",
    "f = np.sin(x)\n",
    "fig = plt.figure()\n",
    "ax = plt.axes()\n",
    "ax.plot(x, f)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 觀察區域海拔高度的需求\n",
    "\n",
    "我們使用等高線圖（Contour plot）或填滿等高線圖（Contour-filled plot）用作區域海拔高度變化之圖形。呼叫 `ax.contour()` 或 `ax.contourf()` 依序放置經度、緯度與海拔高度的二維陣列就能完成繪圖。\n",
    "\n",
    "在機器學習的視覺化中常會以模型係數做為經緯度，以誤差函式做為海拔高度，藉此觀察不同係數組合對應的誤差函式大小。在以下的式子中讀者可以將 $X$ 與 $Y$ 想像是模型係數，$Z$ 是每組係數組合所對應的誤差。\n",
    "\n",
    "\\begin{equation}\n",
    "Z = 2 (e^{-X^{2} - Y^{2}} - e^{-(X-1)^2 - (Y-1)^2})\n",
    "\\end{equation}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "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": [
    "x = np.linspace(-3.0, 3.0)\n",
    "y = np.linspace(-2.0, 2.0)\n",
    "X, Y = np.meshgrid(x, y)\n",
    "Z1 = np.exp(-X**2 - Y**2)\n",
    "Z2 = np.exp(-(X - 1)**2 - (Y - 1)**2)\n",
    "Z = (Z1 - Z2) * 2\n",
    "fig = plt.figure()\n",
    "ax = plt.axes()\n",
    "ax.contour(X, Y, Z, cmap='RdBu')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "填滿等高線圖（Contour-filled plot）在機器學習的視覺化中另外用途是決策邊界（Decision boundary）的呈現，常搭配散佈圖來描述資料特徵與類別預測之間的關聯。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "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": [
    "fig = plt.figure()\n",
    "ax = plt.axes()\n",
    "ax.contourf(X, Y, Z, alpha=0.5, cmap='RdBu')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 顯示二維數值陣列的需求\n",
    "\n",
    "一張解析度為 $w \\times h$ 的灰階圖片可以用一個外觀 `(w, h)` 的二維數值陣列來表示圖片中 $w \\times h$ 個點的像素（Pixel）強度，而呼叫 `ax.imshow()` 可以將其顯示成為圖片，以著名的手寫數字資料集 [MNIST](http://yann.lecun.com/exdb/mnist/) 為例，每張手寫數字圖片都是 `(28, 28)` 外觀的二維數值陣列、共 784 個像素。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(28, 28)"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(X_train, y_train), (X_test, y_test) = datasets.mnist.load_data(path=\"mnist.npz\")\n",
    "first_picture = X_train[0, :, :]\n",
    "first_picture.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "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": [
    "fig = plt.figure()\n",
    "ax = plt.axes()\n",
    "ax.imshow(first_picture, cmap='Greys')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 如何為圖形添加元素\n",
    "\n",
    "有時我們會需要標題、軸標籤或者其他元素，來讓讀者能更快理解圖形的內涵。常用的圖形元素有：\n",
    "\n",
    "- 標題\n",
    "- 軸標籤\n",
    "- 刻度標籤\n",
    "- 文字\n",
    "- 圖例或數值說明"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "利用軸物件的 `set_title()` 方法可以加入標題。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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unaQLgYfyehdExLrmNd/MzHrSY9BHxC8B9bBMAGfWqZsNzN6q1pmZWZ/5N2PNzArnoDczK5yD3syscA56M7PCOejNzArnoDczK5yD3syscA56M7PCOejNzArnoDczK5yD3syscA56M7PCOejNzArnoDczK5yD3syscA56M7PCOejNzArnoDczK1yP/0pQ0mzgOGBNRHygi/qvACdXtvdHwPj8/2JXABuAzcCmiGhrVsPNzKwxjZzRzwFm1KuMiP8dEVMjYipwLvD/av4B+BG53iFvZtYCPQZ9RNwLrOtpuewkYF6fWmRmZk3VtDF6SSNJZ/7XV4oDuFPSQkkzm/VcZmbWuB7H6Hvhk8C/1wzbTIuIDkm7AQskPZ6/IbxD/iCYCTBkp/FNbJaZ2btbM++6OZGaYZuI6Mg/1wDzgUPqrRwRsyKiLSLahowc08RmmZm9uzUl6CWNAT4C/LRSNkrS6M5p4ChgSTOez8zMGtfI7ZXzgOnAOEmrgPOBYQARcUVe7M+AOyPi1cqquwPzJXU+z9yIuL15TTczs0b0GPQRcVIDy8wh3YZZLXsamLK1DTMzs+bwb8aamRXOQW9mVjgHvZlZ4Rz0ZmaFc9CbmRXOQW9mVjgHvZlZ4Rz0ZmaFc9CbmRXOQW9mVjgHvZlZ4Zr59+ib5oMTx9B+8bGtboaZWRF8Rm9mVjgHvZlZ4Rz0ZmaFc9CbmRXOQW9mVjgHvZlZ4Rz0ZmaF6zHoJc2WtEbSkjr10yWtl7QoP86r1M2Q9ISk5ZLOaWbDzcysMY2c0c8BZvSwzL9FxNT8uABA0hDgO8DRwP7ASZL270tjzcys93oM+oi4F1i3Fds+BFgeEU9HxBvANcDxW7EdMzPrg2aN0R8u6RFJt0k6IJdNBFZWllmVy7okaaakdknta9eubVKzzMysGUH/MLB3REwB/hm4cWs2EhGzIqItItrGjx/fhGaZmRk0Iegj4uWIeCVP3woMkzQO6AD2rCw6KZeZmdk21Oegl/QeScrTh+RtvgA8BOwraR9Jw4ETgZv6+nxmZtY7Pf6ZYknzgOnAOEmrgPOBYQARcQXwKeDzkjYBrwEnRkQAmyR9AbgDGALMjojH+qUXZmZWl1ImDyxtbW3R3t7e6maYmQ0akhZGRFtXdf7NWDOzwjnozcwK56A3Myucg97MrHAOejOzwvV4e2UrLO5Yz+Rzbml1M8zMtpkVFx/bb9v2Gb2ZWeEc9GZmhXPQm5kVzkFvZlY4B72ZWeEc9GZmhXPQm5kVzkFvZlY4B72ZWeEc9GZmhXPQm5kVzkFvZla4HoNe0mxJayQtqVN/sqRHJS2WdJ+kKZW6Fbl8kST/b0AzsxZo5Ix+DjCjm/pngI9ExAeBC4FZNfVHRMTUev/L0MzM+lePf6Y4Iu6VNLmb+vsqsw8Ak/reLDMza5Zmj9GfDtxWmQ/gTkkLJc1s8nOZmVkDmvaPRyQdQQr6aZXiaRHRIWk3YIGkxyPi3jrrzwRmAgzZaXyzmmVm9q7XlDN6SQcC3weOj4gXOssjoiP/XAPMBw6pt42ImBURbRHRNmTkmGY0y8zMaELQS9oLuAE4JSKerJSPkjS6cxo4Cujyzh0zM+s/PQ7dSJoHTAfGSVoFnA8MA4iIK4DzgF2B70oC2JTvsNkdmJ/LhgJzI+L2fuiDmZl1o5G7bk7qof4M4Iwuyp8GprxzDTMz25b8m7FmZoVz0JuZFc5Bb2ZWOAe9mVnhHPRmZoVz0JuZFc5Bb2ZWOAe9mVnhHPRmZoVz0JuZFc5Bb2ZWOAe9mVnhHPRmZoVz0JuZFc5Bb2ZWOAe9mVnhHPRmZoVz0JuZFc5Bb2ZWuIaCXtJsSWskLalTL0mXSVou6VFJH6rUnSbpqfw4rVkNNzOzxjR6Rj8HmNFN/dHAvvkxE7gcQNIuwPnAocAhwPmSxm5tY83MrPcaCvqIuBdY180ixwM/iuQBYGdJE4BPAAsiYl1EvAgsoPsPDDMza7JmjdFPBFZW5lflsnrl7yBppqR2Se2bN65vUrPMzGzAXIyNiFkR0RYRbUNGjml1c8zMitGsoO8A9qzMT8pl9crNzGwbaVbQ3wScmu++OQxYHxGrgTuAoySNzRdhj8plZma2jQxtZCFJ84DpwDhJq0h30gwDiIgrgFuBY4DlwEbgs7lunaQLgYfypi6IiO4u6pqZWZM1FPQRcVIP9QGcWaduNjC7900zM7NmGDAXY83MrH846M3MCuegNzMrnIPezKxwDnozs8I56M3MCuegNzMrnIPezKxwDnozs8I56M3MCuegNzMrnIPezKxwDnozs8I56M3MCuegNzMrnIPezKxwDnozs8I56M3MCtdQ0EuaIekJScslndNF/bckLcqPJyW9VKnbXKm7qZmNNzOznvX4P2MlDQG+AxwJrAIeknRTRCztXCYivlxZ/m+AgyqbeC0ipjavyWZm1huNnNEfAiyPiKcj4g3gGuD4bpY/CZjXjMaZmVnfNRL0E4GVlflVuewdJO0N7APcVSneXlK7pAcknVDvSSTNzMu1b964voFmmZlZI3ocuumlE4HrImJzpWzviOiQ9F7gLkmLI+I3tStGxCxgFsCICftGk9tlZvau1cgZfQewZ2V+Ui7ryonUDNtEREf++TRwD1uO35uZWT9rJOgfAvaVtI+k4aQwf8fdM5LeD4wF7q+UjZU0Ik+PAz4MLK1d18zM+k+PQzcRsUnSF4A7gCHA7Ih4TNIFQHtEdIb+icA1EVEddvkj4EpJb5E+VC6u3q1jZmb9r6Ex+oi4Fbi1puy8mvmvd7HefcAH+9A+MzPrI/9mrJlZ4Rz0ZmaFc9CbmRXOQW9mVjgHvZlZ4Rz0ZmaFc9CbmRXOQW9mVjgHvZlZ4Rz0ZmaFc9CbmRWu2X+Pvik+OHEM7Rcf2+pmmJkVwWf0ZmaFc9CbmRXOQW9mVjgHvZlZ4Rz0ZmaFc9CbmRXOQW9mVjgHvZlZ4Rz0ZmaFU0S0ug3vIGkD8ESr29EPxgHPt7oR/cD9Glzcr8Gl0X7tHRHju6oYkH8CAXgiItpa3Yhmk9Tufg0e7tfg4n7V56EbM7PCOejNzAo3UIN+Vqsb0E/cr8HF/Rpc3K86BuTFWDMza56BekZvZmZN4qA3MyvcgAp6STMkPSFpuaRzWt2erSVptqQ1kpZUynaRtEDSU/nn2Fa2cWtI2lPS3ZKWSnpM0pdy+aDum6TtJf1K0iO5X/+Yy/eR9GA+Hq+VNLzVbd0akoZI+rWkm/P8oO+XpBWSFktaJKk9lw3q4xBA0s6SrpP0uKRlkg5vRr8GTNBLGgJ8Bzga2B84SdL+rW3VVpsDzKgpOwf4RUTsC/wizw82m4CzI2J/4DDgzPwaDfa+vQ58NCKmAFOBGZIOAy4BvhUR7wNeBE5vYRv74kvAssp8Kf06IiKmVu4xH+zHIcC3gdsj4v3AFNLr1vd+RcSAeACHA3dU5s8Fzm11u/rQn8nAksr8E8CEPD2B9EthLW9nH/v4U+DIkvoGjAQeBg4l/Tbi0Fy+xfE5WB7ApBwOHwVuBlRIv1YA42rKBvVxCIwBniHfJNPMfg2YM3pgIrCyMr8ql5Vi94hYnaefA3ZvZWP6StJk4CDgQQroWx7eWASsARYAvwFeiohNeZHBejxeCvwd8Fae35Uy+hXAnZIWSpqZywb7cbgPsBb4QR5q+76kUTShXwMp6N81In00D9r7WiXtCFwPnBURL1frBmvfImJzREwlnQEfAry/xU3qM0nHAWsiYmGr29IPpkXEh0hDvWdK+pNq5SA9DocCHwIuj4iDgFepGabZ2n4NpKDvAPaszE/KZaX4naQJAPnnmha3Z6tIGkYK+R9HxA25uIi+AUTES8DdpCGNnSV1/j2owXg8fhj4U0krgGtIwzffZvD3i4joyD/XAPNJH86D/ThcBayKiAfz/HWk4O9zvwZS0D8E7JvvCBgOnAjc1OI2NdNNwGl5+jTS+PagIknAVcCyiPhmpWpQ903SeEk75+kdSNcdlpEC/1N5sUHXr4g4NyImRcRk0vvprog4mUHeL0mjJI3unAaOApYwyI/DiHgOWClpv1z0MWApzehXqy9A1Fx0OAZ4kjQ++vetbk8f+jEPWA28SfqUPp00NvoL4Cng58AurW7nVvRrGulr46PAovw4ZrD3DTgQ+HXu1xLgvFz+XuBXwHLgJ8CIVre1D32cDtxcQr9y+x/Jj8c6s2KwH4e5D1OB9nws3giMbUa//CcQzMwKN5CGbszMrB846M3MCuegNzMrnIPezKxwDnozs8I56M3MCuegNzMr3P8H+Penk6YtoU8AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "np.random.seed(42)\n",
    "random_integers = np.random.randint(1, 100, size=100)\n",
    "n_odds = np.sum(random_integers % 2 == 0)\n",
    "n_evens = np.sum(random_integers % 2 == 1)\n",
    "y = np.array([n_odds, n_evens])\n",
    "x = np.array([1, 2])\n",
    "fig = plt.figure()\n",
    "ax = plt.axes()\n",
    "ax.barh(x, y)\n",
    "ax.set_title(\"Odd/even numbers in 100 random intergers.\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "利用軸物件的 `set_xlabel()` 與 `set_ylabel()` 方法分別加入 X 軸與 Y 軸標籤。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "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": [
    "fig = plt.figure()\n",
    "ax = plt.axes()\n",
    "ax.barh(x, y)\n",
    "ax.set_title(\"Odd/even numbers in 100 random intergers.\")\n",
    "ax.set_xlabel(\"Frequency\")\n",
    "ax.set_ylabel(\"Type\", rotation=0) # 指定 Y 軸標籤的角度\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "利用軸物件的 `set_xticks()`、`set_xticklabels()`、`set_yticks()` 與 `set_yticklabels()` 方法分別加入 X 軸與 Y 軸標籤刻度。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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kadbwSkSS1G2VVyJJtgO+3XrvDCwDrmr9D6mqm9dj2SRJt3OrDJGq+h2wO0CSw4Hrq+pd01AuSdIMsLbNWZsmuTjJhgBJtproT3JKkvcmOSfJBUke0qbZPMnHk5yR5OwkT5zyWkiSxmJtQ+RG4BRg4u7004DPVdUtrX+zqtod+F/Ax9uw1wPfqaqHAH8DvDPJ5utUaknS7ULPjfWPAs9u3c8GjhoZdxxAVX0P2CrJNsC+wGuSnMMQQJsAd+0tsCTp9mOtH/GtqlOTLEiyEJhXVReMjp48ORDg76rqov5iSpJuj3of8f0kcCzLX4UAHASQZB9gaVUtBb4OvCRJ2rg9OtcpSbqd6Q2RTwHb0pqvRvwpydnAvwPPbcPeBmwInJfkR61fkjQLrHFzVlUdPtK7D3BiVV07abJjqurQSfPdCDy/u4SSpNuttb4nkuT9wOOA/ae+OJKkmaTnxvpLVjJ84TqXRpI0o/jbWZKkboaIJKmbISJJ6maISJK6GSKSpG6GiCSpmyEiSepmiEiSuhkikqRua/2N9Zns/CuWsuA1J4+7GJI0rS458oDVT9TJKxFJUjdDRJLUzRCRJHUzRCRJ3QwRSVI3Q0SS1M0QkSR1M0QkSd0MEUlSN0NEktTNEJEkdTNEJEndDBFJUjdDRJLUzRCRJHUzRCRJ3QwRSVI3Q0SS1M0QkSR1M0QkSd0MEUlSN0NEktTNEJEkdTNEJEndDBFJUjdDRJLUzRCRJHUzRCRJ3QwRSVI3Q0SS1M0QkSR1M0QkSd0MEUlSN0NEktTNEJEkdTNEJEndDBFJUjdDRJLUzRCRJHUzRCRJ3aY9RJLskuSLSX6W5BdJ3ptkoxVMd0qSB69g+MFJPjA9pZUkrcq0hkiSAJ8DvlBVuwL3ArYA3j6d5ZAkTY3pvhJ5JPCnqjoKoKqWAS8DnpNk8yTHJ7kwyeeBTSdmSvLsJD9NcgbwsJHhT0lyQZJzk3xvmusiSXPe/Gle3/2AxaMDquoPSX4FHAbcUFV/mWQ34CyAJDsCbwEeBCwFvguc3WZ/E/DYqroiyTYrWmGSQ4BDAOZttcPU10iS5rDb0431hcAxAFV1HnBeG74XcEpVXVVVNwMnjMxzKnB0kucB81a00Kr6cFU9uKoePG+zrddb4SVpLpruEPkxwxXFf0uyFXBX4Na1XVhVvQB4A3AXYHGS7aaikJKkNTPdIfJtYLMkzwRIMg/4Z+Bo4GvA37fh9wd2a/OcDjwiyXZJNgSeMrGwJPeoqtOr6k3AVQxhIkmaJtN6T6SqKsnfAv+W5I0MIfYV4HWt+6gkFwIX0u6dVNWSJIcDpwHXAueMLPKdSXYFwhBQ505XXSRJ039jnaq6DHjCSkY/bSXzHAUctYLhT5rCokmS1tLt6ca6JGmGMUQkSd0MEUlSN0NEktTNEJEkdTNEJEndDBFJUjdDRJLUzRCRJHUzRCRJ3QwRSVI3Q0SS1M0QkSR1M0QkSd0MEUlSN0NEktTNEJEkdTNEJEndDBFJUjdDRJLUzRCRJHUzRCRJ3QwRSVI3Q0SS1M0QkSR1M0QkSd0MEUlSN0NEktTNEJEkdTNEJEndDBFJUjdDRJLUzRCRJHUzRCRJ3QwRSVI3Q0SS1M0QkSR1M0QkSd3mj7sA0+mvdt6aRUceMO5iSNKs4ZWIJKmbISJJ6maISJK6GSKSpG6GiCSpmyEiSepmiEiSuhkikqRuhogkqVuqatxlmDZJrgMuGnc51oPtgavHXYj1ZLbWzXrNLHO9Xnerqh1WNGJO/ewJcFFVPXjchZhqSRbNxnrB7K2b9ZpZrNfK2ZwlSepmiEiSus21EPnwuAuwnszWesHsrZv1mlms10rMqRvrkqSpNdeuRCRJU8gQkSR1mzMhkmS/JBcl+XmS14y7PL2SfDzJlUkuGBl2hyTfTPKz9nfbcZaxR5K7JPlukh8n+VGSf2rDZ3TdkmyS5Iwk57Z6vaUNv3uS09v+eEKSjcZd1h5J5iU5O8lJrX/G1yvJJUnOT3JOkkVt2IzeDwGSbJPkxCQ/SXJhkodORb3mRIgkmQf8K/A44L7A05Pcd7yl6nY0sN+kYa8Bvl1VuwLfbv0zza3AYVV1X2Bv4EXtPZrpdbsJeGRVPQDYHdgvyd7AO4B/qap7Ar8HnjvGMq6LfwIuHOmfLfX6m6rafeQ7FDN9PwR4L/C1qroP8ACG923d61VVs/4FPBT4+kj/a4HXjrtc61CfBcAFI/0XATu27h0ZvlQ59nKuYx2/CDxmNtUN2Aw4C9iL4VvC89vw5fbPmfICdmkHnkcCJwGZJfW6BNh+0rAZvR8CWwMX0x6mmsp6zYkrEWBn4LKR/svbsNniTlW1pHX/BrjTOAuzrpIsAPYATmcW1K01+ZwDXAl8E/gFcG1V3domman743uAVwF/bv3bMTvqVcA3kixOckgbNtP3w7sDVwFHtebHjybZnCmo11wJkTmjhlOKGfvcdpItgM8Ch1bVH0bHzdS6VdWyqtqd4cz9IcB9xlykdZbk8cCVVbV43GVZD/apqgcyNH+/KMlfj46cofvhfOCBwAerag/gj0xquuqt11wJkSuAu4z079KGzRa/TbIjQPt75ZjL0yXJhgwB8qmq+lwbPCvqBlBV1wLfZWjm2SbJxG/XzcT98WHA/0hyCXA8Q5PWe5n59aKqrmh/rwQ+zxD8M30/vBy4vKpOb/0nMoTKOtdrroTImcCu7cmRjYCnAV8ac5mm0peAZ7XuZzHcT5hRkgT4GHBhVb17ZNSMrluSHZJs07o3ZbjPcyFDmDy5TTbj6lVVr62qXapqAcPn6TtV9QxmeL2SbJ5ky4luYF/gAmb4flhVvwEuS3LvNuhRwI+ZgnrNmW+sJ9mfoQ13HvDxqnr7mIvUJclxwEKGn3D+LfBm4AvAp4G7ApcCT62qa8ZVxh5J9gH+Ezif29rYX8dwX2TG1i3JbsAnGPa7DYBPV9Vbk/wFwxn8HYCzgX+oqpvGV9J+SRYCr6iqx8/0erXyf771zgeOraq3J9mOGbwfAiTZHfgosBHwS+DZtH2SdajXnAkRSdLUmyvNWZKk9cAQkSR1M0QkSd0MEUlSN0NEktRt/uonkZRkGcPjxxMOrKpLxlQc6XbDR3ylNZDk+qraYiXjwvBZ+vOKxkuzmc1ZUockC9r/p/kkwzea75LklUnOTHLexP8NadO+PslPk3w/yXFJXtGGn5Lkwa17+/YTIhM/2PjOkWU9vw1f2OaZ+J8Qn2oBRpI9k/yg/d+SM5JsmeR77QtmE+X4fpIHTNtG0pxgc5a0ZjZtv8QLw09qvwzYFXhWVf0wyb6t/yEMP4n+pfbDfX9k+FmQ3Rk+b2cBq/vRwucCS6tqzyQbA6cm+UYbtwdwP+DXwKnAw5KcAZwAHFRVZybZCriR4WdkDgYOTXIvYJOqOnddN4Q0yhCR1syN7Zd4gf/+ufpLq+qHbdC+7XV269+CIVS2BD5fVTe0+dbkN9v2BXZLMvEbVFu3Zd0MnFFVl7dlncPwv2WWAkuq6kyAiV8/TvIZ4I1JXgk8h+EfmklTyhCR+v1xpDvA/6mqD41OkOTQVcx/K7c1KW8yaVkvqaqvT1rWQob/lDhhGav4DFfVDUm+CTwReCrwoFWUReriPRFpanwdeE77fygk2TnJHYHvAQcm2bT9OuwTRua5hNsO7E+etKwXtp/GJ8m92i/KrsxFwI5J9mzTbznyc+wfBd4HnFlVv1+nGkor4JWINAWq6htJ/hI4rd3rvp7hF2zPSnICcC7D/2o4c2S2dwGfbv897+SR4R9laKY6q904vwo4cBXrvjnJQcD728/N3wg8Gri+qhYn+QNw1BRVVVqOj/hK0yjJ4QwH93dN0/p2Ak4B7uMjyFofbM6SZqkkz2T4fyyvN0C0vnglIknq5pWIJKmbISJJ6maISJK6GSKSpG6GiCSp238Bxr7kUPOWd7EAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure()\n",
    "ax = plt.axes()\n",
    "ax.barh(x, y)\n",
    "ax.set_title(\"Odd/even numbers in 100 random intergers.\")\n",
    "ax.set_xlabel(\"Frequency\")\n",
    "ax.set_ylabel(\"Type\", rotation=0) # 指定 Y 軸標籤的角度\n",
    "ax.set_yticks([1, 2])\n",
    "ax.set_yticklabels(['Odds', 'Evens'])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "利用軸物件的 `set_xlim()` 與 `set_ylim()` 方法調整圖形的 X 軸和 Y 軸的上下限。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "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": [
    "fig = plt.figure()\n",
    "ax = plt.axes()\n",
    "ax.barh(x, y)\n",
    "ax.set_title(\"Odd/even numbers in 100 random intergers.\")\n",
    "ax.set_xlabel(\"Frequency\")\n",
    "ax.set_ylabel(\"Type\", rotation=0) # 指定 Y 軸標籤的角度\n",
    "ax.set_yticks([1, 2])\n",
    "ax.set_yticklabels(['Odds', 'Evens'])\n",
    "ax.set_xlim(0, 65)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "利用軸物件的 `text()` 方法為圖形添加文字說明。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "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": [
    "fig = plt.figure()\n",
    "ax = plt.axes()\n",
    "ax.barh(x, y)\n",
    "ax.set_title(\"Odd/even numbers in 100 random intergers.\")\n",
    "ax.set_xlabel(\"Frequency\")\n",
    "ax.set_ylabel(\"Type\", rotation=0) # 指定 Y 軸標籤的角度\n",
    "ax.set_yticks([1, 2])\n",
    "ax.set_yticklabels(['Odds', 'Evens'])\n",
    "ax.set_xlim(0, 65)\n",
    "for xi, yi in zip(x, y):\n",
    "    ax.text(yi + 1, xi - 0.05, \"{}\".format(yi))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "圖例能夠以軸物件的 `legend()` 方法搭配作圖時的 `label` 參數創建。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "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": [
    "x = np.linspace(-2*np.pi, 2*np.pi)\n",
    "f = np.sin(x)\n",
    "g = np.cos(x)\n",
    "fig = plt.figure()\n",
    "ax = plt.axes()\n",
    "ax.plot(x, f, label=\"sin(x)\")\n",
    "ax.plot(x, g, label=\"cos(x)\")\n",
    "ax.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "特定圖形例如等高線圖（Contour plot）可以在等高線上加入海拔高度數值說明數字；填滿等高線圖（Contour-filled plot）可以加入海拔高度數值說明色條。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "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": [
    "x = np.linspace(-3.0, 3.0)\n",
    "y = np.linspace(-2.0, 2.0)\n",
    "X, Y = np.meshgrid(x, y)\n",
    "Z1 = np.exp(-X**2 - Y**2)\n",
    "Z2 = np.exp(-(X - 1)**2 - (Y - 1)**2)\n",
    "Z = (Z1 - Z2) * 2\n",
    "fig = plt.figure()\n",
    "ax = plt.axes()\n",
    "CS = ax.contour(X, Y, Z, cmap='RdBu')\n",
    "ax.clabel(CS, inline=1, fontsize=10)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure()\n",
    "ax = plt.axes()\n",
    "CS = ax.contourf(X, Y, Z, alpha=0.5, cmap='RdBu')\n",
    "fig.colorbar(CS)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 如何在圖形中加入中文字\n",
    "\n",
    "中文的使用者在為圖形添加元素時會碰上中文無法顯示生成為空格的問題，這是因為 matplotlib 預設的字體不支援中文，解決方式是另外指定支援中文的字體，例如 macOS 常用的細黑體（STHeiti Light.ttc）或者 Windows 常用的微軟正黑體（msjh.ttf），透過 `matplotlib.font_manager` 所提供的 `FontProperties()` 函式傳入能夠支援繁體中文字體的檔案路徑。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "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": [
    "font_path = \"/System/Library/Fonts/STHeiti Light.ttc\" # Windows 使用者可以指定 C:/Windows/Fonts/msjh.ttf\n",
    "font_path = \"STHeiti Light.ttc\" # 如果在雲端主機執行 Jupyter Notebook 需要置放支援中文的字體於工作目錄\n",
    "tc_font = FontProperties(fname=font_path)\n",
    "np.random.seed(42)\n",
    "random_integers = np.random.randint(1, 100, size=100)\n",
    "n_odds = np.sum(random_integers % 2 == 0)\n",
    "n_evens = np.sum(random_integers % 2 == 1)\n",
    "y = np.array([n_odds, n_evens])\n",
    "x = np.array([1, 2])\n",
    "fig = plt.figure()\n",
    "ax = plt.axes()\n",
    "ax.barh(x, y)\n",
    "ax.set_title(\"在 100 個隨機整數中的奇偶數\", fontproperties=tc_font)\n",
    "ax.set_xlabel(\"頻率\", fontproperties=tc_font)\n",
    "ax.set_ylabel(\"種類\", rotation=0, fontproperties=tc_font) # 指定 Y 軸標籤的角度\n",
    "ax.set_yticks([1, 2])\n",
    "ax.set_yticklabels(['奇數', '偶數'], fontproperties=tc_font)\n",
    "ax.set_xlim(0, 65)\n",
    "for xi, yi in zip(x, y):\n",
    "    ax.text(yi + 1, xi - 0.05, \"{}\".format(yi))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 如何繪製子圖\n",
    "\n",
    "子圖（subplots）是資料科學經常採用的技巧，藉由將視覺化在同一個畫布上並列呈現，進而輕鬆地比較組別之間的差別，這時可以改用 `plt.subplots(m, n)` 同時創建畫布物件和 $m \\times n$ 個軸物件，多個軸物件會擺置在一個外觀 `(m, n)` 的 `ndarray`。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'numpy.ndarray'>\n",
      "(3, 5)\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 15 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axes = plt.subplots(3, 5)\n",
    "print(type(axes))\n",
    "print(axes.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "接著就能夠像索引數值陣列中的數值一般，運用 `[m, n]` 的語法對位於 `[m, n]` 位置的軸物件呼叫作圖方法或者添加元素。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "class ImshowSubplots:\n",
    "    \"\"\"\n",
    "    This class plots 2d-arrays with subplots.\n",
    "    Args:\n",
    "        rows (int): The number of rows of axes.\n",
    "        cols (int): The number of columns of axes.\n",
    "        fig_size (tuple): Figure size.\n",
    "    \"\"\"\n",
    "    def __init__(self, rows, cols, fig_size):\n",
    "        self._rows = rows\n",
    "        self._cols = cols\n",
    "        self._fig_size = fig_size\n",
    "    def im_show(self, X, y, label_dict=None):\n",
    "        \"\"\"\n",
    "        This function plots 2d-arrays with subplots.\n",
    "        Args:\n",
    "            X (ndarray): 2d-arrays.\n",
    "            y (ndarray): Labels for 2d-arrays.\n",
    "            label_dict (dict): Str labels for y if any.\n",
    "        \"\"\"\n",
    "        n_pics = self._rows*self._cols\n",
    "        first_n_pics = X[:n_pics, :, :]\n",
    "        first_n_labels = y[:n_pics]\n",
    "        fig, axes = plt.subplots(self._rows, self._cols, figsize=self._fig_size)\n",
    "        for i in range(n_pics):\n",
    "            row_idx = i % self._rows\n",
    "            col_idx = i // self._rows\n",
    "            axes[row_idx, col_idx].imshow(first_n_pics[i], cmap=\"Greys\")\n",
    "            if label_dict is not None:\n",
    "                axes[row_idx, col_idx].set_title(\"Label: {}\".format(label_dict(first_n_labels[i])))\n",
    "            else:\n",
    "                axes[row_idx, col_idx].set_title(\"Label: {}\".format(first_n_labels[i]))\n",
    "            axes[row_idx, col_idx].set_xticks([])\n",
    "            axes[row_idx, col_idx].set_yticks([])\n",
    "        plt.tight_layout()\n",
    "        plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x432 with 15 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "iss = ImshowSubplots(3, 5, (8, 6))\n",
    "iss.im_show(X_train, y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "運用子圖顯示二維數值陣列之後還會用到，因此我們定義 `ImshowSubplots` 類別並封裝到 `pyvizml` 模組之中，後續如果還要使用它，就可以用 `from MODULE import CLASS` 的指令載入。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 延伸閱讀\n",
    "\n",
    "1. Tutorials - Matplotlib (https://matplotlib.org/tutorials/index.html) \n",
    "2. Visualization with Matplotlib. In: Jake VanderPlas, Python Data Science Handbook (https://jakevdp.github.io/PythonDataScienceHandbook/04.00-introduction-to-matplotlib.html) \n",
    "3. The MNIST database of handwritten digits (http://yann.lecun.com/exdb/mnist/)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python Machine Learning",
   "language": "python",
   "name": "pyml"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}