4-4 Matplotlib 長條圖、直方圖、圓餅圖.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "fa6c88e4",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 匯入套件(模組)\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5274cdef",
   "metadata": {},
   "source": [
    "![直方圖（Histogram）與長條圖（Bar chart）之差異](https://miro.medium.com/v2/resize:fit:1400/0*OSp7hkXwZAOMAZZ3)\n",
    "\n",
    "參考資料:\n",
    "- [Python 學習筆記 : Matplotlib 資料視覺化 (二) 統計圖](http://yhhuang1966.blogspot.com/2020/05/python-matplotlib.html)\n",
    "- [如何分辨長相近似的孿生兄弟-直方圖（Histogram）與長條圖（Bar chart）之差異](https://medium.com/marketingdatascience/如何分辨長相近似的孿生兄弟-直方圖-histogram-與長條圖-bar-chart-之差異-154602ac0ba6)\n",
    "- [Examples](https://matplotlib.org/stable/gallery/)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "346567c4",
   "metadata": {},
   "source": [
    "# 長條圖 bar()\n",
    "- 通常用於「類別型」資料 (categorical data)\n",
    "  - 資料之間是離散的 (discrete)、間斷的\n",
    "- plt.bar(x, height, width=0.8, ...)\n",
    "  - `x`: array-like 的資料，例如 list、array、tuple 等，作為 x 軸的標題\n",
    "  - `height`: array-like 的資料，作為 y 軸的值\n",
    "  - `width`: bar 的寬度\n",
    "- matplotlib.pyplot.xticks(ticks=None, labels=None, ...)\n",
    "  - `ticks`: x 軸的刻度\n",
    "  - `label`: 對應 x 軸的標題，類似取代 x 軸的刻度，用其它名稱代替\n",
    "- 參考連結:\n",
    "  - [matplotlib.pyplot.bar](https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.bar.html)\n",
    "  - [matplotlib.pyplot.xticks](https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.xticks.html)\n",
    "  - [Python matplotlib 畫圖 – 柱狀圖 (長條圖)](https://python-ecw.com/2020/11/12/matplotlib柱狀圖/)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "f882f086",
   "metadata": {},
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "'''\n",
    "班上投票，Alex 得票 7749，Bill 得票 9527，Carl 得票 2266，請用長條圖表示\n",
    "'''\n",
    "\n",
    "# x 軸文字的座標\n",
    "x = [1, 2, 3]\n",
    "\n",
    "# 得票數 (因為有 3 名候選人)\n",
    "height = [7749, 9527, 2266]\n",
    "candidates = ['Alex', 'Bill', 'Carl']\n",
    "\n",
    "# bar 的寬度\n",
    "width = 0.5\n",
    "\n",
    "# 繪製長條圖\n",
    "plt.bar(\n",
    "    x, \n",
    "    height, \n",
    "    width, \n",
    "    edgecolor='black', \n",
    "    linewidth=1, \n",
    "    color=['yellow', 'green', 'red']\n",
    ")\n",
    "\n",
    "# 設定數值文字在圖表上\n",
    "for index in range(len(x)):\n",
    "    # matplotlib.pyplot.text(圖表的x座標, 圖表的y座標, 顯示的文字)\n",
    "    plt.text(\n",
    "        x[index], \n",
    "        height[index], \n",
    "        height[index])\n",
    "\n",
    "# 設定圖表標題\n",
    "plt.title('Election')\n",
    "plt.ylabel('Votes')\n",
    "\n",
    "# 設定 x 和 y 軸的刻度\n",
    "plt.xticks(x, candidates)\n",
    "plt.yticks(np.arange(0, 10000, step=1000))\n",
    "\n",
    "# 顯示圖表\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ffab1dad",
   "metadata": {},
   "source": [
    "# 直方圖\n",
    "- 通常用於「數值型」資料 (numeric data)\n",
    "  - 資料之間是連續的 (continueous)\n",
    "- plt.hist(x, bins, ...)\n",
    "  - x: 你的資料，list、array 或是 sequece\n",
    "  - bin: 組距，預設 10\n",
    "- 參考資料:\n",
    "  - [matplotlib.pyplot.hist](https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.hist.html)\n",
    "  - [Python 學習筆記 : Matplotlib 資料視覺化 (二) 統計圖](http://yhhuang1966.blogspot.com/2020/05/python-matplotlib.html)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "b8f0ffe9",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "'''\n",
    "請以班上 100 個學生考試成績，最低 0 分，最高 100 分，繪製直方圖\n",
    "'''\n",
    "\n",
    "# 隨機產生 100 位同學的成績 (用 np.random.randint 的話，要轉成 list)\n",
    "x = list(np.random.randint(0, 101, size=100))\n",
    "\n",
    "# 組距 (將成績 0 到 100 分，組距為 10，回傳 11 個數字)\n",
    "bins = np.arange(0, 101, 10)\n",
    "\n",
    "# 繪製直方圖 (會回傳每個組距的數量，以及對應組距)\n",
    "arr_counts, arr_bins, obj = plt.hist(\n",
    "    x, \n",
    "    bins,\n",
    "    edgecolor='black',\n",
    "    linewidth=1\n",
    ")\n",
    "\n",
    "# 設定數值文字在圖表上\n",
    "for i in range(len(arr_counts)):\n",
    "    plt.text(\n",
    "        arr_bins[i], # x 為 0 ~ 90 的座標上寫字\n",
    "        arr_counts[i], \n",
    "        arr_counts[i].astype('int32')\n",
    "    )\n",
    "\n",
    "# 設定標題\n",
    "plt.title('Students Exam')\n",
    "plt.xticks(bins)\n",
    "plt.ylabel('Scores')\n",
    "\n",
    "# 顯示圖表\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "115ca6ba",
   "metadata": {},
   "source": [
    "# 圓餅圖 pie()\n",
    "- 用於描述「類別型」資料\n",
    "- plt.pie(x, explode=None, labels=None, colors=None, autopct=None,shadow=False, labeldistance=1.1, radius=1, counterclock=True, center=(0, 0), ...)\n",
    "  - x: 你的資料。\n",
    "  - explode: 餅分離的程度，list 格式，其中的每個元素值介於 0 到 1 之間。\n",
    "  - labels: 圓餅圖的標題，list 格式。\n",
    "  - autopct: 每一個項目的百分比格式，例如「%2.2f%%」，代表整數 2 位數，小數 2 位數。\n",
    "  - labeldistance: 項目標題與圓餅圖中間距離是半徑的多少倍。\n",
    "  - center: 圓中心座標，預設 (0, 0)。\n",
    "  - shadow: 圓餅圖是否有陰影，預設為 False。\n",
    "- 參考資料:\n",
    "  - [matplotlib.pyplot.pie](https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.pie.html#matplotlib.pyplot.pie)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "75104129",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "'''\n",
    "有一家庭支出費用如下，以此設計圓餅圖:\n",
    "旅行 (Travel): 8000\n",
    "娛樂 (Entertainment): 2000\n",
    "教育 (Education): 3000\n",
    "交通 (Transportation): 5000\n",
    "餐費 (Food): 6000\n",
    "'''\n",
    "\n",
    "# 設定支出項目\n",
    "labels = [\n",
    "    'Travel', \n",
    "    'Entertainment', \n",
    "    'Education', \n",
    "    'Transportation', \n",
    "    'Food'\n",
    "]\n",
    "\n",
    "# 設定支出費用\n",
    "x = [\n",
    "    8000, \n",
    "    2000, \n",
    "    3000, \n",
    "    5000, \n",
    "    6000\n",
    "]\n",
    "\n",
    "# 繪製圓餅圖\n",
    "plt.pie(\n",
    "    x,\n",
    "    explode=(0, 0.2, 0, 0, 0),\n",
    "    labels=labels,\n",
    "    autopct=\"%.2f%%\"\n",
    ")\n",
    "\n",
    "# 顯示圖表\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "58112661",
   "metadata": {},
   "outputs": [],
   "source": [
    "'''\n",
    "補充: 確認電腦內部字型\n",
    "'''\n",
    "from matplotlib.font_manager import fontManager\n",
    "for i in sorted(fontManager.get_font_names()):\n",
    "    print(i)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "771d8e3e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "'''\n",
    "承上，將標題改成中文來顯示\n",
    "'''\n",
    "\n",
    "# 設定字型\n",
    "import matplotlib\n",
    "matplotlib.rc('font', family='Microsoft JhengHei')\n",
    "\n",
    "# 設定支出項目\n",
    "labels = [\n",
    "    '旅行', \n",
    "    '娛樂', \n",
    "    '教育', \n",
    "    '交通', \n",
    "    '餐費'\n",
    "]\n",
    "\n",
    "# 設定支出費用\n",
    "x = [\n",
    "    8000, \n",
    "    2000, \n",
    "    3000, \n",
    "    5000, \n",
    "    6000\n",
    "]\n",
    "\n",
    "# 繪製圓餅圖\n",
    "plt.pie(\n",
    "    x,\n",
    "    explode=(0,0.2,0,0,0),\n",
    "    labels=labels,\n",
    "    autopct=\"%.2f%%\"\n",
    ")\n",
    "\n",
    "# 顯示圖表\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "153025e6",
   "metadata": {},
   "source": [
    "# 其它範例"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "64298bfb",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "'''Stacked Bar chart'''\n",
    "\n",
    "# 匯入套件\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "\n",
    "# 設定字型 (optional)\n",
    "import matplotlib\n",
    "matplotlib.rc('font', family='Microsoft JhengHei')\n",
    "\n",
    "# 建立資料\n",
    "'''\n",
    "y 用 np.array() 來產生數據，\n",
    "是因為 plt.bar() 當中的 bottom 參數，\n",
    "可以透過與上一個陣列相加來賦值\n",
    "'''\n",
    "x = ['A', 'B', 'C', 'D']\n",
    "h1 = np.array([10, 20, 10, 30])\n",
    "h2 = np.array([20, 25, 15, 25])\n",
    "h3 = np.array([12, 15, 19, 6])\n",
    "h4 = np.array([10, 29, 13, 19])\n",
    " \n",
    "# 繪製 stacked bar\n",
    "# (重點在 bottom 屬性; h1 和 h2 h3 可以相加，是因為 numpy)\n",
    "plt.bar(x, h1, color='r', edgecolor='black', linewidth=1)\n",
    "plt.bar(x, h2, bottom=h1, color='b', edgecolor='black', linewidth=1)\n",
    "plt.bar(x, h3, bottom=h1+h2, color='y', edgecolor='black', linewidth=1)\n",
    "plt.bar(x, h4, bottom=h1+h2+h3, color='g', edgecolor='black', linewidth=1)\n",
    "\n",
    "# 圖表標題\n",
    "plt.xlabel(\"隊伍\")\n",
    "plt.ylabel(\"分數\")\n",
    "plt.legend([\"Round 1\", \"Round 2\", \"Round 3\", \"Round 4\"])\n",
    "plt.title(\"每個隊伍在 4 輪中的分數\")\n",
    "\n",
    "# 顯示圖表\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "8d32f933",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "'''\n",
    "Horizontal Bar chart\n",
    "'''\n",
    "# 匯入套件\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# 設定字型 (optional)\n",
    "import matplotlib\n",
    "matplotlib.rc('font', family='Microsoft JhengHei')\n",
    "\n",
    "# 設定類別項目\n",
    "list_labels = [\n",
    "    'Python', \n",
    "    'Scala', \n",
    "    'C#', \n",
    "    'Java', \n",
    "    'PHP'\n",
    "]\n",
    "\n",
    "# 設定類別項目的 x 軸座標\n",
    "y = [1, 2, 3, 4, 5]\n",
    "\n",
    "# 每個類別項目的值\n",
    "width = [50, 10, 13, 27, 22]\n",
    "\n",
    "# 繪製 Horizontal Bar (y 標籤順序由下往上)\n",
    "plt.barh(\n",
    "    y, \n",
    "    width,\n",
    "    edgecolor='black',\n",
    "    linewidth=1, \n",
    "    tick_label=list_labels, \n",
    "    color=['red', 'green', 'blue', 'yellow', 'purple'])\n",
    "\n",
    "# 設定數值文字在圖表上\n",
    "for i in range(len(x)):\n",
    "    # plt.text() 依然使用 x, y text 的順序\n",
    "    plt.text(width[i], y[i], width[i])\n",
    "\n",
    "# 圖表標題\n",
    "plt.ylabel(\"程式語言\")\n",
    "plt.xlabel(\"使用次數\")\n",
    "\n",
    "# 顯示圖表\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "cc51d389",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "'''\n",
    "Grouped Bar chart\n",
    "'''\n",
    "# 匯入套件\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# 設定字型 (optional)\n",
    "import matplotlib\n",
    "matplotlib.rc('font', family='Microsoft JhengHei')\n",
    "\n",
    "# 設定群組資料的值 (第1隊、第2隊)\n",
    "y_score_1 = [35, 60, 18, 29, 77]\n",
    "y_score_2 = [28, 33, 93, 44, 32]\n",
    "\n",
    "# 設定 x 軸的順序，這裡要注意的是，group 的 bar chart，要另外指定 x 軸的位置\n",
    "BAR_WIDTH = 0.4\n",
    "x_axis_1 = [1, 2, 3, 4, 5]\n",
    "x_axis_2 = [i + BAR_WIDTH for i in x_axis_1]\n",
    "'''\n",
    "x_axis_2 = [\n",
    "    1 + BAR_WIDTH,\n",
    "    2 + BAR_WIDTH,\n",
    "    3 + BAR_WIDTH,\n",
    "    4 + BAR_WIDTH,\n",
    "    5 + BAR_WIDTH\n",
    "]\n",
    "'''\n",
    "\n",
    "# 設定 x 刻度的中心位置\n",
    "ticks = [i + BAR_WIDTH / 2 for i in x_axis_1]\n",
    "'''\n",
    "ticks = [\n",
    "    1 + BAR_WIDTH / 2,\n",
    "    2 + BAR_WIDTH / 2,\n",
    "    3 + BAR_WIDTH / 2,\n",
    "    4 + BAR_WIDTH / 2,\n",
    "    5 + BAR_WIDTH / 2\n",
    "]\n",
    "'''\n",
    "\n",
    "# 設定 x 標題\n",
    "list_labels = [\"Round 1\", \"Round 2\", \"Round 3\", \"Round 4\", \"Round 5\"]\n",
    "\n",
    "# 繪製圖表\n",
    "plt.bar(\n",
    "    x_axis_1, \n",
    "    y_score_1, \n",
    "    BAR_WIDTH, \n",
    "    color=\"y\", \n",
    "    edgecolor='black',\n",
    "    linewidth=1,\n",
    "    label=\"第 1 隊\"\n",
    ")\n",
    "plt.bar(\n",
    "    x_axis_2, \n",
    "    y_score_2, \n",
    "    BAR_WIDTH, \n",
    "    color=\"b\", \n",
    "    edgecolor='black',\n",
    "    linewidth=1,\n",
    "    label=\"第 2 隊\"\n",
    ")\n",
    "\n",
    "# 設定數值文字在圖表上\n",
    "for i in range(len(x_axis_1)):\n",
    "    plt.text(\n",
    "        x_axis_1[i], \n",
    "        y_score_1[i], \n",
    "        y_score_1[i]\n",
    "    )\n",
    "    plt.text(\n",
    "        x_axis_2[i], \n",
    "        y_score_2[i], \n",
    "        y_score_2[i]\n",
    "    )\n",
    "\n",
    "# 圖表標題\n",
    "plt.xlabel(\"回合數\")\n",
    "plt.ylabel(\"分數\")\n",
    "plt.title(\"隊伍競賽分數\")\n",
    "plt.xticks(ticks, list_labels)\n",
    "plt.legend(loc='upper left', ncols=2)\n",
    "\n",
    "# 顯示圖表\n",
    "plt.show()"
   ]
  }
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