視覺化_長條圖_圓餅圖.ipynb

{"nbformat":4,"nbformat_minor":0,"metadata":{"colab":{"name":"視覺化_長條圖_圓餅圖.ipynb","provenance":[],"collapsed_sections":["NY3HmF15aTRm"],"toc_visible":true},"kernelspec":{"name":"python3","display_name":"Python 3"},"language_info":{"name":"python"}},"cells":[{"cell_type":"markdown","metadata":{"id":"view-in-github"},"source":["<a href=\"https://colab.research.google.com/github/sueshow/Python_Basics/blob/main/%E8%A6%96%E8%A6%BA%E5%8C%96_%E9%95%B7%E6%A2%9D%E5%9C%96_%E5%9C%93%E9%A4%85%E5%9C%96.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"]},{"cell_type":"markdown","metadata":{"id":"vH1Z1Vx3gnON"},"source":["![Logo](https://raw.githubusercontent.com/AI-FREE-Team/Machine-Learning-Basic/main/README_imgs/aifreeteam.png) \n","<center>Welcome to the course《Python: from business analytics to Artificial Intelligence》by AI . FREE Team.</center>\n","<center>歡迎大家來到 AI . FREE Team 《Python 從商業分析到人工智慧》的第二堂課 - 機器學習(ML)基礎教學。 </center>\n","<br/>\n","\n","<center>作者: Michelle Chuang (<a href=\"https://github.com/sueshow\">Github</a>)</center>\n","<center>AI . FREE Team: <a href=\"https://aifreeblog.herokuapp.com/\">https://aifreeblog.herokuapp.com/</a></center>\n","<br>"]},{"cell_type":"markdown","metadata":{"id":"cYuQGWSkgqIQ"},"source":["# 前言"]},{"cell_type":"markdown","metadata":{"id":"T-xoBT8Y3cFd"},"source":["本文開始帶領大家以視覺化的統計圖表探索資料，將介紹 <font color=\"#00dd00\">**Bar Chart**</font> 和 <font color=\"#00dd00\">**Pie Plot**</font> ，讓大家瞭解如何善用統計圖表說明資料。在開始之前別忘了匯入中文字型喔！要先把檔案放到右側 file 夾中，確認上傳完成後再執行下面的語法"]},{"cell_type":"code","metadata":{"id":"3dHuCd804rq4"},"source":["import matplotlib as mpl\n","\n","mpl.font_manager.fontManager.addfont( 'taipei_sans_tc_beta.ttf' )\n","mpl.rc( 'font', family='Taipei Sans TC Beta' )"],"execution_count":null,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"JHXRhR45gvX0"},"source":["## 匯入基本套件"]},{"cell_type":"code","metadata":{"id":"q6YGgfZ9ig_D","colab":{"base_uri":"https://localhost:8080/"},"outputId":"0ac879fb-2a41-49e2-dff0-076a00a0459c"},"source":["import numpy as np \n","import pandas as pd \n","import matplotlib\n","import matplotlib.pyplot as plt   # 繪製影像 \n","import seaborn as sns\n","\n","print( 'NumPy 版本: ', np.__version__ )\n","print( 'Pandas 版本: ', pd.__version__ )  \n","print( 'Matplotlib 版本: ', matplotlib.__version__ )\n","print( 'Seaborn 版本: ', sns.__version__ )"],"execution_count":null,"outputs":[{"output_type":"stream","text":["NumPy 版本:  1.19.5\n","Pandas 版本:  1.1.5\n","Matplotlib 版本:  3.2.2\n","Seaborn 版本:  0.11.1\n"],"name":"stdout"}]},{"cell_type":"markdown","metadata":{"id":"K1EzOQNmhrBY"},"source":["# 長條圖 (Bar Chart)"]},{"cell_type":"markdown","metadata":{"id":"as5h5wABi2TK"},"source":["長條圖 (Bar Chart) 能用來描述類別資料的次數分配情況，或計量的高低比較，如：不同公司的營收、不同國家的染疫人數等。"]},{"cell_type":"markdown","metadata":{"id":"aEAGzYhKruqJ"},"source":["## 實際應用：COVID-19"]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":621},"id":"3yLH-h-vJCwv","cellView":"form","outputId":"c9e4e9ac-a995-4f69-d0d3-87fe6e4bb963"},"source":["#@markdown 資料來源: [Our World in Data](https://ourworldindata.org/grapher/full-list-cumulative-total-tests-per-thousand-bar-chart?country=JPN~USA~GBR~CHN~IDN~IND~TWN~KOR~SGP~DEU~HKG)\n","\n","from IPython.core.display import HTML\n","\n","HTML('<iframe src=\"https://ourworldindata.org/grapher/full-list-cumulative-total-tests-per-thousand-bar-chart?country=JPN~USA~GBR~CHN~IDN~IND~TWN~KOR~SGP~DEU~HKG\" loading=\"lazy\" style=\"width: 100%; height: 600px; border: 0px none;\"></iframe>')"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/html":["<iframe src=\"https://ourworldindata.org/grapher/full-list-cumulative-total-tests-per-thousand-bar-chart?country=JPN~USA~GBR~CHN~IDN~IND~TWN~KOR~SGP~DEU~HKG\" loading=\"lazy\" style=\"width: 100%; height: 600px; border: 0px none;\"></iframe>"],"text/plain":["<IPython.core.display.HTML object>"]},"metadata":{"tags":[]},"execution_count":44}]},{"cell_type":"markdown","metadata":{"id":"fXgSTgKF23lm"},"source":["## 小範例"]},{"cell_type":"markdown","metadata":{"id":"ugKRcg6XpkbZ"},"source":["使用 [`matplotlib.pyplot.bar( x, height, align, width, color, tick_label )`](https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.bar.html) 函數繪製**垂直**長條圖，常用的參數說明如下：\n","  * x：x 軸的類別型態資料  \n","  * height：y 軸的數值型態資料\n","  * align：x 軸各類別資料對齊長條圖的方式，兩種對齊方式分別為：  \n","    * 'edge'：對齊長條圖下緣的左端點\n","    * 'center'(預設)：對齊長條圖下緣的中心點\n","  * width：長條圖的寬度 \n","  * color：長條圖的顏色  \n","  * tick_label：x 軸類別型態資料的相對應標籤，預設為 None  \n","\n","另外， [`matplotlib.pyplot.barh( )`](https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.barh.html) 函數則用來繪製水平長條圖，其常用的參數說明與垂直長條圖相同。    "]},{"cell_type":"markdown","metadata":{"id":"Ijk2s4STXrRE"},"source":["### 垂直長條圖 Vertical Bar Chart"]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":452},"id":"sdOhNuF72-l3","outputId":"c4f1f94e-0036-40fa-fd39-eb4b7657ddf6"},"source":["plt.style.use( 'default' )\n","# Data \n","x = ['A','B','C','D','E']\n","y = [30,50,85,20,70]  \n","xlabel = ['=0','1~10','11~20','21~30','31~40']\n","bar_width = 0.5\n","\n","plt.bar( x, y, align='edge', tick_label=xlabel, width=bar_width)\n","plt.title('Vertical Bar Chart')\n","plt.show()"],"execution_count":null,"outputs":[{"output_type":"display_data","data":{"image/png":"iVBORw0KGgoAAAANSUhEUgAAAh8AAAGzCAYAAACPa3XZAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAAPYQAAD2EBqD+naQAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjIsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+WH4yJAAAgAElEQVR4nO3df3zP9f7/8ft+2Htj3psxm8UYyVDoTLEo0rKEyI4ifRJKzhnF+nGsi18Thhykz1DSqkvGoQ6lX3IWU8cmFiXOV2QdajZH2sacjez1/aOL96d3G3lvb8/9cLteLq/L8X6+nq/n8/F+P0/bfa/36/V+e1iWZQkAAMAQz+ouAAAAXF0IHwAAwCjCBwAAMIrwAQAAjCJ8AAAAowgfAADAKMIHAAAwivABAACMInwAAACjCB9AHfXwww+rVatWV2z83r17q3fv3lds/OrUqlUrDRgwoLrLAOoswgfgRvfcc4/q16+vU6dOXbTPiBEj5OPjox9//LHK8+Xm5mrGjBnas2dPlce6Ulq1aiUPDw/H5uvrq7Zt2+rpp5/WyZMnjdaSn5+vp556SpGRkapfv74aNGigqKgozZo1SwUFBUZruWD//v2aMWOGvvvuu2qZH6gO3tVdAFCXjBgxQhs3btT69ev10EMPldt/5swZvfPOO7rrrrvUuHHjKs+Xm5urpKQktWrVSl26dHHat2LFCpWVlVV5Dnfo0qWLnnzySUlSSUmJsrOztXjxYmVkZOjzzz83UsPOnTt199136/Tp03rwwQcVFRUlSdq1a5fmzp2rbdu26eOPPzZSy6/t379fSUlJ6t279xU9UwXUJIQPwI3uueceNWzYUGlpaRWGj3feeUfFxcUaMWJEleb5+eeffzdY1KtXr0pzuNM111yjBx980PH4kUcekb+/vxYsWKCDBw+qbdu2VZ6juLhYDRo0qHBfQUGB7r33Xnl5eWn37t2KjIx02j979mytWLGiyjW4oqSkRD4+PkbnBGoK3nYB3MjPz09DhgxRenq6jh8/Xm5/WlqaGjZsqHvuuUfSL78UJ06cqBYtWshms+naa6/VvHnznILFd999Jw8PDy1YsECLFy9WmzZtZLPZtHTpUt10002SpFGjRjne1njttdckVXzNR1lZmV544QXdcMMN8vX1VXBwsO666y7t2rXL0Sc1NVV9+vRR06ZNZbPZ1KFDBy1btszNr5QUGhoqSfL2/r+/gb766is9/PDDat26tXx9fRUaGqrRo0eXe4tqxowZ8vDw0P79+/XAAw+oUaNG6tmz50Xneumll/TDDz9o4cKF5YKHJIWEhGjKlCnl2j/77DPdfPPN8vX1VevWrfXGG2847T958qSeeuop3XDDDfL395fdble/fv305ZdfOvXbunWrPDw8tGbNGk2ZMkXXXHON6tevryVLlmjo0KGSpNtvv92xhlu3br30iwfUcpz5ANxsxIgRev3117V27VqNHz/e0X7y5Elt2rRJw4cPl5+fn86cOaNevXrphx9+0GOPPabw8HBt375diYmJOnbsmBYvXuw0bmpqqkpKSjR27FjZbDbde++9OnXqlKZNm6axY8fq1ltvlSTdcsstF61tzJgxeu2119SvXz898sgj+vnnn/Xpp58qKytLXbt2lSQtW7ZMHTt21D333CNvb29t3LhRf/7zn1VWVqb4+PhKvSbnzp3TiRMnJP3yF//u3bu1cOFC3XbbbYqIiHD027x5sw4fPqxRo0YpNDRU+/bt08svv6x9+/YpKytLHh4eTuMOHTpUbdu21Zw5c2RZ1kXnf/fdd+Xn56c//vGPl13zoUOH9Mc//lFjxozRyJEj9eqrr+rhhx9WVFSUOnbsKEk6fPiwNmzYoKFDhyoiIkL5+fl66aWX1KtXL+3fv19hYWFOYz733HPy8fHRU089pdLSUvXt21ePP/64lixZomeffVbt27eXJMf/AnWWBcCtfv75Z6tZs2ZWdHS0U/vy5cstSdamTZssy7Ks5557zmrQoIH1zTffOPWbPHmy5eXlZR05csSyLMvKycmxJFl2u906fvy4U9+dO3dakqzU1NRydYwcOdJq2bKl4/Enn3xiSbIef/zxcn3Lysoc/z5z5ky5/bGxsVbr1q2d2nr16mX16tWr/AvwGy1btrQkldt69OhhnThxwqlvRXOvXr3akmRt27bN0TZ9+nRLkjV8+PDfnd+yLKtRo0ZW586dL6vvr2v+9ZzHjx+3bDab9eSTTzraSkpKrPPnzzsdm5OTY9lsNmvmzJmOti1btliSrNatW5d7juvWrbMkWVu2bLns+oDajrddADfz8vLSsGHDlJmZ6XQHQ1pamkJCQnTHHXdIktatW6dbb71VjRo10okTJxxbTEyMzp8/r23btjmNGxcXp+Dg4ErX9fbbb8vDw0PTp08vt+/XZxT8/Pwc/y4sLNSJEyfUq1cvHT58WIWFhZWau1u3btq8ebM2b96s9957T7Nnz9a+fft0zz336L///W+Fc5eUlOjEiRPq3r27JOmLL74oN+64ceMua/6ioiI1bNjQpZo7dOjgOJskScHBwWrXrp0OHz7saLPZbPL0/OXH6Pnz5/Xjjz/K399f7dq1q7DekSNHOj1H4GpF+ACugAsXlKalpUmSvv/+e3366acaNmyYvLy8JEkHDx7URx99pODgYKctJiZGkspdM/Lrtycq49tvv1VYWJiCgoIu2e+f//ynYmJi1KBBAwUGBio4OFjPPvusJFU6fDRp0kQxMTGKiYlR//799eyzz+qVV17R9u3b9corrzj6nTx5Uk888YRCQkLk5+en4OBgx/OuaO7LfU3sdvslb3+uSHh4eLm2Ro0a6aeffnI8Lisr06JFi9S2bVvZbDY1adJEwcHB+uqrr6pUL1DXcc0HcAVERUUpMjJSq1ev1rPPPqvVq1fLsiynu1zKysp055136plnnqlwjOuuu87psYm/mL/99lvdcccdioyM1MKFC9WiRQv5+Pjogw8+0KJFi9x66+6FM0Dbtm3ThAkTJEn33Xeftm/frqefflpdunSRv7+/ysrKdNddd1U49+W+JpGRkdqzZ4/Onj172XeYXAiJv2X96tqSOXPmaOrUqRo9erSee+45BQUFydPTUxMnTqxSvUBdR/gArpARI0Zo6tSp+uqrr5SWlqa2bds67k6RpDZt2uj06dOOMx2V8dsLMC+lTZs22rRpk06ePHnRsx8bN25UaWmp3n33Xae//Lds2VLpGi/m559/liSdPn1akvTTTz8pPT1dSUlJmjZtmqPfwYMHqzzXwIEDlZmZqbffflvDhw+v8ngXvPXWW7r99tu1cuVKp/aCggI1adLkssZwZQ2BuoK3XYAr5MJZjmnTpmnPnj3lPtvjvvvuU2ZmpjZt2lTu2IKCAscv50u58LkWl/PpnHFxcbIsS0lJSeX2Xfhr/sJf+7/+676wsFCpqam/O76rNm7cKEnq3LnzReeWVO6un8oYN26cmjVrpieffFLffPNNuf3Hjx/XrFmzXB7Xy8urXL3r1q3TDz/8cNljuLKGQF3BmQ/gComIiNAtt9yid955R5LKhY+nn35a7777rgYMGOC4hbO4uFh79+7VW2+9pe++++53/3pu06aNAgMDtXz5cjVs2FANGjRQt27dKry24Pbbb9f//M//aMmSJTp48KDjrYxPP/1Ut99+u8aPH6++ffvKx8dHAwcO1GOPPabTp09rxYoVatq0qY4dO1bp1+KHH37Qm2++KUk6e/asvvzyS7300ktq0qSJ4y0Xu92u2267TfPnz9e5c+d0zTXX6OOPP1ZOTk6l572gUaNGWr9+ve6++2516dLF6RNOv/jiC61evVrR0dEujztgwADNnDlTo0aN0i233KK9e/dq1apVat269WWP0aVLF3l5eWnevHkqLCyUzWZzfM4KUGdV4502QJ2XkpJiSbJuvvnmCvefOnXKSkxMtK699lrLx8fHatKkiXXLLbdYCxYssM6ePWtZ1v/davv8889XOMY777xjdejQwfL29na67fa3t9pa1i+3AT///PNWZGSk5ePjYwUHB1v9+vWzsrOzHX3effddq1OnTpavr6/VqlUra968edarr75qSbJycnIc/Sp7q62np6fVtGlTa/jw4dahQ4ec+n7//ffWvffeawUGBloBAQHW0KFDrdzcXEuSNX36dEe/C7fa/uc///nd+X8tNzfXmjRpknXddddZvr6+Vv369a2oqChr9uzZVmFhoVPN/fv3L3f8b59zSUmJ9eSTT1rNmjWz/Pz8rB49eliZmZnl+l241XbdunUV1rVixQqrdevWlpeXF7fd4qrgYVmX+GQeAAAAN+OaDwAAYBThAwAAGEX4AAAARhE+AACAUYQPAABgFOEDAAAYVeM+ZKysrEy5ublq2LAhHzsMAEAtYVmWTp06pbCwMMe3PV9MjQsfubm5atGiRXWXAQAAKuHo0aNq3rz5JfvUuPDRsGFDSb8Ub7fbq7kaAABwOYqKitSiRQvH7/FLqXHh48JbLXa7nfABAEAtczmXTHDBKQAAMIrwAQAAjCJ8AAAAowgfAADAKMIHAAAwivABAACMInwAAACjCB8AAMAowgcAADCK8AEAAIwifAAAAKMIHwAAwCjCBwAAMIrwAQAAjPKu7gIA1BytJr9f3SW4xXdz+1d3CQAugTMfAADAKMIHAAAwivABAACMInwAAACjCB8AAMAowgcAADCK8AEAAIwifAAAAKMIHwAAwCjCBwAAMIrwAQAAjCJ8AAAAowgfAADAKMIHAAAwivABAACMInwAAACjXAof58+f19SpUxURESE/Pz+1adNGzz33nCzLcvSxLEvTpk1Ts2bN5Ofnp5iYGB08eNDthQMAgNrJpfAxb948LVu2TP/7v/+rf/3rX5o3b57mz5+vF1980dFn/vz5WrJkiZYvX64dO3aoQYMGio2NVUlJiduLBwAAtY+3K523b9+uQYMGqX///pKkVq1aafXq1fr8888l/XLWY/HixZoyZYoGDRokSXrjjTcUEhKiDRs2aNiwYW4uHwAA1DYunfm45ZZblJ6erm+++UaS9OWXX+qzzz5Tv379JEk5OTnKy8tTTEyM45iAgAB169ZNmZmZFY5ZWlqqoqIipw0AANRdLp35mDx5soqKihQZGSkvLy+dP39es2fP1ogRIyRJeXl5kqSQkBCn40JCQhz7fis5OVlJSUmVqR0AANRCLp35WLt2rVatWqW0tDR98cUXev3117VgwQK9/vrrlS4gMTFRhYWFju3o0aOVHgsAANR8Lp35ePrppzV58mTHtRs33HCD/v3vfys5OVkjR45UaGioJCk/P1/NmjVzHJefn68uXbpUOKbNZpPNZqts/QAAoJZx6czHmTNn5OnpfIiXl5fKysokSREREQoNDVV6erpjf1FRkXbs2KHo6Gg3lAsAAGo7l858DBw4ULNnz1Z4eLg6duyo3bt3a+HChRo9erQkycPDQxMnTtSsWbPUtm1bRUREaOrUqQoLC9PgwYOvyBMAAAC1i0vh48UXX9TUqVP15z//WcePH1dYWJgee+wxTZs2zdHnmWeeUXFxscaOHauCggL17NlTH330kXx9fd1ePAAAqH08rF9/PGkNUFRUpICAABUWFsput1d3OcBVpdXk96u7BLf4bm7/6i4BuOq48vub73YBAABGET4AAIBRhA8AAGAU4QMAABhF+AAAAEYRPgAAgFGEDwAAYBThAwAAGEX4AAAARhE+AACAUYQPAABgFOEDAAAYRfgAAABGET4AAIBRhA8AAGAU4QMAABhF+AAAAEYRPgAAgFGEDwAAYBThAwAAGEX4AAAARhE+AACAUYQPAABgFOEDAAAYRfgAAABGET4AAIBRhA8AAGAU4QMAABhF+AAAAEYRPgAAgFGEDwAAYJRL4aNVq1by8PAot8XHx0uSSkpKFB8fr8aNG8vf319xcXHKz8+/IoUDAIDayaXwsXPnTh07dsyxbd68WZI0dOhQSdKkSZO0ceNGrVu3ThkZGcrNzdWQIUPcXzUAAKi1vF3pHBwc7PR47ty5atOmjXr16qXCwkKtXLlSaWlp6tOnjyQpNTVV7du3V1ZWlrp3717hmKWlpSotLXU8LioqcvU5AACAWsSl8PFrZ8+e1ZtvvqmEhAR5eHgoOztb586dU0xMjKNPZGSkwsPDlZmZedHwkZycrKSkpMqWAQCAMa0mv1/dJbjFd3P7V+v8lb7gdMOGDSooKNDDDz8sScrLy5OPj48CAwOd+oWEhCgvL++i4yQmJqqwsNCxHT16tLIlAQCAWqDSZz5Wrlypfv36KSwsrEoF2Gw22Wy2Ko0BAABqj0qFj3//+9/6xz/+ob///e+OttDQUJ09e1YFBQVOZz/y8/MVGhpa9UoBAECdUKm3XVJTU9W0aVP17/9/7xlFRUWpXr16Sk9Pd7QdOHBAR44cUXR0dNUrBQAAdYLLZz7KysqUmpqqkSNHytv7/w4PCAjQmDFjlJCQoKCgINntdk2YMEHR0dEXvdgUAABcfVwOH//4xz905MgRjR49uty+RYsWydPTU3FxcSotLVVsbKyWLl3qlkIBAEDd4HL46Nu3ryzLqnCfr6+vUlJSlJKSUuXCAABA3cR3uwAAAKMIHwAAwCjCBwAAMIrwAQAAjCJ8AAAAowgfAADAKMIHAAAwivABAACMInwAAACjCB8AAMAowgcAADCK8AEAAIwifAAAAKMIHwAAwCjCBwAAMIrwAQAAjCJ8AAAAowgfAADAKMIHAAAwivABAACMInwAAACjCB8AAMAowgcAADCK8AEAAIwifAAAAKMIHwAAwCjCBwAAMIrwAQAAjCJ8AAAAowgfAADAKJfDxw8//KAHH3xQjRs3lp+fn2644Qbt2rXLsd+yLE2bNk3NmjWTn5+fYmJidPDgQbcWDQAAai+XwsdPP/2kHj16qF69evrwww+1f/9+/fWvf1WjRo0cfebPn68lS5Zo+fLl2rFjhxo0aKDY2FiVlJS4vXgAAFD7eLvSed68eWrRooVSU1MdbREREY5/W5alxYsXa8qUKRo0aJAk6Y033lBISIg2bNigYcOGualsAABQW7l05uPdd99V165dNXToUDVt2lQ33nijVqxY4difk5OjvLw8xcTEONoCAgLUrVs3ZWZmVjhmaWmpioqKnDYAAFB3uRQ+Dh8+rGXLlqlt27batGmT/vSnP+nxxx/X66+/LknKy8uTJIWEhDgdFxIS4tj3W8nJyQoICHBsLVq0qMzzAAAAtYRL4aOsrEx/+MMfNGfOHN14440aO3asHn30US1fvrzSBSQmJqqwsNCxHT16tNJjAQCAms+l8NGsWTN16NDBqa19+/Y6cuSIJCk0NFSSlJ+f79QnPz/fse+3bDab7Ha70wYAAOoul8JHjx49dODAAae2b775Ri1btpT0y8WnoaGhSk9Pd+wvKirSjh07FB0d7YZyAQBAbefS3S6TJk3SLbfcojlz5ui+++7T559/rpdfflkvv/yyJMnDw0MTJ07UrFmz1LZtW0VERGjq1KkKCwvT4MGDr8gTAAAAtYtL4eOmm27S+vXrlZiYqJkzZyoiIkKLFy/WiBEjHH2eeeYZFRcXa+zYsSooKFDPnj310UcfydfX1+3FAwCA2sel8CFJAwYM0IABAy6638PDQzNnztTMmTOrVBgAAKib+G4XAABgFOEDAAAYRfgAAABGET4AAIBRhA8AAGAU4QMAABhF+AAAAEYRPgAAgFGEDwAAYBThAwAAGEX4AAAARhE+AACAUYQPAABgFOEDAAAYRfgAAABGET4AAIBRhA8AAGAU4QMAABhF+AAAAEYRPgAAgFGEDwAAYBThAwAAGEX4AAAARhE+AACAUYQPAABgFOEDAAAYRfgAAABGET4AAIBRhA8AAGAU4QMAABjlUviYMWOGPDw8nLbIyEjH/pKSEsXHx6tx48by9/dXXFyc8vPz3V40AACovVw+89GxY0cdO3bMsX322WeOfZMmTdLGjRu1bt06ZWRkKDc3V0OGDHFrwQAAoHbzdvkAb2+FhoaWay8sLNTKlSuVlpamPn36SJJSU1PVvn17ZWVlqXv37lWvFgAA1Houn/k4ePCgwsLC1Lp1a40YMUJHjhyRJGVnZ+vcuXOKiYlx9I2MjFR4eLgyMzMvOl5paamKioqcNgAAUHe5dOajW7dueu2119SuXTsdO3ZMSUlJuvXWW/X1118rLy9PPj4+CgwMdDomJCREeXl5Fx0zOTlZSUlJlasetV6rye9Xdwlu8d3c/tVdAgDUGi6Fj379+jn+3alTJ3Xr1k0tW7bU2rVr5efnV6kCEhMTlZCQ4HhcVFSkFi1aVGosAABQ81XpVtvAwEBdd911OnTokEJDQ3X27FkVFBQ49cnPz6/wGpELbDab7Ha70wYAAOquKoWP06dP69tvv1WzZs0UFRWlevXqKT093bH/wIEDOnLkiKKjo6tcKAAAqBtcetvlqaee0sCBA9WyZUvl5uZq+vTp8vLy0vDhwxUQEKAxY8YoISFBQUFBstvtmjBhgqKjo7nTBQAAOLgUPr7//nsNHz5cP/74o4KDg9WzZ09lZWUpODhYkrRo0SJ5enoqLi5OpaWlio2N1dKlS69I4QAAoHZyKXysWbPmkvt9fX2VkpKilJSUKhUFAADqLr7bBQAAGEX4AAAARhE+AACAUYQPAABgFOEDAAAYRfgAAABGET4AAIBRhA8AAGAU4QMAABhF+AAAAEYRPgAAgFGEDwAAYBThAwAAGEX4AAAARhE+AACAUYQPAABgFOEDAAAYRfgAAABGET4AAIBRhA8AAGAU4QMAABhF+AAAAEYRPgAAgFGEDwAAYBThAwAAGEX4AAAARhE+AACAUYQPAABgFOEDAAAYRfgAAABGET4AAIBRVQofc+fOlYeHhyZOnOhoKykpUXx8vBo3bix/f3/FxcUpPz+/yoUCAIC6odLhY+fOnXrppZfUqVMnp/ZJkyZp48aNWrdunTIyMpSbm6shQ4ZUuVAAAFA3VCp8nD59WiNGjNCKFSvUqFEjR3thYaFWrlyphQsXqk+fPoqKilJqaqq2b9+urKwstxUNAABqr0qFj/j4ePXv318xMTFO7dnZ2Tp37pxTe2RkpMLDw5WZmVnhWKWlpSoqKnLaAABA3eXt6gFr1qzRF198oZ07d5bbl5eXJx8fHwUGBjq1h4SEKC8vr8LxkpOTlZSU5GoZAACglnLpzMfRo0f1xBNPaNWqVfL19XVLAYmJiSosLHRsR48edcu4AACgZnIpfGRnZ+v48eP6wx/+IG9vb3l7eysjI0NLliyRt7e3QkJCdPbsWRUUFDgdl5+fr9DQ0ArHtNlsstvtThsAAKi7XHrb5Y477tDevXud2kaNGqXIyEj95S9/UYsWLVSvXj2lp6crLi5OknTgwAEdOXJE0dHR7qsaAADUWi6Fj4YNG+r66693amvQoIEaN27saB8zZowSEhIUFBQku92uCRMmKDo6Wt27d3df1QAAoNZy+YLT37No0SJ5enoqLi5OpaWlio2N1dKlS909DQAAqKWqHD62bt3q9NjX11cpKSlKSUmp6tAAAKAO4rtdAACAUYQPAABgFOEDAAAYRfgAAABGET4AAIBRhA8AAGAU4QMAABhF+AAAAEYRPgAAgFGEDwAAYBThAwAAGEX4AAAARhE+AACAUYQPAABgFOEDAAAYRfgAAABGET4AAIBRhA8AAGAU4QMAABhF+AAAAEYRPgAAgFGEDwAAYBThAwAAGEX4AAAARhE+AACAUYQPAABgFOEDAAAYRfgAAABGET4AAIBRhA8AAGCUS+Fj2bJl6tSpk+x2u+x2u6Kjo/Xhhx869peUlCg+Pl6NGzeWv7+/4uLilJ+f7/aiAQBA7eVS+GjevLnmzp2r7Oxs7dq1S3369NGgQYO0b98+SdKkSZO0ceNGrVu3ThkZGcrNzdWQIUOuSOEAAKB28nal88CBA50ez549W8uWLVNWVpaaN2+ulStXKi0tTX369JEkpaamqn379srKylL37t3dVzUAAKi1Kn3Nx/nz57VmzRoVFxcrOjpa2dnZOnfunGJiYhx9IiMjFR4erszMzIuOU1paqqKiIqcNAADUXS6d+ZCkvXv3Kjo6WiUlJfL399f69evVoUMH7dmzRz4+PgoMDHTqHxISory8vIuOl5ycrKSkJNcrr6RWk983NteV9N3c/tVdAgAAleLymY927dppz5492rFjh/70pz9p5MiR2r9/f6ULSExMVGFhoWM7evRopccCAAA1n8tnPnx8fHTttddKkqKiorRz50698MILuv/++3X27FkVFBQ4nf3Iz89XaGjoRcez2Wyy2WyVKB0AANRGVf6cj7KyMpWWlioqKkr16tVTenq6Y9+BAwd05MgRRUdHV3UaAABQR7h05iMxMVH9+vVTeHi4Tp06pbS0NG3dulWbNm1SQECAxowZo4SEBAUFBclut2vChAmKjo7mThcAAODgUvg4fvy4HnroIR07dkwBAQHq1KmTNm3apDvvvFOStGjRInl6eiouLk6lpaWKjY3V0qVLr0jhAACgdnIpfKxcufKS+319fZWSkqKUlJQqFQUAAOouvtsFAAAYRfgAAABGET4AAIBRhA8AAGAU4QMAABhF+AAAAEYRPgAAgFGEDwAAYBThAwAAGEX4AAAARhE+AACAUYQPAABgFOEDAAAYRfgAAABGET4AAIBRhA8AAGAU4QMAABhF+AAAAEYRPgAAgFGEDwAAYBThAwAAGEX4AAAARhE+AACAUYQPAABgFOEDAAAYRfgAAABGET4AAIBRhA8AAGAU4QMAABhF+AAAAEYRPgAAgFEuhY/k5GTddNNNatiwoZo2barBgwfrwIEDTn1KSkoUHx+vxo0by9/fX3FxccrPz3dr0QAAoPZyKXxkZGQoPj5eWVlZ2rx5s86dO6e+ffuquLjY0WfSpEnauHGj1q1bp4yMDOXm5mrIkCFuLxwAANRO3q50/uijj5wev/baa2ratKmys7N12223qbCwUCtXrlRaWpr69OkjSUpNTVX79u2VlZWl7t27lxuztLRUpaWljsdFRUWVeR4AAKCWcCl8/FZhYUvv2G4AABD2SURBVKEkKSgoSJKUnZ2tc+fOKSYmxtEnMjJS4eHhyszMrDB8JCcnKykpqSplAECd1Gry+9Vdgtt8N7d/dZeAGqTSF5yWlZVp4sSJ6tGjh66//npJUl5ennx8fBQYGOjUNyQkRHl5eRWOk5iYqMLCQsd29OjRypYEAABqgUqf+YiPj9fXX3+tzz77rEoF2Gw22Wy2Ko0BAABqj0qd+Rg/frzee+89bdmyRc2bN3e0h4aG6uzZsyooKHDqn5+fr9DQ0KpVCgAA6gSXwodlWRo/frzWr1+vTz75RBEREU77o6KiVK9ePaWnpzvaDhw4oCNHjig6Oto9FQMAgFrNpbdd4uPjlZaWpnfeeUcNGzZ0XMcREBAgPz8/BQQEaMyYMUpISFBQUJDsdrsmTJig6OjoCi82BQAAVx+XwseyZcskSb1793ZqT01N1cMPPyxJWrRokTw9PRUXF6fS0lLFxsZq6dKlbikWAADUfi6FD8uyfrePr6+vUlJSlJKSUumiAABA3cV3uwAAAKMIHwAAwCjCBwAAMIrwAQAAjCJ8AAAAowgfAADAKMIHAAAwivABAACMInwAAACjCB8AAMAowgcAADCK8AEAAIwifAAAAKMIHwAAwCjCBwAAMIrwAQAAjCJ8AAAAowgfAADAKMIHAAAwivABAACMInwAAACjCB8AAMAowgcAADCK8AEAAIwifAAAAKMIHwAAwCjCBwAAMIrwAQAAjCJ8AAAAowgfAADAKJfDx7Zt2zRw4ECFhYXJw8NDGzZscNpvWZamTZumZs2ayc/PTzExMTp48KDbCgYAALWby+GjuLhYnTt3VkpKSoX758+fryVLlmj58uXasWOHGjRooNjYWJWUlFS5WAAAUPt5u3pAv3791K9fvwr3WZalxYsXa8qUKRo0aJAk6Y033lBISIg2bNigYcOGVa1aAABQ67n1mo+cnBzl5eUpJibG0RYQEKBu3bopMzOzwmNKS0tVVFTktAEAgLrLreEjLy9PkhQSEuLUHhIS4tj3W8nJyQoICHBsLVq0cGdJAACghqn2u10SExNVWFjo2I4ePVrdJQEAgCvIreEjNDRUkpSfn+/Unp+f79j3WzabTXa73WkDAAB1l1vDR0REhEJDQ5Wenu5oKyoq0o4dOxQdHe3OqQAAQC3l8t0up0+f1qFDhxyPc3JytGfPHgUFBSk8PFwTJ07UrFmz1LZtW0VERGjq1KkKCwvT4MGD3Vo4AAConVwOH7t27dLtt9/ueJyQkCBJGjlypF577TU988wzKi4u1tixY1VQUKCePXvqo48+kq+vr/uqBgAAtZbL4aN3796yLOui+z08PDRz5kzNnDmzSoUBAIC6qdrvdgEAAFcXwgcAADCK8AEAAIwifAAAAKMIHwAAwCjCBwAAMIrwAQAAjCJ8AAAAowgfAADAKMIHAAAwivABAACMInwAAACjCB8AAMAowgcAADCK8AEAAIwifAAAAKMIHwAAwCjCBwAAMIrwAQAAjCJ8AAAAowgfAADAKMIHAAAwivABAACMInwAAACjCB8AAMAowgcAADCK8AEAAIwifAAAAKMIHwAAwCjCBwAAMOqKhY+UlBS1atVKvr6+6tatmz7//PMrNRUAAKhFrkj4+Nvf/qaEhARNnz5dX3zxhTp37qzY2FgdP378SkwHAABqkSsSPhYuXKhHH31Uo0aNUocOHbR8+XLVr19fr7766pWYDgAA1CLe7h7w7Nmzys7OVmJioqPN09NTMTExyszMLNe/tLRUpaWljseFhYWSpKKiIneXJkkqKz1zRcY17Uq9PqaxHjUL61Gz1JX1kFiTmuZKrMeFMS3L+t2+bg8fJ06c0Pnz5xUSEuLUHhISov/3//5fuf7JyclKSkoq196iRQt3l1anBCyu7grwa6xHzcJ61DysSc1yJdfj1KlTCggIuGQft4cPVyUmJiohIcHxuKysTCdPnlTjxo3l4eHh1rmKiorUokULHT16VHa73a1jw3WsR83CetQsrEfNw5pcmmVZOnXqlMLCwn63r9vDR5MmTeTl5aX8/Hyn9vz8fIWGhpbrb7PZZLPZnNoCAwPdXZYTu93O/3FqENajZmE9ahbWo+ZhTS7u9854XOD2C059fHwUFRWl9PR0R1tZWZnS09MVHR3t7ukAAEAtc0XedklISNDIkSPVtWtX3XzzzVq8eLGKi4s1atSoKzEdAACoRbxmzJgxw92DXn/99QoMDNTs2bO1YMECSdKqVavUrl07d0/lMi8vL/Xu3Vve3tV+uQvEetQ0rEfNwnrUPKyJe3hYl3NPDAAAgJvw3S4AAMAowgcAADCK8AEAAIwifAAAAKMIHwAAwKirNnycPHlSI0aMkN1uV2BgoMaMGaPTp09Xd1m1yrZt2zRw4ECFhYXJw8NDGzZsqNJ4jz/+uKKiomSz2dSlS5cK+3z11Ve69dZb5evrqxYtWmj+/PlVmrO2+r3X/u9//7v69u3r+JqCPXv2VGm+rVu3atCgQWrWrJkaNGigLl26aNWqVeX6rVu3TpGRkfL19dUNN9ygDz74oErz1hbJycm66aab1LBhQzVt2lSDBw/WgQMHnPq8/PLL6t27t+x2uzw8PFRQUFClOT/77DP16NFDjRs3lp+fnyIjI7Vo0aJy/VJSUtSqVSv5+vqqW7du+vzzz6s0b22wbNkyderUyfFJpNHR0frwww8d+929Fr/2z3/+U97e3hX+DLsa1+JirtrwMWLECO3bt0+bN2/We++9p23btmns2LHVXVatUlxcrM6dOyslJcVtY44ePVr3339/hfuKiorUt29ftWzZUtnZ2Xr++ec1Y8YMvfzyy26bv7b4vde+uLhYPXv21Lx589wy3/bt29WpUye9/fbb+uqrrzRq1Cg99NBDeu+995z6DB8+XGPGjNHu3bs1ePBgDR48WF9//bVbaqjJMjIyFB8fr6ysLG3evFnnzp1T3759VVxc7Ohz5swZ3XXXXXr22WfdMmeDBg00fvx4bdu2Tf/61780ZcoUTZkyxem/h7/97W9KSEjQ9OnT9cUXX6hz586KjY3V8ePH3VJDTdW8eXPNnTtX2dnZ2rVrl/r06aNBgwZp3759kty/FhcUFBTooYce0h133FFu39W6FhdlXYX2799vSbJ27tzpaPvwww8tDw8P64cffqjGymovSdb69evdMtb06dOtzp07l2tfunSp1ahRI6u0tNTR9pe//MVq166dW+atrS712ufk5FiSrN27d7t93rvvvtsaNWqU4/F9991n9e/f36lPt27drMcee8ztc9d0x48ftyRZGRkZ5fZt2bLFkmT99NNPbp/33nvvtR588EHH45tvvtmKj493PD5//rwVFhZmJScnu33umq5Ro0bWK6+84tTm7rW4//77rSlTplT4M4y1cFanznzMmTNH/v7+l9yOHDmizMxMBQYGqmvXro5jY2Ji5OnpqR07dlTjM7j6WC58xl1mZqZuu+02+fj4ONpiY2N14MAB/fTTT1eivKtaWVnZJfcXFhYqKCjI8TgzM1MxMTFOfWJjY5WZmXlF6qvJCgsLJcnp9XGHS63J7t27tX37dvXq1UuSdPbsWWVnZzutiaenp2JiYq6qNTl//rzWrFmj4uJit36/2G/XIjU1VYcPH9b06dPL9WUtyqtT4WPcuHHas2fPJbewsDDl5eWpadOmTsd6e3srKChIeXl51VR93XP8+HE99thjCgsLU0REhOLj47Vz506VlZUpJydH9957r+OH9OXIy8tTSEiIU9uFx6yb67799ls98MADatq0qdq1a6fJkyc7Tkvv3btXgwYNuuixa9eu1c6dO52+r+li63O1rU1ZWZkmTpyoHj166Prrr3fp2N27d2vgwIFq0qSJOnfurFmzZiknJ0fSL9d4PPTQQ+WOad68uWw2m7p27ar4+Hg98sgjkqQTJ07o/PnzV+2a7N27V/7+/rLZbBo3bpzWr1+vDh06XPbxrqzFwYMHNXnyZL355psVfuz61b4WFalTH04fFBTk9r80UHlTp05VUFCQPvjgAx0/flxr167VnXfeqeLiYvn7+yshIYGvpa5GkyZNUo8ePZSYmKjDhw9r9erVuummm3Tu3Dk1adJESUlJFR63ZcsWjRo1SitWrFDHjh0NV13zxcfH6+uvv9Znn33m8rHjxo3T6NGjNWfOHO3bt0+rVq3SzJkzZVmWwsPDHd+V9WuffvqpTp8+raysLE2ePFnXXnuthg8f7o6nUqu1a9dOe/bsUWFhod566y2NHDlSGRkZlx1ALnctzp8/rwceeEBJSUm67rrrruRTqlPqVPiYM2eO5syZc8k++/fvV2hoaLmLfH7++WedPHlSoaGhV7LEq8q8efMUGBjoeNy3b18tX75c//nPfxQaGioPDw+XxgsNDVV+fr5T24XHrJvr3njjDcf63HDDDRo0aJBKS0v1008/XfT1zMjI0MCBA7Vo0aJyf4VfbH2uprUZP3684wL25s2bu3z8pk2bnNZk2LBhOnPmjIqLixUcHFzhMREREY7++fn5mjFjhoYPH64mTZrIy8vrql0THx8fXXvttZKkqKgo7dy5Uy+88IJeeumlyzr+ctfi1KlT2rVrl3bv3q3x48dL+uXsl2VZ8vb21scff6yePXte1WtRkavybZfo6GgVFBQoOzvbcewnn3yisrIydevWrRqfQd3y6+Bxgbe3t5o1a+Zy8JCk6Ohobdu2TefOnXO0bd68We3atVOjRo2qVOvVqKL1sdlsF/1huHXrVvXv31/z5s2r8M6w6OhopaenO7Vt3rzZre+z11SWZWn8+PFav369PvnkE0cgcFVFa1K/fv2LBo/fKisrU2lpqaRffvlGRUU5rUlZWZnS09OvijX5rV+/NpfjctfCbrdr7969Tr9nxo0b5zjz0q1bN9aiAnXqzMflvu3Svn173XXXXXr00Ue1fPlynTt3TuPHj9ewYcMUFhZmoNK64fTp0zp06JDjcU5Ojvbs2aOgoCCFh4e7PN6hQ4d0+vRp5eXl6b///a/jsyk6dOggHx8fx6nNMWPG6C9/+Yu+/vprvfDCCxV+tkFd93uv/cmTJ3XkyBHl5uZKkuMzJ0JDQyv1l9aWLVs0YMAAPfHEE4qLi3O8T+3j4+P4b+6JJ55Qr1699Ne//lX9+/fXmjVrtGvXrqviVuj4+HilpaXpnXfeUcOGDR2vT0BAgPz8/CT9ck1MXl6eY9327t2rhg0bKjw8vFJvF6ekpCg8PFyRkZGSfvnslwULFujxxx939ElISNDIkSPVtWtX3XzzzVq8eLGKi4udrtWpixITE9WvXz+Fh4fr1KlTSktL09atW7Vp0yZJ7l0LT0/Pctf2NG3aVL6+vk7tV+taXFS13mtTjX788Udr+PDhlr+/v2W3261Ro0ZZp06dqu6yapULt6n9dhs5cmSlxuvVq1eF4+Xk5Dj6fPnll1bPnj0tm81mXXPNNdbcuXPd82Rqmd977VNTUyvcP3369ErNN3LkyArH69Wrl1O/tWvXWtddd53l4+NjdezY0Xr//fer9kRriYpeG0lWamqqo8/06dN/t48rlixZYnXs2NGqX7++ZbfbrRtvvNFaunSpdf78ead+L774ohUeHm75+PhYN998s5WVlVWFZ1o7jB492mrZsqXl4+NjBQcHW3fccYf18ccfO/a7ey1+62IfF3A1rsXFeFiWC/c6AgAAVFGduuYDAADUfIQPAABgFOEDAAAYRfgAAABGET4AAIBRhA8AAGAU4QMAABhF+AAAAEYRPgAAgFGEDwAAYBThAwAAGPX/AX7n5p3YGReyAAAAAElFTkSuQmCC\n","text/plain":["<Figure size 640x480 with 1 Axes>"]},"metadata":{"tags":[]}}]},{"cell_type":"markdown","metadata":{"id":"WzrGq1aLXx4Y"},"source":["### 水平長條圖 Horizontal Bar Chart"]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":452},"id":"XiExBvu0XyJ4","outputId":"a8077e0b-312c-4bdd-fccd-f8ec582a2003"},"source":["plt.barh( x, y, tick_label=xlabel )\n","plt.title('Horizontal Bar Chart')\n","plt.show()"],"execution_count":null,"outputs":[{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["<Figure size 640x480 with 1 Axes>"]},"metadata":{"tags":[]}}]},{"cell_type":"markdown","metadata":{"id":"JTP6_9UUYCL_"},"source":["### 累積長條圖"]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":425},"id":"rDZv1YAyYCeB","outputId":"2d2af858-eb82-40a6-b77e-e6b8ca2071cb"},"source":["df = pd.DataFrame(np.random.rand(10,2), columns=['A', 'B'])\n","df.plot.bar(stacked=True)\n","plt.show()"],"execution_count":null,"outputs":[{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["<Figure size 640x480 with 1 Axes>"]},"metadata":{"tags":[]}}]},{"cell_type":"markdown","metadata":{"id":"kFjzgOs-gvvu"},"source":["## 實作範例 - 台灣 COVID-19 縣市鄉鎮疫情表單"]},{"cell_type":"markdown","metadata":{"id":"06-trgjRWrsf"},"source":["* 資料來源 : [COVID-19 全球疫情地圖](https://covid-19.nchc.org.tw/dt_005-covidTable_taiwan.php?downloadall=yes) [台灣 COVID-19 縣市鄉鎮疫情表單](https://covid-19.nchc.org.tw/city_confirmed.php?downloadall=yes) \n","* 下載時間 : 2021/06/14 06:15 PM"]},{"cell_type":"markdown","metadata":{"id":"t6NE11W2t4zx"},"source":["### Step 1：匯入資料"]},{"cell_type":"markdown","metadata":{"id":"JmTtg50x1E8n"},"source":["首先，透過程式碼將資料集檔案 (excel) 從 GitHub 下載"]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"tB87xF7Zz_cv","outputId":"d5f799b2-254c-4b25-ec41-13abfec86370"},"source":["!git clone https://github.com/AI-FREE-Team/Machine-Learning-Basic.git"],"execution_count":null,"outputs":[{"output_type":"stream","text":["Cloning into 'Machine-Learning-Basic'...\n","remote: Enumerating objects: 291, done.\u001b[K\n","remote: Counting objects: 100% (291/291), done.\u001b[K\n","remote: Compressing objects: 100% (236/236), done.\u001b[K\n","remote: Total 291 (delta 130), reused 153 (delta 48), pack-reused 0\u001b[K\n","Receiving objects: 100% (291/291), 28.75 MiB | 20.54 MiB/s, done.\n","Resolving deltas: 100% (130/130), done.\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"tBGyqOqRYU92"},"source":["#若需要重新下載資料，先執行下面的語法：\n","#!rm -r /content/Machine-Learning-Basic"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"Dvjg0q11hsSI","colab":{"base_uri":"https://localhost:8080/","height":206},"outputId":"a263cc7d-62c1-4263-eb53-2be67de4bf57"},"source":["# 匯入 xlsx 檔\n","raw_data02 = pd.read_excel( '/content/Machine-Learning-Basic/Content/COVID-19/台灣COVID-19縣市鄉鎮疫情表單.xlsx', header=1 )\n","raw_data02.head()"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/html":["<div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>id</th>\n","      <th>個案研判日</th>\n","      <th>縣市</th>\n","      <th>鄉鎮</th>\n","      <th>性別</th>\n","      <th>是否為境外移入</th>\n","      <th>年齡層</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>175</td>\n","      <td>2021-06-13</td>\n","      <td>花蓮縣</td>\n","      <td>新城鄉</td>\n","      <td>女</td>\n","      <td>否</td>\n","      <td>15-19</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>174</td>\n","      <td>2021-06-13</td>\n","      <td>桃園市</td>\n","      <td>龜山區</td>\n","      <td>男</td>\n","      <td>否</td>\n","      <td>55-59</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>173</td>\n","      <td>2021-06-13</td>\n","      <td>桃園市</td>\n","      <td>龜山區</td>\n","      <td>男</td>\n","      <td>否</td>\n","      <td>45-49</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>172</td>\n","      <td>2021-06-13</td>\n","      <td>桃園市</td>\n","      <td>龜山區</td>\n","      <td>女</td>\n","      <td>否</td>\n","      <td>5-9</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>171</td>\n","      <td>2021-06-13</td>\n","      <td>桃園市</td>\n","      <td>蘆竹區</td>\n","      <td>男</td>\n","      <td>否</td>\n","      <td>55-59</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>"],"text/plain":["    id       個案研判日   縣市   鄉鎮 性別 是否為境外移入    年齡層\n","0  175  2021-06-13  花蓮縣  新城鄉  女       否  15-19\n","1  174  2021-06-13  桃園市  龜山區  男       否  55-59\n","2  173  2021-06-13  桃園市  龜山區  男       否  45-49\n","3  172  2021-06-13  桃園市  龜山區  女       否    5-9\n","4  171  2021-06-13  桃園市  蘆竹區  男       否  55-59"]},"metadata":{"tags":[]},"execution_count":11}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"7bhF24q-ZgQV","outputId":"1093cd29-bb0f-4e23-d930-b6b2442b74b2"},"source":["# 檢視各欄位的資料型態\n","raw_data02.info()"],"execution_count":null,"outputs":[{"output_type":"stream","text":["<class 'pandas.core.frame.DataFrame'>\n","RangeIndex: 12921 entries, 0 to 12920\n","Data columns (total 7 columns):\n"," #   Column   Non-Null Count  Dtype \n","---  ------   --------------  ----- \n"," 0   id       12921 non-null  int64 \n"," 1   個案研判日    12921 non-null  object\n"," 2   縣市       12921 non-null  object\n"," 3   鄉鎮       12921 non-null  object\n"," 4   性別       12921 non-null  object\n"," 5   是否為境外移入  12921 non-null  object\n"," 6   年齡層      12921 non-null  object\n","dtypes: int64(1), object(6)\n","memory usage: 706.7+ KB\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"rIQzaHWRXbf9","outputId":"ef906df3-cd6a-4efe-ce04-9051cc40881e"},"source":["print( f'原始資料長相：{raw_data02.shape}\\n' )\n","print( f'每個欄位缺失值的總數：\\n{raw_data02.isnull( ).sum()}\\n' )"],"execution_count":null,"outputs":[{"output_type":"stream","text":["原始資料長相：(12921, 7)\n","\n","每個欄位缺失值的總數：\n","id         0\n","個案研判日      0\n","縣市         0\n","鄉鎮         0\n","性別         0\n","是否為境外移入    0\n","年齡層        0\n","dtype: int64\n","\n"],"name":"stdout"}]},{"cell_type":"markdown","metadata":{"id":"8xkm5ufOX3wZ"},"source":["發現資料很齊全，不需要進行資料的缺漏值處理"]},{"cell_type":"markdown","metadata":{"id":"tw4sXXvryp51"},"source":["### Step 2：資料處理"]},{"cell_type":"markdown","metadata":{"id":"H4Lq-j8kYPq0"},"source":["#### 01、樞紐分析\n","依【縣市】欄位中的縣市別，統計各縣市的個案總數："]},{"cell_type":"code","metadata":{"id":"PHuOsJ5MY5Ac"},"source":["# 統計各縣市的個案總數\n","summary = raw_data02[['縣市']].groupby(['縣市']).size()"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"l_nn70eZaPBV"},"source":["# Series 轉成 DataFrame 資料結構\n","summary = pd.DataFrame( summary )\n","summary.reset_index( inplace=True ) "],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":739},"id":"UlPUF9QxaQ2x","outputId":"022a60fe-72d7-4f83-8869-4a03009741c7"},"source":["# 重新定義 DataFrame 欄位名稱\n","summary.columns = ['縣市','個案總數'] \n","summary"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/html":["<div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>縣市</th>\n","      <th>個案總數</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>南投縣</td>\n","      <td>31</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>台中市</td>\n","      <td>178</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>台北市</td>\n","      <td>3935</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>台南市</td>\n","      <td>40</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>台東縣</td>\n","      <td>22</td>\n","    </tr>\n","    <tr>\n","      <th>5</th>\n","      <td>嘉義市</td>\n","      <td>9</td>\n","    </tr>\n","    <tr>\n","      <th>6</th>\n","      <td>嘉義縣</td>\n","      <td>17</td>\n","    </tr>\n","    <tr>\n","      <th>7</th>\n","      <td>基隆市</td>\n","      <td>249</td>\n","    </tr>\n","    <tr>\n","      <th>8</th>\n","      <td>境外移入</td>\n","      <td>1208</td>\n","    </tr>\n","    <tr>\n","      <th>9</th>\n","      <td>宜蘭縣</td>\n","      <td>92</td>\n","    </tr>\n","    <tr>\n","      <th>10</th>\n","      <td>屏東縣</td>\n","      <td>33</td>\n","    </tr>\n","    <tr>\n","      <th>11</th>\n","      <td>彰化縣</td>\n","      <td>250</td>\n","    </tr>\n","    <tr>\n","      <th>12</th>\n","      <td>新北市</td>\n","      <td>5634</td>\n","    </tr>\n","    <tr>\n","      <th>13</th>\n","      <td>新竹市</td>\n","      <td>35</td>\n","    </tr>\n","    <tr>\n","      <th>14</th>\n","      <td>新竹縣</td>\n","      <td>53</td>\n","    </tr>\n","    <tr>\n","      <th>15</th>\n","      <td>桃園市</td>\n","      <td>574</td>\n","    </tr>\n","    <tr>\n","      <th>16</th>\n","      <td>澎湖縣</td>\n","      <td>5</td>\n","    </tr>\n","    <tr>\n","      <th>17</th>\n","      <td>花蓮縣</td>\n","      <td>51</td>\n","    </tr>\n","    <tr>\n","      <th>18</th>\n","      <td>苗栗縣</td>\n","      <td>420</td>\n","    </tr>\n","    <tr>\n","      <th>19</th>\n","      <td>連江縣</td>\n","      <td>4</td>\n","    </tr>\n","    <tr>\n","      <th>20</th>\n","      <td>雲林縣</td>\n","      <td>19</td>\n","    </tr>\n","    <tr>\n","      <th>21</th>\n","      <td>高雄市</td>\n","      <td>62</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>"],"text/plain":["      縣市  個案總數\n","0    南投縣    31\n","1    台中市   178\n","2    台北市  3935\n","3    台南市    40\n","4    台東縣    22\n","5    嘉義市     9\n","6    嘉義縣    17\n","7    基隆市   249\n","8   境外移入  1208\n","9    宜蘭縣    92\n","10   屏東縣    33\n","11   彰化縣   250\n","12   新北市  5634\n","13   新竹市    35\n","14   新竹縣    53\n","15   桃園市   574\n","16   澎湖縣     5\n","17   花蓮縣    51\n","18   苗栗縣   420\n","19   連江縣     4\n","20   雲林縣    19\n","21   高雄市    62"]},"metadata":{"tags":[]},"execution_count":16}]},{"cell_type":"code","metadata":{"id":"JRhCLQppaTLL"},"source":["# 由小到大排序 '個案總數' 欄位\n","summary.sort_values( '個案總數', inplace=True )"],"execution_count":null,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"91lNf1EeyqRz"},"source":["### Step 3：繪圖"]},{"cell_type":"code","metadata":{"id":"DH3EXbM4yqdF","colab":{"base_uri":"https://localhost:8080/","height":452},"outputId":"30757766-bec5-4e86-9c1c-ad595a6a95cf"},"source":["# 水平長條圖 Horizontal Bar Chart\n","plt.barh( summary['縣市'], summary['個案總數'] )\n","\n","# 圖標題\n","plt.title( '個案總數' )\n","plt.show()"],"execution_count":null,"outputs":[{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["<Figure size 640x480 with 1 Axes>"]},"metadata":{"tags":[]}}]},{"cell_type":"markdown","metadata":{"id":"-YjR0Jk0gv5X"},"source":["# 圓餅圖 (Pie Chart)"]},{"cell_type":"markdown","metadata":{"id":"KoUGC4mPutQp"},"source":["圓餅圖 (Pie Chart) 可用來描述資料的數量、頻率或佔比等的相對關係，如：各類商品的銷售量、新冠肺炎確診的年齡層族群等。"]},{"cell_type":"markdown","metadata":{"id":"wyrvfsOrwAyp"},"source":["## 實際應用：Coronavirus_cases_by_age"]},{"cell_type":"markdown","metadata":{"id":"1eDPfmDeiwy9"},"source":["![Pie Chart](https://raw.githubusercontent.com/AI-FREE-Team/Machine-Learning-Basic/main/Content/(Pie_Chart)_Coronavirus_cases_by_age.png)"]},{"cell_type":"markdown","metadata":{"id":"N29nHuyTjOQm"},"source":["資料來源：[Eight weeks of coronavirus in India through data: Is the growth really exponential ?](https://citizenmatters.in/is-coronavirus-growing-exponentially-in-india-8-weeks-of-coronavirus-through-data-16902)"]},{"cell_type":"markdown","metadata":{"id":"QFwetXtQ28QX"},"source":["## 小範例"]},{"cell_type":"markdown","metadata":{"id":"HUiCSLS1yN4h"},"source":["使用 [`matplotlib.pyplot.pie( x, labels, colors, autopct, explode, radius, center, counterclock, startangle )`](https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.pie.html) 函數繪製垂直的直方圖，常用的參數說明如下：\n","  * x：資料 (1 維陣列)\n","  * labels：圓餅圖中每個扇形的標籤\n","  * colors：圓餅圖中每個扇形的顏色\n","  * autopct：圓餅圖中每個扇形所佔的比例格式，例如 : '%2.1%%' 表示整數2位數，小數 1 位數\n","  * explode：設定圓餅圖中分離出的扇形距離\n","  * radius：圓餅圖的半徑，預設為 `radius=1`\n","  * center：圓餅圖的中心點座標，預設為原點 $(0,0)$\n","  * counterclock：圓餅圖中扇形的呈現順序是否為逆時針，預設為 `counterclock=True`\n","  * startangle：圓餅圖的起始角度，預設為 `startangle=0`"]},{"cell_type":"code","metadata":{"id":"u863shTa5CCx","colab":{"base_uri":"https://localhost:8080/","height":406},"outputId":"6f525fc7-4143-433b-d577-073285181b7d"},"source":["plt.style.use( 'default' )\n","# Data \n","Products = ['Apple','Orange','Bananas','Mango','Watermelo']\n","Amount = [23,18,35,15,9] \n","\n","# 圓餅圖 Pie Chart\n","Colors = ['lightblue','lightgreen','red','yellow','pink']\n","plt.pie( x=Amount, labels=Products, colors=Colors, autopct='%2.1f%%', explode=(0,0,0.1,0,0), shadow=True )\n","plt.show()"],"execution_count":null,"outputs":[{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["<Figure size 640x480 with 1 Axes>"]},"metadata":{"tags":[]}}]},{"cell_type":"markdown","metadata":{"id":"RnMzCn8crbUQ"},"source":["## 實作範例 - 鐵達尼號"]},{"cell_type":"markdown","metadata":{"id":"BjQeOq7srgB9"},"source":["### Step 1：匯入資料"]},{"cell_type":"markdown","metadata":{"id":"U5fMW4hZwMrc"},"source":["要記得先把資料集檔案 (csv) 從 GitHub 下載下來喔！可先參考「折線圖/趨勢圖」的 Step 1，在此就不重複說明囉！"]},{"cell_type":"markdown","metadata":{"id":"KKUyEuk2bU1A"},"source":["* 資料來源：自 Kaggle 上 [鐵達尼號生存預測比賽](https://www.kaggle.com/c/titanic/overview/description) 取得，鐵達尼號已成為歷史上最著名的船舶之一。\n","<br>\n","* 資料說明：1912 年 4 月 10 日，鐵達尼號展開首航，也是唯一一次的載客出航，最終目的地為紐約。部分乘客為當時世界上頂尖富豪，以及許多來自英國、愛爾蘭、斯堪地那維亞和歐洲其他地區的移民，他們尋求在美國展開新生活的機會。4月15日在中途發生擦撞冰山後沉沒的嚴重災難。2,224 名船上人員中有 1,514 人罹難，成為近代史上最嚴重的和平時期船難。船長愛德華·約翰·史密斯最終與船一起沉沒，鐵達尼號總設計師湯瑪斯·安德魯斯也在這起災難中死亡。"]},{"cell_type":"code","metadata":{"id":"diGLuJd9wH-_"},"source":["#鐵達尼號 from kaggle\n","titanic_train = pd.read_csv( '/content/Machine-Learning-Basic/Content/Titanic/train.csv' )\n","titanic_test = pd.read_csv( '/content/Machine-Learning-Basic/Content/Titanic/test.csv' )\n","titanic_submit = pd.read_csv( '/content/Machine-Learning-Basic/Content/Titanic/gender_submission.csv' )"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":206},"id":"RZJWJpl90Icg","outputId":"77f091cd-9154-47ea-8d01-8dade53934ef"},"source":["# 合併 train 及 test 的資料 \n","titanic_data = titanic_train.append( titanic_test, sort=False )\n","titanic_data.head()"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/html":["<div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>PassengerId</th>\n","      <th>Survived</th>\n","      <th>Pclass</th>\n","      <th>Name</th>\n","      <th>Sex</th>\n","      <th>Age</th>\n","      <th>SibSp</th>\n","      <th>Parch</th>\n","      <th>Ticket</th>\n","      <th>Fare</th>\n","      <th>Cabin</th>\n","      <th>Embarked</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>1</td>\n","      <td>0.0</td>\n","      <td>3</td>\n","      <td>Braund, Mr. Owen Harris</td>\n","      <td>male</td>\n","      <td>22.0</td>\n","      <td>1</td>\n","      <td>0</td>\n","      <td>A/5 21171</td>\n","      <td>7.2500</td>\n","      <td>NaN</td>\n","      <td>S</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>2</td>\n","      <td>1.0</td>\n","      <td>1</td>\n","      <td>Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\n","      <td>female</td>\n","      <td>38.0</td>\n","      <td>1</td>\n","      <td>0</td>\n","      <td>PC 17599</td>\n","      <td>71.2833</td>\n","      <td>C85</td>\n","      <td>C</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>3</td>\n","      <td>1.0</td>\n","      <td>3</td>\n","      <td>Heikkinen, Miss. Laina</td>\n","      <td>female</td>\n","      <td>26.0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>STON/O2. 3101282</td>\n","      <td>7.9250</td>\n","      <td>NaN</td>\n","      <td>S</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>4</td>\n","      <td>1.0</td>\n","      <td>1</td>\n","      <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n","      <td>female</td>\n","      <td>35.0</td>\n","      <td>1</td>\n","      <td>0</td>\n","      <td>113803</td>\n","      <td>53.1000</td>\n","      <td>C123</td>\n","      <td>S</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>5</td>\n","      <td>0.0</td>\n","      <td>3</td>\n","      <td>Allen, Mr. William Henry</td>\n","      <td>male</td>\n","      <td>35.0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>373450</td>\n","      <td>8.0500</td>\n","      <td>NaN</td>\n","      <td>S</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>"],"text/plain":["   PassengerId  Survived  Pclass  ...     Fare Cabin  Embarked\n","0            1       0.0       3  ...   7.2500   NaN         S\n","1            2       1.0       1  ...  71.2833   C85         C\n","2            3       1.0       3  ...   7.9250   NaN         S\n","3            4       1.0       1  ...  53.1000  C123         S\n","4            5       0.0       3  ...   8.0500   NaN         S\n","\n","[5 rows x 12 columns]"]},"metadata":{"tags":[]},"execution_count":23}]},{"cell_type":"markdown","metadata":{"id":"HF6wA8Rprjm3"},"source":["### Step 2：資料處理"]},{"cell_type":"markdown","metadata":{"id":"SDkDztIEslb-"},"source":["#### 01、計算生還者的比率(Survival Ratio)"]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":112},"id":"jVbCnvA0vTKa","outputId":"b3eb872b-32f0-4518-a2c6-fe96ac30cda8"},"source":["Survived_Counts = titanic_data['Survived'].value_counts().reset_index()\n","Survived_Counts.columns = ['Survived','Counts']\n","Survived_Counts"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/html":["<div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>Survived</th>\n","      <th>Counts</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>0.0</td>\n","      <td>549</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>1.0</td>\n","      <td>342</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>"],"text/plain":["   Survived  Counts\n","0       0.0     549\n","1       1.0     342"]},"metadata":{"tags":[]},"execution_count":24}]},{"cell_type":"markdown","metadata":{"id":"haSr2wQBl8Td"},"source":["#### 02、轉換標題"]},{"cell_type":"code","metadata":{"id":"B6tqfxkokykE"},"source":["level = { 0:'Death', 1:'Survive' }\n","titanic_data['Survived Note'] = titanic_data['Survived'].map(level)"],"execution_count":null,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"xZZFIFqRrn8n"},"source":["### Step 3：繪圖"]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":444},"id":"A9cAaQ12roG0","outputId":"4e001cbf-644d-4873-f5f3-68e79bd1d580"},"source":["sns.set_style('darkgrid')\n","\n","plt.figure( figsize=(10,5) )\n","titanic_data['Survived Note'].value_counts().plot(kind='pie', colors=['lightcoral','skyblue'], autopct='%1.2f%%')\n","\n","plt.title('Survival')  # 圖標題\n","plt.ylabel('')\n","plt.show()"],"execution_count":null,"outputs":[{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["<Figure size 1000x500 with 1 Axes>"]},"metadata":{"tags":[]}}]},{"cell_type":"markdown","metadata":{"id":"U4v6UCzWNNEd"},"source":["# 結論"]},{"cell_type":"markdown","metadata":{"id":"t_1KntT6czq3"},"source":["如同 Wikipedia 所述，有效的視覺化可以幫助用戶分析和推理資料和證據。它使複雜的資料更容易理解、理解和使用。當日後在進行資料分析時，建議讀者官方文件 (Matplotlib Tutorial](https://matplotlib.org/stable/tutorials/index.html) 及 [Seaborn Tutorial](https://seaborn.pydata.org/tutorial.html) 尋找方法，以累積程式除錯的能力與經驗。<br>\n","視覺化將是探索式資料分析(Exploratory Data Analysis；EDA)的過程中重要工具之一，我們可依據各資料的特性來選用適合的圖示來進行視覺化，本文介紹長條圖、圓餅圖來觀察資料，讀者可透過瞭解資料特性後選擇適合呈現的樣式，讓數據透過視覺化說話。<br>\n","針對本文的內容，若讀者們有發現任何的錯誤或疑問，非常歡迎您 [來信 sueshow2006@gmail.com](mailto:sueshow2006@gmail.com) 給予建議及討論，讓我們一同來學習成長！"]},{"cell_type":"markdown","metadata":{"id":"nwVrXSCDgw0a"},"source":["# 參考資料"]},{"cell_type":"markdown","metadata":{"id":"Zf4SYTb8gyrI"},"source":["* [Select by partial string from a pandas DataFrame](https://stackoverflow.com/questions/11350770/select-by-partial-string-from-a-pandas-dataframe)\n","* [Titanic - Feature understanding from Plots](https://www.kaggle.com/saisivasriram/titanic-feature-understanding-from-plots)\n","* [Titanic Analysis_Learning to Swim with Python](https://www.kaggle.com/sgus1318/titanic-analysis-learning-to-swim-with-python)\n","* [python pandas pivot_table count frequency in one column](https://stackoverflow.com/questions/22412033/python-pandas-pivot-table-count-frequency-in-one-column)  \n","* [matplotlib (上)](https://blog.csdn.net/bryant_meng/article/details/80980820)\n","* [matplotlib and seaborn (下)](https://blog.csdn.net/bryant_meng/article/details/86383749)\n","* [How to delete the last row of data of a pandas dataframe](https://stackoverflow.com/questions/26921651/how-to-delete-the-last-row-of-data-of-a-pandas-dataframe) \n","* [如何在 Colab 中透過 Kaggle API 下載資料集](https://blog.toright.com/posts/6817/%E5%A6%82%E4%BD%95%E5%9C%A8-colab-%E4%B8%AD%E9%80%8F%E9%81%8E-kaggle-api-%E4%B8%8B%E8%BC%89%E8%B3%87%E6%96%99%E9%9B%86.html)"]},{"cell_type":"markdown","metadata":{"id":"NY3HmF15aTRm"},"source":["# 返回 [課程大綱](https://github.com/AI-FREE-Team/Machine-Learning-Basic#%E8%AA%B2%E7%A8%8B%E5%A4%A7%E7%B6%B1-course-outline)"]}]}