python-data-analysis-glimpse.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "# 成為資料分析師 | Python 資料分析\n",
    "\n",
    ">  資料分析套件快速入門\n",
    "\n",
    "郭耀仁 <yaojenkuo@datainpoint.com> from [DATAINPOINT](https://datainpoint.substack.com/welcome)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "## 大綱\n",
    "\n",
    "- 資料分析與管線流\n",
    "- NumPy 快速入門\n",
    "- Pandas 快速入門\n",
    "- Matplotlib 快速入門"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## 資料分析與管線流"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "## 資料分析的一切都與管線流息息相關\n",
    "\n",
    "![Imgur](https://i.imgur.com/din6Ig6.png)\n",
    "\n",
    "圖片來源: <https://r4ds.had.co.nz/>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "## 每個環節的意涵\n",
    "\n",
    "- Import：從常見來源將資料載入分析環境\n",
    "- Tidy/Transform：以適當的資料結構整併內容與轉換樣式\n",
    "- Visualize：探索分析資料的形狀、相關、組成與趨勢\n",
    "- Model：預測或挖掘資料的隱含特徵\n",
    "- Communicate：向產品、行銷與管理團隊精準且有效地傳達分析洞察\n",
    "- Program：利用程式語言掌握管線流"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "## 每個環節都有對應套件支援\n",
    "\n",
    "- [NumPy](https://numpy.org/) 可以支援 Import、Tidy/Transform 與 Model\n",
    "- [Pandas](https://pandas.pydata.org/) 可以支援 Import、Tidy/Transform 與 Visualise\n",
    "- [Matplotlib](https://matplotlib.org/) 可以支援 Visualise、Model 與 Communicate"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "## 確認分析環境能夠使用這三個套件"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib as mpl"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "## 檢視這些第三方套件的版本資訊"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1.18.4\n",
      "1.0.3\n",
      "3.2.1\n"
     ]
    }
   ],
   "source": [
    "print(np.__version__)\n",
    "print(pd.__version__)\n",
    "print(mpl.__version__)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## NumPy 快速入門"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "## 簡介 NumPy 套件\n",
    "\n",
    "> NumPy，Numerical Python 的簡稱，是使用 Python 進行科學計算的第三方套件，創造了一個稱為 N 維陣列（ndarray）的類別，透過 N 維陣列，可以將 Python 從一個泛用（general purposed）程式語言轉變成一個科學計算（scientific computing）程式語言，並且有豐富的統計、線性代數與隨機的函式。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "## 範例資料：COVID-19 每日報告\n",
    "\n",
    "資料來源：<https://github.com/CSSEGISandData/COVID-19/tree/master/csse_covid_19_data/csse_covid_19_daily_reports>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [],
   "source": [
    "import datetime\n",
    "\n",
    "def get_latest_daily_report():\n",
    "    \"\"\"\n",
    "    This function returns the latest global daily report from https://github.com/CSSEGISandData/COVID-19 and its file date.\n",
    "    \"\"\"\n",
    "    latest_date = datetime.date.today()\n",
    "    day_delta = datetime.timedelta(days=1)\n",
    "    fmt = '%m-%d-%Y'\n",
    "    while True:\n",
    "        try:\n",
    "            latest_date_fmt = latest_date.strftime(fmt)\n",
    "            csv_url = \"https://raw.githubusercontent.com/CSSEGISandData/COVID-19/master/csse_covid_19_data/csse_covid_19_daily_reports/{}.csv\".format(latest_date_fmt)\n",
    "            daily_report = pd.read_csv(csv_url)\n",
    "            print(\"載入了 {} 的每日報告。\".format(latest_date_fmt))\n",
    "            break\n",
    "        except:\n",
    "            latest_date_fmt = latest_date.strftime(fmt)\n",
    "            print(\"尚未有 {} 的每日報告。\".format(latest_date_fmt))\n",
    "            latest_date -= day_delta\n",
    "    return latest_date, daily_report"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "尚未有 09-13-2020 的每日報告。\n",
      "載入了 09-12-2020 的每日報告。\n"
     ]
    }
   ],
   "source": [
    "latest_date, daily_report = get_latest_daily_report()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "## N 維陣列可由資料框取出"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "numpy.ndarray"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "confirmed = daily_report['Confirmed'].values\n",
    "type(confirmed)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "## N 維陣列提供兩個好用的功能\n",
    "\n",
    "- 向量化（Vectorization）運算、有時也稱為元素級別（Element-wise）運算\n",
    "- 布林索引（Boolean indexing）"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "## 向量化運算"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "deaths = daily_report['Deaths'].values\n",
    "recovered = daily_report['Recovered'].values\n",
    "active = confirmed - deaths - recovered # Vectorization"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "## 布林索引"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3954\n",
      "[False False False ... False False False]\n",
      "65\n"
     ]
    }
   ],
   "source": [
    "print(confirmed.size)\n",
    "print(confirmed > 100000)                 # Creating a boolean array\n",
    "print(confirmed[confirmed > 100000].size) # Boolean indexing"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "## 試著使用內建資料結構 `list` 完成前兩個任務（藉此體驗 N 維陣列的便利性）\n",
    "\n",
    "1. 計算治療中個案數；\n",
    "2. 挑選確診數超過 10 萬的觀測值。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [],
   "source": [
    "# 計算治療中個案數\n",
    "confirmed = list(daily_report['Confirmed'].values)\n",
    "deaths = list(daily_report['Deaths'].values)\n",
    "recovered = list(daily_report['Recovered'].values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [],
   "source": [
    "# 挑選確診數超過 10 萬的觀測值\n",
    "confirmed = list(daily_report['Confirmed'].values)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "## 除了 N 維陣列，NumPy 還附帶豐富的函式\n",
    "\n",
    "- 通用函式\n",
    "- 聚合函式\n",
    "- 隨機函式\n",
    "- 線性代數函式\n",
    "- ...etc."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "## 暸解更多 NumPy 提供的功能\n",
    "\n",
    "[NumPy User Guide](https://www.numpy.org/devdocs/user/index.html)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## Pandas 快速入門"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "## 簡介 Pandas 套件\n",
    "\n",
    "> Pandas，Panel DataFrame Series 的簡稱，是在 Python 中分析表格資料的第三方套件，創造了稱為索引（Index）、序列（Series）與資料框（DataFrame）的類別，透過這些類別，可以讓 Python 在面對文字檔案、Excel 試算表與關聯式資料庫時能夠使用更直覺的觀念操作。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "## 將 CSV 文字檔案讀入成為資料框\n",
    "\n",
    "- 範例資料：COVID-19 每日報告\n",
    "- 資料來源：<https://github.com/CSSEGISandData/COVID-19/tree/master/csse_covid_19_data/csse_covid_19_daily_reports>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "尚未有 09-13-2020 的每日報告。\n",
      "載入了 09-12-2020 的每日報告。\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "pandas.core.frame.DataFrame"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "latest_date, daily_report = get_latest_daily_report()\n",
    "type(daily_report)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "## Pandas 提供三種新的資料結構\n",
    "\n",
    "- `DataFrame`\n",
    "- `Series`\n",
    "- `Index`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "<class 'pandas.core.series.Series'>\n",
      "<class 'pandas.core.indexes.base.Index'>\n",
      "<class 'pandas.core.indexes.range.RangeIndex'>\n"
     ]
    }
   ],
   "source": [
    "print(type(daily_report))              # DataFrame\n",
    "print(type(daily_report['Confirmed'])) # Series\n",
    "print(type(daily_report.columns))      # Index\n",
    "print(type(daily_report.index))        # Index"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "## 使用更直覺的觀念操作資料\n",
    "\n",
    "- 如何定義「更直覺」？\n",
    "    - Spreadsheet-like；\n",
    "    - SQL-like"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "## 基礎的資料框操作\n",
    "\n",
    "- 衍生（mutate），新增欄位到資料框中，特別是新欄位與既有欄位具有函式的輸出以及輸入關係；\n",
    "- 選擇（select），從資料框中依據名稱挑出單個或多個欄位；\n",
    "- 篩選（filter），依據判斷條件（布林值）從資料框中挑出符合（布林值為 True）的觀測值；\n",
    "- 摘要（summarise），對欄位進行聚合（Aggregate）的運算將多筆觀測值總結；\n",
    "- 排序（arrange），對觀測值由小到大（遞增）或者由大到小（遞減）變動排列順序；\n",
    "- 分組（group by），將欄位依照獨一類別進行摘要。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "## 衍生（mutate）\n",
    "\n",
    "治療中案例數可以用既有的欄位計算而得，常見的定義為：確診數減去死亡數再減痊癒數。\n",
    "\n",
    "\\begin{equation}\n",
    "\\text{Active} = \\text{Confirmed} - \\text{Deaths} - \\text{Recovered}\n",
    "\\end{equation}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0        5987\n",
       "1        4361\n",
       "2       12527\n",
       "3         348\n",
       "4        1914\n",
       "        ...  \n",
       "3949     9717\n",
       "3950        1\n",
       "3951      216\n",
       "3952     1147\n",
       "3953     1609\n",
       "Length: 3954, dtype: int64"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "active = daily_report[\"Confirmed\"] - daily_report[\"Deaths\"] - daily_report[\"Recovered\"]\n",
    "active"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "## 選擇（select）\n",
    "\n",
    "在中括號裡頭輸入欄位名稱可以將資料以 `Series` 外型從資料框中取出。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0              Afghanistan\n",
       "1                  Albania\n",
       "2                  Algeria\n",
       "3                  Andorra\n",
       "4                   Angola\n",
       "               ...        \n",
       "3949    West Bank and Gaza\n",
       "3950        Western Sahara\n",
       "3951                 Yemen\n",
       "3952                Zambia\n",
       "3953              Zimbabwe\n",
       "Name: Country_Region, Length: 3954, dtype: object"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "daily_report[\"Country_Region\"]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "## 選擇（select）\n",
    "\n",
    "若想要選擇多個欄位，將多個欄位名稱以 `list` 傳入中括號。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "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",
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       "    .dataframe thead th {\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Country_Region</th>\n",
       "      <th>Confirmed</th>\n",
       "      <th>Deaths</th>\n",
       "      <th>Recovered</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>38641</td>\n",
       "      <td>1420</td>\n",
       "      <td>31234</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Albania</td>\n",
       "      <td>11185</td>\n",
       "      <td>330</td>\n",
       "      <td>6494</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Algeria</td>\n",
       "      <td>48007</td>\n",
       "      <td>1605</td>\n",
       "      <td>33875</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Andorra</td>\n",
       "      <td>1344</td>\n",
       "      <td>53</td>\n",
       "      <td>943</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Angola</td>\n",
       "      <td>3335</td>\n",
       "      <td>132</td>\n",
       "      <td>1289</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3949</th>\n",
       "      <td>West Bank and Gaza</td>\n",
       "      <td>29906</td>\n",
       "      <td>210</td>\n",
       "      <td>19979</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3950</th>\n",
       "      <td>Western Sahara</td>\n",
       "      <td>10</td>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3951</th>\n",
       "      <td>Yemen</td>\n",
       "      <td>2009</td>\n",
       "      <td>582</td>\n",
       "      <td>1211</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3952</th>\n",
       "      <td>Zambia</td>\n",
       "      <td>13466</td>\n",
       "      <td>312</td>\n",
       "      <td>12007</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3953</th>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>7508</td>\n",
       "      <td>224</td>\n",
       "      <td>5675</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3954 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          Country_Region  Confirmed  Deaths  Recovered\n",
       "0            Afghanistan      38641    1420      31234\n",
       "1                Albania      11185     330       6494\n",
       "2                Algeria      48007    1605      33875\n",
       "3                Andorra       1344      53        943\n",
       "4                 Angola       3335     132       1289\n",
       "...                  ...        ...     ...        ...\n",
       "3949  West Bank and Gaza      29906     210      19979\n",
       "3950      Western Sahara         10       1          8\n",
       "3951               Yemen       2009     582       1211\n",
       "3952              Zambia      13466     312      12007\n",
       "3953            Zimbabwe       7508     224       5675\n",
       "\n",
       "[3954 rows x 4 columns]"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "multiple_columns = [\"Country_Region\", \"Confirmed\", \"Deaths\", \"Recovered\"]\n",
    "daily_report[multiple_columns]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "## 篩選（filter）\n",
    "\n",
    "在中括號裡頭輸入判斷條件所獲得的布林值 `Series` 可以獲得指定的觀測值。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0       False\n",
       "1       False\n",
       "2       False\n",
       "3       False\n",
       "4       False\n",
       "        ...  \n",
       "3949    False\n",
       "3950    False\n",
       "3951    False\n",
       "3952    False\n",
       "3953    False\n",
       "Name: Country_Region, Length: 3954, dtype: bool"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "is_tw = daily_report['Country_Region'] == 'Taiwan*' # 台灣在哪裡\n",
    "is_tw"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "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>FIPS</th>\n",
       "      <th>Admin2</th>\n",
       "      <th>Province_State</th>\n",
       "      <th>Country_Region</th>\n",
       "      <th>Last_Update</th>\n",
       "      <th>Lat</th>\n",
       "      <th>Long_</th>\n",
       "      <th>Confirmed</th>\n",
       "      <th>Deaths</th>\n",
       "      <th>Recovered</th>\n",
       "      <th>Active</th>\n",
       "      <th>Combined_Key</th>\n",
       "      <th>Incidence_Rate</th>\n",
       "      <th>Case-Fatality_Ratio</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>622</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Taiwan*</td>\n",
       "      <td>2020-09-13 04:30:52</td>\n",
       "      <td>23.7</td>\n",
       "      <td>121.0</td>\n",
       "      <td>498</td>\n",
       "      <td>7</td>\n",
       "      <td>475</td>\n",
       "      <td>16.0</td>\n",
       "      <td>Taiwan*</td>\n",
       "      <td>2.090963</td>\n",
       "      <td>1.405622</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     FIPS Admin2 Province_State Country_Region          Last_Update   Lat  \\\n",
       "622   NaN    NaN            NaN        Taiwan*  2020-09-13 04:30:52  23.7   \n",
       "\n",
       "     Long_  Confirmed  Deaths  Recovered  Active Combined_Key  Incidence_Rate  \\\n",
       "622  121.0        498       7        475    16.0      Taiwan*        2.090963   \n",
       "\n",
       "     Case-Fatality_Ratio  \n",
       "622             1.405622  "
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "daily_report[is_tw] # is_tw 是一個布林值 Series"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "## 摘要（summarize）\n",
    "\n",
    "對 `Series` 呼叫聚合方法，例如加總 `.sum()`。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "28759036"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "daily_report['Confirmed'].sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "## 分組（group by）與摘要（summarise）\n",
    "\n",
    "以國家為分組層級，所以在 `groupby()` 方法中傳入 Country_Region 欄位，獲得一個 `DataFrameGroupBy` 類別。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<pandas.core.groupby.generic.DataFrameGroupBy object at 0x7fa4a8b78898>"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "daily_report.groupby(\"Country_Region\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "## 分組（group by）與摘要（summarise） \n",
    "\n",
    "指定 `DataFrameGroupBy` 類別的欄位與聚合函式，獲得分組摘要的結果，是一個 `Series`。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Country_Region\n",
       "Afghanistan           38641\n",
       "Albania               11185\n",
       "Algeria               48007\n",
       "Andorra                1344\n",
       "Angola                 3335\n",
       "                      ...  \n",
       "West Bank and Gaza    29906\n",
       "Western Sahara           10\n",
       "Yemen                  2009\n",
       "Zambia                13466\n",
       "Zimbabwe               7508\n",
       "Name: Confirmed, Length: 188, dtype: int64"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "daily_report.groupby(\"Country_Region\")['Confirmed'].sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "## 排序（arrange）\n",
    "\n",
    "呼叫 `Series` 的 `sort_values(ascending=False)` 方法將摘要結果由大到小遞減排序。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Country_Region\n",
       "US              6485214\n",
       "India           4754356\n",
       "Brazil          4315687\n",
       "Russia          1053663\n",
       "Peru             716670\n",
       "Colombia         708964\n",
       "Mexico           663973\n",
       "South Africa     648214\n",
       "Spain            566326\n",
       "Argentina        546481\n",
       "Name: Confirmed, dtype: int64"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "confirmed_by_country = daily_report.groupby(\"Country_Region\")['Confirmed'].sum()\n",
    "confirmed_by_country.sort_values(ascending=False)[:10] # 顯示確診人數前 10 高的國家"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "## 除了基礎操作，Pandas 還能夠進行\n",
    "\n",
    "- 網頁表格內容載入\n",
    "- 關聯式資料庫操作\n",
    "- 視覺化\n",
    "- ...etc."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "## 暸解更多 Pandas 提供的功能\n",
    "\n",
    "[pandas: powerful Python data analysis toolkit](http://pandas.pydata.org/pandas-docs/stable/)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## Matplotlib 快速入門"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "## 簡介 Matplotlib 套件\n",
    "\n",
    "> Matplotlib，Matlab Plotting Library 的簡稱，是在 Python 中將資料視覺化的第三方套件。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "## pyplot 模組\n",
    "\n",
    "> 隸屬於 Matplotlib 的繪圖工具，用類似 Matlab 的語法建立視覺化。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "## 使用流程\n",
    "\n",
    "- 將資料整理為 `ndarray` 或 `Series` 格式\n",
    "- 呼叫 `plt.figure()` 展開「畫布物件」、呼叫 `plt.axes()` 展開「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（或者 `plt.savefig()` 儲存圖形）"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "## 將 CSV 文字檔案讀入成為資料框\n",
    "\n",
    "- 範例資料：COVID-19 時間序列\n",
    "- 資料來源：<https://github.com/CSSEGISandData/COVID-19/tree/master/csse_covid_19_data/csse_covid_19_time_series>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "pandas.core.frame.DataFrame"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "request_url = \"https://raw.githubusercontent.com/CSSEGISandData/COVID-19/master/csse_covid_19_data/csse_covid_19_time_series/time_series_covid19_confirmed_global.csv\"\n",
    "time_series = pd.read_csv(request_url)\n",
    "type(time_series)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "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>Province/State</th>\n",
       "      <th>Country/Region</th>\n",
       "      <th>Lat</th>\n",
       "      <th>Long</th>\n",
       "      <th>1/22/20</th>\n",
       "      <th>1/23/20</th>\n",
       "      <th>1/24/20</th>\n",
       "      <th>1/25/20</th>\n",
       "      <th>1/26/20</th>\n",
       "      <th>1/27/20</th>\n",
       "      <th>...</th>\n",
       "      <th>9/3/20</th>\n",
       "      <th>9/4/20</th>\n",
       "      <th>9/5/20</th>\n",
       "      <th>9/6/20</th>\n",
       "      <th>9/7/20</th>\n",
       "      <th>9/8/20</th>\n",
       "      <th>9/9/20</th>\n",
       "      <th>9/10/20</th>\n",
       "      <th>9/11/20</th>\n",
       "      <th>9/12/20</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>NaN</td>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>33.93911</td>\n",
       "      <td>67.709953</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>38288</td>\n",
       "      <td>38304</td>\n",
       "      <td>38324</td>\n",
       "      <td>38398</td>\n",
       "      <td>38494</td>\n",
       "      <td>38520</td>\n",
       "      <td>38544</td>\n",
       "      <td>38572</td>\n",
       "      <td>38606</td>\n",
       "      <td>38641</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>NaN</td>\n",
       "      <td>Albania</td>\n",
       "      <td>41.15330</td>\n",
       "      <td>20.168300</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>9844</td>\n",
       "      <td>9967</td>\n",
       "      <td>10102</td>\n",
       "      <td>10255</td>\n",
       "      <td>10406</td>\n",
       "      <td>10553</td>\n",
       "      <td>10704</td>\n",
       "      <td>10860</td>\n",
       "      <td>11021</td>\n",
       "      <td>11185</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>NaN</td>\n",
       "      <td>Algeria</td>\n",
       "      <td>28.03390</td>\n",
       "      <td>1.659600</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>45469</td>\n",
       "      <td>45773</td>\n",
       "      <td>46071</td>\n",
       "      <td>46364</td>\n",
       "      <td>46653</td>\n",
       "      <td>46938</td>\n",
       "      <td>47216</td>\n",
       "      <td>47488</td>\n",
       "      <td>47752</td>\n",
       "      <td>48007</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>NaN</td>\n",
       "      <td>Andorra</td>\n",
       "      <td>42.50630</td>\n",
       "      <td>1.521800</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>1199</td>\n",
       "      <td>1215</td>\n",
       "      <td>1215</td>\n",
       "      <td>1215</td>\n",
       "      <td>1261</td>\n",
       "      <td>1261</td>\n",
       "      <td>1301</td>\n",
       "      <td>1301</td>\n",
       "      <td>1344</td>\n",
       "      <td>1344</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>NaN</td>\n",
       "      <td>Angola</td>\n",
       "      <td>-11.20270</td>\n",
       "      <td>17.873900</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>2805</td>\n",
       "      <td>2876</td>\n",
       "      <td>2935</td>\n",
       "      <td>2965</td>\n",
       "      <td>2981</td>\n",
       "      <td>3033</td>\n",
       "      <td>3092</td>\n",
       "      <td>3217</td>\n",
       "      <td>3279</td>\n",
       "      <td>3335</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 239 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "  Province/State Country/Region       Lat       Long  1/22/20  1/23/20  \\\n",
       "0            NaN    Afghanistan  33.93911  67.709953        0        0   \n",
       "1            NaN        Albania  41.15330  20.168300        0        0   \n",
       "2            NaN        Algeria  28.03390   1.659600        0        0   \n",
       "3            NaN        Andorra  42.50630   1.521800        0        0   \n",
       "4            NaN         Angola -11.20270  17.873900        0        0   \n",
       "\n",
       "   1/24/20  1/25/20  1/26/20  1/27/20  ...  9/3/20  9/4/20  9/5/20  9/6/20  \\\n",
       "0        0        0        0        0  ...   38288   38304   38324   38398   \n",
       "1        0        0        0        0  ...    9844    9967   10102   10255   \n",
       "2        0        0        0        0  ...   45469   45773   46071   46364   \n",
       "3        0        0        0        0  ...    1199    1215    1215    1215   \n",
       "4        0        0        0        0  ...    2805    2876    2935    2965   \n",
       "\n",
       "   9/7/20  9/8/20  9/9/20  9/10/20  9/11/20  9/12/20  \n",
       "0   38494   38520   38544    38572    38606    38641  \n",
       "1   10406   10553   10704    10860    11021    11185  \n",
       "2   46653   46938   47216    47488    47752    48007  \n",
       "3    1261    1261    1301     1301     1344     1344  \n",
       "4    2981    3033    3092     3217     3279     3335  \n",
       "\n",
       "[5 rows x 239 columns]"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "time_series.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "## 資料整理：轉置"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "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>Province/State</th>\n",
       "      <th>Country/Region</th>\n",
       "      <th>Lat</th>\n",
       "      <th>Long</th>\n",
       "      <th>Date</th>\n",
       "      <th>Confirmed</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>NaN</td>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>33.93911</td>\n",
       "      <td>67.709953</td>\n",
       "      <td>1/22/20</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>NaN</td>\n",
       "      <td>Albania</td>\n",
       "      <td>41.15330</td>\n",
       "      <td>20.168300</td>\n",
       "      <td>1/22/20</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>NaN</td>\n",
       "      <td>Algeria</td>\n",
       "      <td>28.03390</td>\n",
       "      <td>1.659600</td>\n",
       "      <td>1/22/20</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>NaN</td>\n",
       "      <td>Andorra</td>\n",
       "      <td>42.50630</td>\n",
       "      <td>1.521800</td>\n",
       "      <td>1/22/20</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>NaN</td>\n",
       "      <td>Angola</td>\n",
       "      <td>-11.20270</td>\n",
       "      <td>17.873900</td>\n",
       "      <td>1/22/20</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Province/State Country/Region       Lat       Long     Date  Confirmed\n",
       "0            NaN    Afghanistan  33.93911  67.709953  1/22/20          0\n",
       "1            NaN        Albania  41.15330  20.168300  1/22/20          0\n",
       "2            NaN        Algeria  28.03390   1.659600  1/22/20          0\n",
       "3            NaN        Andorra  42.50630   1.521800  1/22/20          0\n",
       "4            NaN         Angola -11.20270  17.873900  1/22/20          0"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "id_cols = time_series.columns[:4]\n",
    "time_series_long = pd.melt(time_series, id_vars=id_cols, var_name='Date', value_name='Confirmed')\n",
    "time_series_long.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "## 資料整理：轉換 `Date` 型別"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 62510 entries, 0 to 62509\n",
      "Data columns (total 6 columns):\n",
      " #   Column          Non-Null Count  Dtype         \n",
      "---  ------          --------------  -----         \n",
      " 0   Province/State  19035 non-null  object        \n",
      " 1   Country/Region  62510 non-null  object        \n",
      " 2   Lat             62510 non-null  float64       \n",
      " 3   Long            62510 non-null  float64       \n",
      " 4   Date            62510 non-null  datetime64[ns]\n",
      " 5   Confirmed       62510 non-null  int64         \n",
      "dtypes: datetime64[ns](1), float64(2), int64(1), object(2)\n",
      "memory usage: 2.9+ MB\n"
     ]
    }
   ],
   "source": [
    "date = pd.to_datetime(time_series_long['Date'])\n",
    "time_series_long = time_series_long.drop('Date', axis=1)\n",
    "time_series_long.insert(4, 'Date', date)\n",
    "time_series_long.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "## 繪製兩個視覺化\n",
    "\n",
    "1. 台灣累計確診人數趨勢圖；\n",
    "2. 台灣每日新增確診人數長條圖。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "## 繪製台灣累計確診人數趨勢圖"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "## 將資料整理為 `ndarray` 或 `Series` 格式"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "tw = time_series_long[time_series_long['Country/Region'] == 'Taiwan*']\n",
    "x = tw['Date'].values\n",
    "y = tw['Confirmed'].values"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "## 呼叫 `plt.figure()` 展開「畫布物件」、呼叫 `plt.axes()` 展開「軸物件」"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "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()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "## 依照探索需求呼叫「軸物件」的作圖方法"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7fa4a9fd8e80>]"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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.plot(x, y)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "## 依照設計需求添加「軸物件」的元素"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7fa4a8bbe390>"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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.plot(x, y, label=\"Taiwan\")\n",
    "ax.set_xlabel('Date')\n",
    "ax.set_title('Cumulative COVID-19 confirmed cases')\n",
    "ax.legend(loc='upper left')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "## 呼叫 plt.show() 顯示圖形\n",
    "\n",
    "或者 `plt.savefig()` 儲存圖形。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "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.plot(x, y, label=\"Taiwan\")\n",
    "ax.set_xlabel('Date')\n",
    "ax.set_title('Cumulative COVID-19 confirmed cases')\n",
    "ax.legend(loc='upper left')\n",
    "plt.show() # plt.savefig('tw_covid19_time_series.png')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "## 繪製台灣每日新增確診人數長條圖"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "## 將資料整理為 `ndarray` 或 `Series` 格式"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "daily_increase = np.diff(tw['Confirmed'].values, n=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "## 呼叫 `plt.figure()` 展開「畫布物件」、呼叫 `plt.axes()` 展開「軸物件」"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "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()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "## 依照探索需求呼叫「軸物件」的作圖方法"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<BarContainer object of 234 artists>"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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.bar(x[1:], daily_increase)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "## 依照設計需求添加「軸物件」的元素"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7fa4ab164860>"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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.bar(x[1:], daily_increase, label='Taiwan')\n",
    "ax.set_title('Daily COVID-19 confirmed cases')\n",
    "ax.set_xlabel('Date')\n",
    "ax.set_ylabel('Daily Increase')\n",
    "ax.set_ylim(0, None)\n",
    "ax.legend(loc='upper right')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "## 呼叫 plt.show() 顯示圖形\n",
    "\n",
    "或者 `plt.savefig()` 儲存圖形。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "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.bar(x[1:], daily_increase, label='Taiwan')\n",
    "ax.set_title('Daily COVID-19 confirmed cases')\n",
    "ax.set_xlabel('Date')\n",
    "ax.set_ylabel('Daily Increase')\n",
    "ax.set_ylim(0, None)\n",
    "ax.legend(loc='upper right')\n",
    "plt.show() # plt.savefig('tw_covid19_daily_increase.png')"
   ]
  }
 ],
 "metadata": {
  "celltoolbar": "Slideshow",
  "kernelspec": {
   "display_name": "Python Data Analysis",
   "language": "python",
   "name": "pyda"
  },
  "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
}