DataAnalysis.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 資料分析與視覺化繪圖\n",
    "\n",
    "*  Numpy\n",
    "*  Pandas\n",
    "*  Matplotlib\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1. Numpy\n",
    "<B><span style=\"color:blue\">說明 :\n",
    "NumPy，全名為 Numerical Python 創建一種稱為 ndarray 的型別，彌補了原生 list 缺少的元素級別（element-wise）運算功能\n",
    "主要用於資料處理上。Numpy 底層以 C 和 Fortran 語言實作，能快速操作多重維度的陣列</span></B> \n",
    "   "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 1.0 安裝套件\n",
    "\n",
    "* Python 2+ : pip install numpy\n",
    "* Python 3+ : pip3 install numpy\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#### 1.1 引用套件"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import numpy as np"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 1.2 初始化陣列"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[10 20 20 10 20]\n",
      " [40 20 20 20 20]]\n"
     ]
    }
   ],
   "source": [
    "# 使用Python 列表建立陣列\n",
    "np.array([1,3,5])  #  [1 3 5]\n",
    "np.array([[1,2],[3,4],[5,6]])  # [[1 2] [3 4] [5 6]]\n",
    "\n",
    "# 使用Numpy arange函數建立連續數值陣列\n",
    "np.arange(1,10)  # [1 2 3 4 5 6 7 8 9]\n",
    "\n",
    "# 使用Numpy linspace函數建立連續等距數值陣列\n",
    "np.linspace(0,4,11) # [0.  0.4 0.8 1.2 1.6 2.  2.4 2.8 3.2 3.6 4. ]\n",
    "\n",
    "# 建立填滿 0 的陣列\n",
    "np.zeros([2, 3]) # [[0. 0. 0.] [0. 0. 0.]]\n",
    "\n",
    "# 建立填滿 1 的陣列\n",
    "np.ones([2, 3]) #[[1. 1. 1.] [1. 1. 1.]]\n",
    "\n",
    "# 建立對角線為1的對角矩陣陣列\n",
    "np.eye(10) \n",
    "\n",
    "#[[1. 0. 0. 0. 0. 0. 0. 0. 0. 0.]\n",
    "# [0. 1. 0. 0. 0. 0. 0. 0. 0. 0.]\n",
    "# [0. 0. 1. 0. 0. 0. 0. 0. 0. 0.]\n",
    "# [0. 0. 0. 1. 0. 0. 0. 0. 0. 0.]\n",
    "# [0. 0. 0. 0. 1. 0. 0. 0. 0. 0.]\n",
    "# [0. 0. 0. 0. 0. 1. 0. 0. 0. 0.]\n",
    "# [0. 0. 0. 0. 0. 0. 1. 0. 0. 0.]\n",
    "# [0. 0. 0. 0. 0. 0. 0. 1. 0. 0.]\n",
    "# [0. 0. 0. 0. 0. 0. 0. 0. 1. 0.]\n",
    "# [0. 0. 0. 0. 0. 0. 0. 0. 0. 1.]]\n",
    "\n",
    "#建立隨機數值陣列\n",
    "#rand : 给定維度生成[0,1]之間的數值陣列，包含0，不包含1\n",
    "np.random.rand(5) # [0.15385879, 0.64608619, 0.72079961, 0.3115263 , 0.6021007 ]\n",
    "np.random.rand(4,2)\n",
    "np.random.rand(4,3,2)\n",
    "\n",
    "#randn : 给定維度生成常態分佈的數值陣列 , N（0,1）\n",
    "np.random.randn(5) \n",
    "np.random.randn(4,2)\n",
    "np.random.randn(4,3,2)\n",
    "\n",
    "#randint : 给定維度生成隨機整數數值陣列 \n",
    "np.random.randint(6,size=5) \n",
    "np.random.randint(2,10)\n",
    "np.random.randint(-10,10,size=(2,2))\n",
    "\n",
    "#從给定的一维陣列中生成随數值\n",
    "demo_list = [10,20,30,40,50]\n",
    "#demo_list = ['Acer', 'ASUS','DELL','HP', 'APPLE']\n",
    "print(np.random.choice(demo_list,size=(2,5), p=[0.1,0.6,0.1,0.1,0.1]))\n",
    "\n",
    "#  從檔CSV案讀取資料\n",
    "#np.genfromtxt('data/NPdata.csv', delimiter=',')\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 1.3 陣列訪問"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0 1 2 3 4 5 6 7 8 9]\n",
      "np1[0] = 0 , np1[5] = 5\n",
      "np1[:5] =  [0 1 2 3 4]\n",
      "np1[3:7] =  [3 4 5 6]\n",
      "np1[6:] =  [6 7 8 9]\n",
      "[1 3 5]\n",
      "[1 3 5 7 9]\n",
      "[0 3 6 9]\n"
     ]
    }
   ],
   "source": [
    "#一維照列\n",
    "np1 = np.arange(0,10)\n",
    "print(np1)\n",
    "print(\"np1[0] = %d , np1[5] = %d\" % (np1[0],np1[5]))\n",
    "print(\"np1[:5] = \",np1[:5])\n",
    "print(\"np1[3:7] = \",np1[3:7])\n",
    "print(\"np1[6:] = \",np1[6:])\n",
    "print(np1[1:6:2])\n",
    "print(np1[1::2])\n",
    "print(np1[::3])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[False False False  True  True  True]\n"
     ]
    }
   ],
   "source": [
    "#使用布林遮罩來取值\n",
    "np3 = np.array([1, 2, 3, 4, 5, 6])\n",
    "print(np3 > 3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0 0 3 4 5 6]\n"
     ]
    }
   ],
   "source": [
    "np3[np3<3]=0\n",
    "print(np3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 1.4 改變數組的維度"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[[ 0  1  2]\n",
      "  [ 3  4  5]\n",
      "  [ 6  7  8]]\n",
      "\n",
      " [[ 9 10 11]\n",
      "  [12 13 14]\n",
      "  [15 16 17]]]\n"
     ]
    }
   ],
   "source": [
    "#使用reshape 函數\n",
    "\n",
    "#a = np.arange(18)\n",
    "#print(a)\n",
    "#print(a.reshape(2,3,3))\n",
    "\n",
    "a = np.arange(18).reshape(2,3,3)\n",
    "print(a)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ 0  1  2  3  4  5  6  7  8  9 10 11 12 13 14 15 16 17]\n",
      "[ 0  1  2  3  4  5  6  7  8  9 10 11 12 13 14 15 16 17]\n"
     ]
    }
   ],
   "source": [
    "#使用ravel函數 ,將數組轉為一維 , return view\n",
    "\n",
    "print(a.ravel())\n",
    "\n",
    "#使用 flatten 函數 ,將數組轉為一維, 使用記憶體來保存結果\n",
    "print(a.flatten())\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 1.5 組合數組"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0, 1, 2],\n",
       "       [3, 4, 5],\n",
       "       [6, 7, 8]])"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a = np.arange(9).reshape(3,3)\n",
    "a"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0,  3,  6],\n",
       "       [ 9, 12, 15],\n",
       "       [18, 21, 24]])"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 純量計算\n",
    "b = a * 3 \n",
    "b"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 1.7 矩陣運算"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### 1.7.1 矩陣加減\n",
    "\n",
    "* 矩陣加法有交換律,減法沒有交換律\n",
    "* 矩陣加法有結合律,減法沒有結合律"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[25 56 36]\n",
      " [30 48 46]\n",
      " [33  6 30]\n",
      " [15 15  2]]\n",
      "[[25 56 36]\n",
      " [30 48 46]\n",
      " [33  6 30]\n",
      " [15 15  2]]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "A = np.array([[12,23,14],[20,29,38],[3,6,10],[15,15,2]])\n",
    "B = np.array([[13,33,22],[10,19,8],[30,0,20],[0,0,0]])\n",
    "print(A+B)\n",
    "print(B+A)\n",
    "np.array_equal(A+B,B+A)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[ -1 -10  -8]\n",
      " [ 10  10  30]\n",
      " [-27   6 -10]\n",
      " [ 15  15   2]]\n",
      "[[  1  10   8]\n",
      " [-10 -10 -30]\n",
      " [ 27  -6  10]\n",
      " [-15 -15  -2]]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "False"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "A = np.array([[12,23,14],[20,29,38],[3,6,10],[15,15,2]])\n",
    "B = np.array([[13,33,22],[10,19,8],[30,0,20],[0,0,0]])\n",
    "print(A-B)\n",
    "print(B-A)\n",
    "np.array_equal(A-B,B-A)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[18 10]\n",
      " [-4  6]]\n",
      "[[18 10]\n",
      " [-4  6]]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "A = np.array([[5,3],[0,1]])\n",
    "B = np.array([[4,-1],[2,3]])\n",
    "C = np.array([[9,8],[-6,2]])\n",
    "print((A+B)+C)\n",
    "print(A+(B+C))\n",
    "\n",
    "np.array_equal((A+B)+C,A+(B+C))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.array_equal((A-B)-C,A-(B-C))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### 1.7.2 矩陣乘法\n",
    "\n",
    "* 矩陣A為 m x n、矩陣B為 p x q，n 與 p 要相等， A * B 才能進行運算\n",
    "* .dot() 函數來實現矩陣與矩陣的相乘\n",
    "* 矩陣相乘具有結合律,分配律"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[  35  240  -15]\n",
      " [  25 -115   45]\n",
      " [  60   32   72]]\n"
     ]
    }
   ],
   "source": [
    "A = np.array([\n",
    "    [10, -20, 15],\n",
    "    [0, 15, -5],\n",
    "    [2, 4, 6]\n",
    "])\n",
    "B = np.array([\n",
    "    [1, 2, -3],\n",
    "    [4, -5, 6],\n",
    "    [7, 8, 9]\n",
    "])\n",
    "\n",
    "print(A.dot(B))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[  1 -12]\n",
      " [-46 -48]]\n",
      "[[  1 -12]\n",
      " [-46 -48]]\n",
      "True\n"
     ]
    }
   ],
   "source": [
    "A = np.array([\n",
    "    [1, -1],\n",
    "    [2, 4]\n",
    "])\n",
    "B = np.array([\n",
    "    [3, 1],\n",
    "    [2, 0]\n",
    "])\n",
    "C = np.array([\n",
    "    [-4, -2],\n",
    "    [5, -10]\n",
    "])\n",
    "D = (A.dot(B)).dot(C)\n",
    "E = A.dot(B.dot(C))\n",
    "print(D)\n",
    "print(E)\n",
    "print(np.array_equal(D, E))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[ -8   9]\n",
      " [ 26 -42]]\n",
      "[[ -8   9]\n",
      " [ 26 -42]]\n",
      "True\n"
     ]
    }
   ],
   "source": [
    "A = np.array([\n",
    "    [1, -1],\n",
    "    [2, 4]\n",
    "])\n",
    "B = np.array([\n",
    "    [3, 1],\n",
    "    [2, 0]\n",
    "])\n",
    "C = np.array([\n",
    "    [-4, -2],\n",
    "    [5, -10]\n",
    "])\n",
    "\n",
    "D = A.dot(B + C)\n",
    "E = A.dot(B) + A.dot(C)\n",
    "print(D)\n",
    "print(E)\n",
    "print(np.array_equal(D, E))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### 1.7.3 轉置矩陣"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[1 3 5]\n",
      " [2 4 6]]\n"
     ]
    }
   ],
   "source": [
    "A = np.array([\n",
    "    [1, 2],\n",
    "    [3, 4],\n",
    "    [5, 6]\n",
    "])\n",
    "B = A.T\n",
    "print(B)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[ 8 12 16]\n",
      " [10 14 18]]\n",
      "[[ 8 12 16]\n",
      " [10 14 18]]\n",
      "True\n"
     ]
    }
   ],
   "source": [
    "A = np.array([\n",
    "    [1, 2],\n",
    "    [3, 4],\n",
    "    [5, 6]\n",
    "])\n",
    "B = np.array([\n",
    "    [7, 8],\n",
    "    [9, 10],\n",
    "    [11, 12]\n",
    "])\n",
    "\n",
    "C = (A + B).T\n",
    "D = A.T + B.T\n",
    "print(C)\n",
    "print(D)\n",
    "print(np.array_equal(C, D))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "True\n"
     ]
    }
   ],
   "source": [
    "A = np.array([\n",
    "    [1, 2],\n",
    "    [3, 4],\n",
    "    [5, 6]\n",
    "])\n",
    "B = np.array([\n",
    "    [1, 0, 2],\n",
    "    [4, 5, 6]\n",
    "])\n",
    "C = (A.dot(B)).T\n",
    "D = (B.T).dot(A.T)\n",
    "print(np.array_equal(C, D))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### 1.7.4 反矩陣\n",
    "* 反矩陣（Inverse）：如果矩陣 M 是一個可逆矩陣（invertible），則矩陣 M 與其反矩陣相乘之後可以得到一個單位矩陣 => 不是每個矩陣都具有反矩陣\n",
    "* np.linalg.inv() 函數計算反矩陣\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[-1.5  3.5]\n",
      " [-1.   2. ]]\n",
      "[[1. 0.]\n",
      " [0. 1.]]\n"
     ]
    }
   ],
   "source": [
    "A = np.array([\n",
    "    [4, -7],\n",
    "    [2, -3]\n",
    "])\n",
    "A_inv = np.linalg.inv(A)\n",
    "print(A_inv)\n",
    "print(A.dot(A_inv))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "矩陣 B 是不可逆矩陣\n"
     ]
    }
   ],
   "source": [
    "B = np.array([\n",
    "    [8, 2],\n",
    "    [12, 3]\n",
    "])\n",
    "try:\n",
    "    B_inv = np.linalg.inv(B)\n",
    "except np.linalg.LinAlgError:\n",
    "    print('矩陣 B 是不可逆矩陣')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2. Pandas\n",
    "<B>說明 :</B>\n",
    "1. Series: 類似於一維數組的對象，它由一組數據（各種NumPy數據類型）以及一組與之相關的數據標籤（即索引）組成\n",
    "2. Dataframe : 表格型的數據結構，它含有一組有序的列，每列可以是不同的值類型"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 2.0 安裝套件"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* Python 2+ : pip install pandas\n",
    "* Python 3+ : pip3 install pandas"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 2.1 引用套件"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 2.2 Series"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0     5\n",
      "1    10\n",
      "2   -10\n",
      "3    20\n",
      "dtype: int64\n",
      "[  5  10 -10  20]\n",
      "RangeIndex(start=0, stop=4, step=1)\n"
     ]
    }
   ],
   "source": [
    "#from pandas import Series\n",
    "import pandas as pd\n",
    "\n",
    "obj = pd.Series([5,10,-10,20])\n",
    "print(obj)\n",
    "print(obj.values)\n",
    "print(obj.index)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "d    10\n",
      "b    20\n",
      "a    -5\n",
      "c    30\n",
      "dtype: int64\n",
      "Index(['d', 'b', 'a', 'c'], dtype='object')\n",
      "-5\n",
      "a    -5\n",
      "c    30\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "#給定索引\n",
    "obj2 = pd.Series([10, 20, -5, 30], index=['d', 'b', 'a', 'c'])\n",
    "print(obj2)\n",
    "print(obj2.index)\n",
    "print(obj2['a'])\n",
    "print(obj2[['a','c']])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'dict'>\n",
      "Ohio      15000\n",
      "Oregon    16000\n",
      "Texas     21000\n",
      "Utah       5000\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "# Ditctionary 轉化 \n",
    "sdata = {'Ohio': 15000, 'Texas': 21000, 'Oregon': 16000, 'Utah': 5000}\n",
    "print(type(sdata))\n",
    "obj3 = pd.Series(sdata)\n",
    "print(obj3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'list'>\n",
      "California        NaN\n",
      "Ohio          15000.0\n",
      "Oregon        16000.0\n",
      "Texas         21000.0\n",
      "dtype: float64\n",
      "California     True\n",
      "Ohio          False\n",
      "Oregon        False\n",
      "Texas         False\n",
      "dtype: bool\n"
     ]
    }
   ],
   "source": [
    "# List轉化\n",
    "states = ['California', 'Ohio', 'Oregon', 'Texas']\n",
    "print(type(states))\n",
    "obj4 = pd.Series(sdata, index=states)\n",
    "print(obj4)\n",
    "print(pd.isnull(obj4))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "state\n",
       "California        NaN\n",
       "Ohio          15000.0\n",
       "Oregon        16000.0\n",
       "Texas         21000.0\n",
       "Name: population, dtype: float64"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "obj4.name = 'population'\n",
    "obj4.index.name = 'state'\n",
    "obj4"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "d    10\n",
      "b    20\n",
      "c    30\n",
      "dtype: int64\n",
      "d    20\n",
      "b    40\n",
      "c    60\n",
      "dtype: int64\n",
      "d    10\n",
      "b    20\n",
      "a     0\n",
      "c    30\n",
      "dtype: int64\n",
      "a     0\n",
      "b    20\n",
      "c    30\n",
      "d    10\n",
      "dtype: int64\n",
      "a     0\n",
      "d    10\n",
      "b    20\n",
      "c    30\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "#使用布林遮罩來取值\n",
    "obj2 = pd.Series([10, 20, -5, 30], index=['d', 'b', 'a', 'c'])\n",
    "print(obj2[obj2>0])\n",
    "print(obj2[obj2>0]*2)\n",
    "obj2[obj2<0] = 0\n",
    "print(obj2)\n",
    "\n",
    "# 依照index排序\n",
    "print(obj2.sort_index())\n",
    "\n",
    "#依照數值排序\n",
    "print(obj2.sort_values())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 2.3 DataFrame"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### 2.3.1 DataFrame"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>pop</th>\n",
       "      <th>state</th>\n",
       "      <th>year</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.5</td>\n",
       "      <td>Ohio</td>\n",
       "      <td>2000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.7</td>\n",
       "      <td>Ohio</td>\n",
       "      <td>2001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3.6</td>\n",
       "      <td>Ohio</td>\n",
       "      <td>2002</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2.4</td>\n",
       "      <td>Nevada</td>\n",
       "      <td>2001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2.9</td>\n",
       "      <td>Nevada</td>\n",
       "      <td>2002</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   pop   state  year\n",
       "0  1.5    Ohio  2000\n",
       "1  1.7    Ohio  2001\n",
       "2  3.6    Ohio  2002\n",
       "3  2.4  Nevada  2001\n",
       "4  2.9  Nevada  2002"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = {'state': ['Ohio', 'Ohio', 'Ohio', 'Nevada', 'Nevada'],\n",
    "        'year': [2000, 2001, 2002, 2001, 2002],\n",
    "        'pop': [1.5, 1.7, 3.6, 2.4, 2.9]}\n",
    "\n",
    "frame = pd.DataFrame(data)\n",
    "frame"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "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>year</th>\n",
       "      <th>pop</th>\n",
       "      <th>state</th>\n",
       "      <th>debt</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>one</th>\n",
       "      <td>2000</td>\n",
       "      <td>1.5</td>\n",
       "      <td>Ohio</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>two</th>\n",
       "      <td>2001</td>\n",
       "      <td>1.7</td>\n",
       "      <td>Ohio</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>three</th>\n",
       "      <td>2002</td>\n",
       "      <td>3.6</td>\n",
       "      <td>Ohio</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>four</th>\n",
       "      <td>2001</td>\n",
       "      <td>2.4</td>\n",
       "      <td>Nevada</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>five</th>\n",
       "      <td>2002</td>\n",
       "      <td>2.9</td>\n",
       "      <td>Nevada</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       year  pop   state debt\n",
       "one    2000  1.5    Ohio  NaN\n",
       "two    2001  1.7    Ohio  NaN\n",
       "three  2002  3.6    Ohio  NaN\n",
       "four   2001  2.4  Nevada  NaN\n",
       "five   2002  2.9  Nevada  NaN"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "frame = pd.DataFrame(data,columns=['year','pop','state','debt'],index=['one','two','three','four','five'])\n",
    "frame\n",
    "frame.columns\n",
    "frame['year']\n",
    "frame.year\n",
    "frame.loc['two']\n",
    "#del frame['debt']\n",
    "frame"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 2.3.2 讀取MysQL資料庫"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "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>ID</th>\n",
       "      <th>FIRST_NAME</th>\n",
       "      <th>LAST_NAME</th>\n",
       "      <th>AGE</th>\n",
       "      <th>SEX</th>\n",
       "      <th>INCOME</th>\n",
       "      <th>COUNTRY</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>Nick</td>\n",
       "      <td>Lin</td>\n",
       "      <td>32</td>\n",
       "      <td>M</td>\n",
       "      <td>80000.0</td>\n",
       "      <td>Taichung</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>David</td>\n",
       "      <td>Yang</td>\n",
       "      <td>45</td>\n",
       "      <td>M</td>\n",
       "      <td>55500.0</td>\n",
       "      <td>Taipei</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>Mark</td>\n",
       "      <td>Hu</td>\n",
       "      <td>28</td>\n",
       "      <td>M</td>\n",
       "      <td>33500.0</td>\n",
       "      <td>Taipei</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>Carol</td>\n",
       "      <td>Chen</td>\n",
       "      <td>25</td>\n",
       "      <td>F</td>\n",
       "      <td>42500.0</td>\n",
       "      <td>Taipei</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   ID FIRST_NAME LAST_NAME  AGE SEX   INCOME   COUNTRY\n",
       "0   1       Nick       Lin   32   M  80000.0  Taichung\n",
       "1   2      David      Yang   45   M  55500.0    Taipei\n",
       "2   3       Mark        Hu   28   M  33500.0    Taipei\n",
       "3   4      Carol      Chen   25   F  42500.0    Taipei"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import MySQLdb\n",
    "conn = MySQLdb.Connect(host=\"localhost\",user = \"root\",passwd =\"user\",db=\"Demo\",charset=\"utf8\")\n",
    "sql = \"SELECT * FROM EMPLOYEE\"\n",
    "df = pd.read_sql_query(sql,con=conn)\n",
    "df\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0     Nick\n",
       "1    David\n",
       "2     Mark\n",
       "3    Carol\n",
       "Name: FIRST_NAME, dtype: object"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['FIRST_NAME']\n",
    "#df.loc[2]\n",
    "#df.loc[2]['INCOME']\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 2.3.3 讀取CSV檔案\n",
    "\n",
    "* 資料來源 :gapminder.csv\n",
    "* 紀錄 142 個國家 1952 至 2007 年每五年的資訊\n",
    "\n",
    "Item | 名稱 | 描述\n",
    "--- | --- |---\n",
    "country | 國家 | factor with 142 levels\n",
    "continent | 洲別 | factor with 5 levels\n",
    "year | 年份 | ranges from 1952 to 2007 in increments of 5 years\n",
    "lifeEXp | 出生預期壽命 | life expectancy at birth, in years\n",
    "pop | 人口數 | population\n",
    "gdpPercap | 人均國內生產總值 | GDP per capita (US$, inflation-adjusted)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "\n",
    "csv_file = \"https://storage.googleapis.com/learn_pd_like_tidyverse/gapminder.csv\"\n",
    "gapminder = pd.read_csv(csv_file)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 2.3.4  Gapminder資料分析"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### 2.3.4.1  顯示型態 - type()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "<class 'pandas.core.series.Series'>\n"
     ]
    }
   ],
   "source": [
    "print(type(gapminder))\n",
    "print(type(gapminder['country']))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### 2.3.4.2  顯示前 N 筆資料 - head(N)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>country</th>\n",
       "      <th>continent</th>\n",
       "      <th>year</th>\n",
       "      <th>lifeExp</th>\n",
       "      <th>pop</th>\n",
       "      <th>gdpPercap</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>Asia</td>\n",
       "      <td>1952</td>\n",
       "      <td>28.801</td>\n",
       "      <td>8425333</td>\n",
       "      <td>779.445314</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>Asia</td>\n",
       "      <td>1957</td>\n",
       "      <td>30.332</td>\n",
       "      <td>9240934</td>\n",
       "      <td>820.853030</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>Asia</td>\n",
       "      <td>1962</td>\n",
       "      <td>31.997</td>\n",
       "      <td>10267083</td>\n",
       "      <td>853.100710</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>Asia</td>\n",
       "      <td>1967</td>\n",
       "      <td>34.020</td>\n",
       "      <td>11537966</td>\n",
       "      <td>836.197138</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>Asia</td>\n",
       "      <td>1972</td>\n",
       "      <td>36.088</td>\n",
       "      <td>13079460</td>\n",
       "      <td>739.981106</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       country continent  year  lifeExp       pop   gdpPercap\n",
       "0  Afghanistan      Asia  1952   28.801   8425333  779.445314\n",
       "1  Afghanistan      Asia  1957   30.332   9240934  820.853030\n",
       "2  Afghanistan      Asia  1962   31.997  10267083  853.100710\n",
       "3  Afghanistan      Asia  1967   34.020  11537966  836.197138\n",
       "4  Afghanistan      Asia  1972   36.088  13079460  739.981106"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 預設N為前五筆\n",
    "gapminder.head()\n",
    "#gapminder.head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### 2.3.4.2  顯示後 N 筆資料 - tail(N)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>country</th>\n",
       "      <th>continent</th>\n",
       "      <th>year</th>\n",
       "      <th>lifeExp</th>\n",
       "      <th>pop</th>\n",
       "      <th>gdpPercap</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1699</th>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>Africa</td>\n",
       "      <td>1987</td>\n",
       "      <td>62.351</td>\n",
       "      <td>9216418</td>\n",
       "      <td>706.157306</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1700</th>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>Africa</td>\n",
       "      <td>1992</td>\n",
       "      <td>60.377</td>\n",
       "      <td>10704340</td>\n",
       "      <td>693.420786</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1701</th>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>Africa</td>\n",
       "      <td>1997</td>\n",
       "      <td>46.809</td>\n",
       "      <td>11404948</td>\n",
       "      <td>792.449960</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1702</th>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>Africa</td>\n",
       "      <td>2002</td>\n",
       "      <td>39.989</td>\n",
       "      <td>11926563</td>\n",
       "      <td>672.038623</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1703</th>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>Africa</td>\n",
       "      <td>2007</td>\n",
       "      <td>43.487</td>\n",
       "      <td>12311143</td>\n",
       "      <td>469.709298</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       country continent  year  lifeExp       pop   gdpPercap\n",
       "1699  Zimbabwe    Africa  1987   62.351   9216418  706.157306\n",
       "1700  Zimbabwe    Africa  1992   60.377  10704340  693.420786\n",
       "1701  Zimbabwe    Africa  1997   46.809  11404948  792.449960\n",
       "1702  Zimbabwe    Africa  2002   39.989  11926563  672.038623\n",
       "1703  Zimbabwe    Africa  2007   43.487  12311143  469.709298"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 預設 N 為 五筆\n",
    "gapminder.tail()\n",
    "#gapminder.tail(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### 2.3.4.3  顯示欄位- DataFrame.columns()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['country', 'continent', 'year', 'lifeExp', 'pop', 'gdpPercap'], dtype='object')"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gapminder.columns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### 2.3.4.5  顯示列欄數- DataFrame.shape()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1704, 6)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gapminder.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### 2.3.4.6  顯示列索引資訊- DataFrame.index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RangeIndex(start=0, stop=1704, step=1)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gapminder.index"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### 2.3.4.7  顯示詳細資訊- DataFrame.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 1704 entries, 0 to 1703\n",
      "Data columns (total 6 columns):\n",
      "country      1704 non-null object\n",
      "continent    1704 non-null object\n",
      "year         1704 non-null int64\n",
      "lifeExp      1704 non-null float64\n",
      "pop          1704 non-null int64\n",
      "gdpPercap    1704 non-null float64\n",
      "dtypes: float64(2), int64(2), object(2)\n",
      "memory usage: 80.0+ KB\n"
     ]
    }
   ],
   "source": [
    "gapminder.info()\n",
    "#gapminder.info"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### 2.3.4.8  顯示各變數描述統計資訊- DataFrame.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>year</th>\n",
       "      <th>lifeExp</th>\n",
       "      <th>pop</th>\n",
       "      <th>gdpPercap</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>1704.00000</td>\n",
       "      <td>1704.000000</td>\n",
       "      <td>1.704000e+03</td>\n",
       "      <td>1704.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>1979.50000</td>\n",
       "      <td>59.474439</td>\n",
       "      <td>2.960121e+07</td>\n",
       "      <td>7215.327081</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>17.26533</td>\n",
       "      <td>12.917107</td>\n",
       "      <td>1.061579e+08</td>\n",
       "      <td>9857.454543</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1952.00000</td>\n",
       "      <td>23.599000</td>\n",
       "      <td>6.001100e+04</td>\n",
       "      <td>241.165877</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>1965.75000</td>\n",
       "      <td>48.198000</td>\n",
       "      <td>2.793664e+06</td>\n",
       "      <td>1202.060309</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>1979.50000</td>\n",
       "      <td>60.712500</td>\n",
       "      <td>7.023596e+06</td>\n",
       "      <td>3531.846989</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>1993.25000</td>\n",
       "      <td>70.845500</td>\n",
       "      <td>1.958522e+07</td>\n",
       "      <td>9325.462346</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>2007.00000</td>\n",
       "      <td>82.603000</td>\n",
       "      <td>1.318683e+09</td>\n",
       "      <td>113523.132900</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             year      lifeExp           pop      gdpPercap\n",
       "count  1704.00000  1704.000000  1.704000e+03    1704.000000\n",
       "mean   1979.50000    59.474439  2.960121e+07    7215.327081\n",
       "std      17.26533    12.917107  1.061579e+08    9857.454543\n",
       "min    1952.00000    23.599000  6.001100e+04     241.165877\n",
       "25%    1965.75000    48.198000  2.793664e+06    1202.060309\n",
       "50%    1979.50000    60.712500  7.023596e+06    3531.846989\n",
       "75%    1993.25000    70.845500  1.958522e+07    9325.462346\n",
       "max    2007.00000    82.603000  1.318683e+09  113523.132900"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gapminder.describe()\n",
    "#gapminder['gdpPercap'].describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### 2.3.4.9  顯示特定欄位資料"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>country</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Afghanistan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Afghanistan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Afghanistan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Afghanistan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Afghanistan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Afghanistan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Afghanistan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Afghanistan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Afghanistan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Afghanistan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Afghanistan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Afghanistan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Albania</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Albania</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Albania</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Albania</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>Albania</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>Albania</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>Albania</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>Albania</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>Albania</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>Albania</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>Albania</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>Albania</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>Algeria</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>Algeria</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>Algeria</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>Algeria</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>Algeria</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>Algeria</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1674</th>\n",
       "      <td>Yemen, Rep.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1675</th>\n",
       "      <td>Yemen, Rep.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1676</th>\n",
       "      <td>Yemen, Rep.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1677</th>\n",
       "      <td>Yemen, Rep.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1678</th>\n",
       "      <td>Yemen, Rep.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1679</th>\n",
       "      <td>Yemen, Rep.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1680</th>\n",
       "      <td>Zambia</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1681</th>\n",
       "      <td>Zambia</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1682</th>\n",
       "      <td>Zambia</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1683</th>\n",
       "      <td>Zambia</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1684</th>\n",
       "      <td>Zambia</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1685</th>\n",
       "      <td>Zambia</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1686</th>\n",
       "      <td>Zambia</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1687</th>\n",
       "      <td>Zambia</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1688</th>\n",
       "      <td>Zambia</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1689</th>\n",
       "      <td>Zambia</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1690</th>\n",
       "      <td>Zambia</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1691</th>\n",
       "      <td>Zambia</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1692</th>\n",
       "      <td>Zimbabwe</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1693</th>\n",
       "      <td>Zimbabwe</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1694</th>\n",
       "      <td>Zimbabwe</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1695</th>\n",
       "      <td>Zimbabwe</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1696</th>\n",
       "      <td>Zimbabwe</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1697</th>\n",
       "      <td>Zimbabwe</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1698</th>\n",
       "      <td>Zimbabwe</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1699</th>\n",
       "      <td>Zimbabwe</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1700</th>\n",
       "      <td>Zimbabwe</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1701</th>\n",
       "      <td>Zimbabwe</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1702</th>\n",
       "      <td>Zimbabwe</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1703</th>\n",
       "      <td>Zimbabwe</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1704 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          country\n",
       "0     Afghanistan\n",
       "1     Afghanistan\n",
       "2     Afghanistan\n",
       "3     Afghanistan\n",
       "4     Afghanistan\n",
       "5     Afghanistan\n",
       "6     Afghanistan\n",
       "7     Afghanistan\n",
       "8     Afghanistan\n",
       "9     Afghanistan\n",
       "10    Afghanistan\n",
       "11    Afghanistan\n",
       "12        Albania\n",
       "13        Albania\n",
       "14        Albania\n",
       "15        Albania\n",
       "16        Albania\n",
       "17        Albania\n",
       "18        Albania\n",
       "19        Albania\n",
       "20        Albania\n",
       "21        Albania\n",
       "22        Albania\n",
       "23        Albania\n",
       "24        Algeria\n",
       "25        Algeria\n",
       "26        Algeria\n",
       "27        Algeria\n",
       "28        Algeria\n",
       "29        Algeria\n",
       "...           ...\n",
       "1674  Yemen, Rep.\n",
       "1675  Yemen, Rep.\n",
       "1676  Yemen, Rep.\n",
       "1677  Yemen, Rep.\n",
       "1678  Yemen, Rep.\n",
       "1679  Yemen, Rep.\n",
       "1680       Zambia\n",
       "1681       Zambia\n",
       "1682       Zambia\n",
       "1683       Zambia\n",
       "1684       Zambia\n",
       "1685       Zambia\n",
       "1686       Zambia\n",
       "1687       Zambia\n",
       "1688       Zambia\n",
       "1689       Zambia\n",
       "1690       Zambia\n",
       "1691       Zambia\n",
       "1692     Zimbabwe\n",
       "1693     Zimbabwe\n",
       "1694     Zimbabwe\n",
       "1695     Zimbabwe\n",
       "1696     Zimbabwe\n",
       "1697     Zimbabwe\n",
       "1698     Zimbabwe\n",
       "1699     Zimbabwe\n",
       "1700     Zimbabwe\n",
       "1701     Zimbabwe\n",
       "1702     Zimbabwe\n",
       "1703     Zimbabwe\n",
       "\n",
       "[1704 rows x 1 columns]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gapminder[['country']]\n",
    "#gapminder[['country', 'continent']]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### 2.3.4.10  條件查詢,找尋Taiwan資料"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "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>country</th>\n",
       "      <th>continent</th>\n",
       "      <th>year</th>\n",
       "      <th>lifeExp</th>\n",
       "      <th>pop</th>\n",
       "      <th>gdpPercap</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1500</th>\n",
       "      <td>Taiwan</td>\n",
       "      <td>Asia</td>\n",
       "      <td>1952</td>\n",
       "      <td>58.50</td>\n",
       "      <td>8550362</td>\n",
       "      <td>1206.947913</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1501</th>\n",
       "      <td>Taiwan</td>\n",
       "      <td>Asia</td>\n",
       "      <td>1957</td>\n",
       "      <td>62.40</td>\n",
       "      <td>10164215</td>\n",
       "      <td>1507.861290</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1502</th>\n",
       "      <td>Taiwan</td>\n",
       "      <td>Asia</td>\n",
       "      <td>1962</td>\n",
       "      <td>65.20</td>\n",
       "      <td>11918938</td>\n",
       "      <td>1822.879028</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1503</th>\n",
       "      <td>Taiwan</td>\n",
       "      <td>Asia</td>\n",
       "      <td>1967</td>\n",
       "      <td>67.50</td>\n",
       "      <td>13648692</td>\n",
       "      <td>2643.858681</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1504</th>\n",
       "      <td>Taiwan</td>\n",
       "      <td>Asia</td>\n",
       "      <td>1972</td>\n",
       "      <td>69.39</td>\n",
       "      <td>15226039</td>\n",
       "      <td>4062.523897</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1505</th>\n",
       "      <td>Taiwan</td>\n",
       "      <td>Asia</td>\n",
       "      <td>1977</td>\n",
       "      <td>70.59</td>\n",
       "      <td>16785196</td>\n",
       "      <td>5596.519826</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1506</th>\n",
       "      <td>Taiwan</td>\n",
       "      <td>Asia</td>\n",
       "      <td>1982</td>\n",
       "      <td>72.16</td>\n",
       "      <td>18501390</td>\n",
       "      <td>7426.354774</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1507</th>\n",
       "      <td>Taiwan</td>\n",
       "      <td>Asia</td>\n",
       "      <td>1987</td>\n",
       "      <td>73.40</td>\n",
       "      <td>19757799</td>\n",
       "      <td>11054.561750</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1508</th>\n",
       "      <td>Taiwan</td>\n",
       "      <td>Asia</td>\n",
       "      <td>1992</td>\n",
       "      <td>74.26</td>\n",
       "      <td>20686918</td>\n",
       "      <td>15215.657900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1509</th>\n",
       "      <td>Taiwan</td>\n",
       "      <td>Asia</td>\n",
       "      <td>1997</td>\n",
       "      <td>75.25</td>\n",
       "      <td>21628605</td>\n",
       "      <td>20206.820980</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1510</th>\n",
       "      <td>Taiwan</td>\n",
       "      <td>Asia</td>\n",
       "      <td>2002</td>\n",
       "      <td>76.99</td>\n",
       "      <td>22454239</td>\n",
       "      <td>23235.423290</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1511</th>\n",
       "      <td>Taiwan</td>\n",
       "      <td>Asia</td>\n",
       "      <td>2007</td>\n",
       "      <td>78.40</td>\n",
       "      <td>23174294</td>\n",
       "      <td>28718.276840</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     country continent  year  lifeExp       pop     gdpPercap\n",
       "1500  Taiwan      Asia  1952    58.50   8550362   1206.947913\n",
       "1501  Taiwan      Asia  1957    62.40  10164215   1507.861290\n",
       "1502  Taiwan      Asia  1962    65.20  11918938   1822.879028\n",
       "1503  Taiwan      Asia  1967    67.50  13648692   2643.858681\n",
       "1504  Taiwan      Asia  1972    69.39  15226039   4062.523897\n",
       "1505  Taiwan      Asia  1977    70.59  16785196   5596.519826\n",
       "1506  Taiwan      Asia  1982    72.16  18501390   7426.354774\n",
       "1507  Taiwan      Asia  1987    73.40  19757799  11054.561750\n",
       "1508  Taiwan      Asia  1992    74.26  20686918  15215.657900\n",
       "1509  Taiwan      Asia  1997    75.25  21628605  20206.820980\n",
       "1510  Taiwan      Asia  2002    76.99  22454239  23235.423290\n",
       "1511  Taiwan      Asia  2007    78.40  23174294  28718.276840"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gapminder[gapminder['country'] == 'Taiwan']\n",
    "\n",
    "#gapminder[gapminder['country'] == 'Taiwan'][['country', 'continent','year','pop']]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### 2.3.4.11  新增欄位"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
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       "    }\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>country</th>\n",
       "      <th>continent</th>\n",
       "      <th>year</th>\n",
       "      <th>lifeExp</th>\n",
       "      <th>pop</th>\n",
       "      <th>gdpPercap</th>\n",
       "      <th>country_abb</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>Asia</td>\n",
       "      <td>1952</td>\n",
       "      <td>28.801</td>\n",
       "      <td>8425333</td>\n",
       "      <td>779.445314</td>\n",
       "      <td>Afg</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>Asia</td>\n",
       "      <td>1957</td>\n",
       "      <td>30.332</td>\n",
       "      <td>9240934</td>\n",
       "      <td>820.853030</td>\n",
       "      <td>Afg</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>Asia</td>\n",
       "      <td>1962</td>\n",
       "      <td>31.997</td>\n",
       "      <td>10267083</td>\n",
       "      <td>853.100710</td>\n",
       "      <td>Afg</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>Asia</td>\n",
       "      <td>1967</td>\n",
       "      <td>34.020</td>\n",
       "      <td>11537966</td>\n",
       "      <td>836.197138</td>\n",
       "      <td>Afg</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>Asia</td>\n",
       "      <td>1972</td>\n",
       "      <td>36.088</td>\n",
       "      <td>13079460</td>\n",
       "      <td>739.981106</td>\n",
       "      <td>Afg</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>Asia</td>\n",
       "      <td>1977</td>\n",
       "      <td>38.438</td>\n",
       "      <td>14880372</td>\n",
       "      <td>786.113360</td>\n",
       "      <td>Afg</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>Asia</td>\n",
       "      <td>1982</td>\n",
       "      <td>39.854</td>\n",
       "      <td>12881816</td>\n",
       "      <td>978.011439</td>\n",
       "      <td>Afg</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>Asia</td>\n",
       "      <td>1987</td>\n",
       "      <td>40.822</td>\n",
       "      <td>13867957</td>\n",
       "      <td>852.395945</td>\n",
       "      <td>Afg</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>Asia</td>\n",
       "      <td>1992</td>\n",
       "      <td>41.674</td>\n",
       "      <td>16317921</td>\n",
       "      <td>649.341395</td>\n",
       "      <td>Afg</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>Asia</td>\n",
       "      <td>1997</td>\n",
       "      <td>41.763</td>\n",
       "      <td>22227415</td>\n",
       "      <td>635.341351</td>\n",
       "      <td>Afg</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>Asia</td>\n",
       "      <td>2002</td>\n",
       "      <td>42.129</td>\n",
       "      <td>25268405</td>\n",
       "      <td>726.734055</td>\n",
       "      <td>Afg</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>Asia</td>\n",
       "      <td>2007</td>\n",
       "      <td>43.828</td>\n",
       "      <td>31889923</td>\n",
       "      <td>974.580338</td>\n",
       "      <td>Afg</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Albania</td>\n",
       "      <td>Europe</td>\n",
       "      <td>1952</td>\n",
       "      <td>55.230</td>\n",
       "      <td>1282697</td>\n",
       "      <td>1601.056136</td>\n",
       "      <td>Alb</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Albania</td>\n",
       "      <td>Europe</td>\n",
       "      <td>1957</td>\n",
       "      <td>59.280</td>\n",
       "      <td>1476505</td>\n",
       "      <td>1942.284244</td>\n",
       "      <td>Alb</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Albania</td>\n",
       "      <td>Europe</td>\n",
       "      <td>1962</td>\n",
       "      <td>64.820</td>\n",
       "      <td>1728137</td>\n",
       "      <td>2312.888958</td>\n",
       "      <td>Alb</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Albania</td>\n",
       "      <td>Europe</td>\n",
       "      <td>1967</td>\n",
       "      <td>66.220</td>\n",
       "      <td>1984060</td>\n",
       "      <td>2760.196931</td>\n",
       "      <td>Alb</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>Albania</td>\n",
       "      <td>Europe</td>\n",
       "      <td>1972</td>\n",
       "      <td>67.690</td>\n",
       "      <td>2263554</td>\n",
       "      <td>3313.422188</td>\n",
       "      <td>Alb</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>Albania</td>\n",
       "      <td>Europe</td>\n",
       "      <td>1977</td>\n",
       "      <td>68.930</td>\n",
       "      <td>2509048</td>\n",
       "      <td>3533.003910</td>\n",
       "      <td>Alb</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>Albania</td>\n",
       "      <td>Europe</td>\n",
       "      <td>1982</td>\n",
       "      <td>70.420</td>\n",
       "      <td>2780097</td>\n",
       "      <td>3630.880722</td>\n",
       "      <td>Alb</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>Albania</td>\n",
       "      <td>Europe</td>\n",
       "      <td>1987</td>\n",
       "      <td>72.000</td>\n",
       "      <td>3075321</td>\n",
       "      <td>3738.932735</td>\n",
       "      <td>Alb</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>Albania</td>\n",
       "      <td>Europe</td>\n",
       "      <td>1992</td>\n",
       "      <td>71.581</td>\n",
       "      <td>3326498</td>\n",
       "      <td>2497.437901</td>\n",
       "      <td>Alb</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>Albania</td>\n",
       "      <td>Europe</td>\n",
       "      <td>1997</td>\n",
       "      <td>72.950</td>\n",
       "      <td>3428038</td>\n",
       "      <td>3193.054604</td>\n",
       "      <td>Alb</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>Albania</td>\n",
       "      <td>Europe</td>\n",
       "      <td>2002</td>\n",
       "      <td>75.651</td>\n",
       "      <td>3508512</td>\n",
       "      <td>4604.211737</td>\n",
       "      <td>Alb</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>Albania</td>\n",
       "      <td>Europe</td>\n",
       "      <td>2007</td>\n",
       "      <td>76.423</td>\n",
       "      <td>3600523</td>\n",
       "      <td>5937.029526</td>\n",
       "      <td>Alb</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>Algeria</td>\n",
       "      <td>Africa</td>\n",
       "      <td>1952</td>\n",
       "      <td>43.077</td>\n",
       "      <td>9279525</td>\n",
       "      <td>2449.008185</td>\n",
       "      <td>Alg</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>Algeria</td>\n",
       "      <td>Africa</td>\n",
       "      <td>1957</td>\n",
       "      <td>45.685</td>\n",
       "      <td>10270856</td>\n",
       "      <td>3013.976023</td>\n",
       "      <td>Alg</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>Algeria</td>\n",
       "      <td>Africa</td>\n",
       "      <td>1962</td>\n",
       "      <td>48.303</td>\n",
       "      <td>11000948</td>\n",
       "      <td>2550.816880</td>\n",
       "      <td>Alg</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>Algeria</td>\n",
       "      <td>Africa</td>\n",
       "      <td>1967</td>\n",
       "      <td>51.407</td>\n",
       "      <td>12760499</td>\n",
       "      <td>3246.991771</td>\n",
       "      <td>Alg</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>Algeria</td>\n",
       "      <td>Africa</td>\n",
       "      <td>1972</td>\n",
       "      <td>54.518</td>\n",
       "      <td>14760787</td>\n",
       "      <td>4182.663766</td>\n",
       "      <td>Alg</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>Algeria</td>\n",
       "      <td>Africa</td>\n",
       "      <td>1977</td>\n",
       "      <td>58.014</td>\n",
       "      <td>17152804</td>\n",
       "      <td>4910.416756</td>\n",
       "      <td>Alg</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1674</th>\n",
       "      <td>Yemen, Rep.</td>\n",
       "      <td>Asia</td>\n",
       "      <td>1982</td>\n",
       "      <td>49.113</td>\n",
       "      <td>9657618</td>\n",
       "      <td>1977.557010</td>\n",
       "      <td>Yem</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1675</th>\n",
       "      <td>Yemen, Rep.</td>\n",
       "      <td>Asia</td>\n",
       "      <td>1987</td>\n",
       "      <td>52.922</td>\n",
       "      <td>11219340</td>\n",
       "      <td>1971.741538</td>\n",
       "      <td>Yem</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1676</th>\n",
       "      <td>Yemen, Rep.</td>\n",
       "      <td>Asia</td>\n",
       "      <td>1992</td>\n",
       "      <td>55.599</td>\n",
       "      <td>13367997</td>\n",
       "      <td>1879.496673</td>\n",
       "      <td>Yem</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1677</th>\n",
       "      <td>Yemen, Rep.</td>\n",
       "      <td>Asia</td>\n",
       "      <td>1997</td>\n",
       "      <td>58.020</td>\n",
       "      <td>15826497</td>\n",
       "      <td>2117.484526</td>\n",
       "      <td>Yem</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1678</th>\n",
       "      <td>Yemen, Rep.</td>\n",
       "      <td>Asia</td>\n",
       "      <td>2002</td>\n",
       "      <td>60.308</td>\n",
       "      <td>18701257</td>\n",
       "      <td>2234.820827</td>\n",
       "      <td>Yem</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1679</th>\n",
       "      <td>Yemen, Rep.</td>\n",
       "      <td>Asia</td>\n",
       "      <td>2007</td>\n",
       "      <td>62.698</td>\n",
       "      <td>22211743</td>\n",
       "      <td>2280.769906</td>\n",
       "      <td>Yem</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1680</th>\n",
       "      <td>Zambia</td>\n",
       "      <td>Africa</td>\n",
       "      <td>1952</td>\n",
       "      <td>42.038</td>\n",
       "      <td>2672000</td>\n",
       "      <td>1147.388831</td>\n",
       "      <td>Zam</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1681</th>\n",
       "      <td>Zambia</td>\n",
       "      <td>Africa</td>\n",
       "      <td>1957</td>\n",
       "      <td>44.077</td>\n",
       "      <td>3016000</td>\n",
       "      <td>1311.956766</td>\n",
       "      <td>Zam</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1682</th>\n",
       "      <td>Zambia</td>\n",
       "      <td>Africa</td>\n",
       "      <td>1962</td>\n",
       "      <td>46.023</td>\n",
       "      <td>3421000</td>\n",
       "      <td>1452.725766</td>\n",
       "      <td>Zam</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1683</th>\n",
       "      <td>Zambia</td>\n",
       "      <td>Africa</td>\n",
       "      <td>1967</td>\n",
       "      <td>47.768</td>\n",
       "      <td>3900000</td>\n",
       "      <td>1777.077318</td>\n",
       "      <td>Zam</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1684</th>\n",
       "      <td>Zambia</td>\n",
       "      <td>Africa</td>\n",
       "      <td>1972</td>\n",
       "      <td>50.107</td>\n",
       "      <td>4506497</td>\n",
       "      <td>1773.498265</td>\n",
       "      <td>Zam</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1685</th>\n",
       "      <td>Zambia</td>\n",
       "      <td>Africa</td>\n",
       "      <td>1977</td>\n",
       "      <td>51.386</td>\n",
       "      <td>5216550</td>\n",
       "      <td>1588.688299</td>\n",
       "      <td>Zam</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1686</th>\n",
       "      <td>Zambia</td>\n",
       "      <td>Africa</td>\n",
       "      <td>1982</td>\n",
       "      <td>51.821</td>\n",
       "      <td>6100407</td>\n",
       "      <td>1408.678565</td>\n",
       "      <td>Zam</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1687</th>\n",
       "      <td>Zambia</td>\n",
       "      <td>Africa</td>\n",
       "      <td>1987</td>\n",
       "      <td>50.821</td>\n",
       "      <td>7272406</td>\n",
       "      <td>1213.315116</td>\n",
       "      <td>Zam</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1688</th>\n",
       "      <td>Zambia</td>\n",
       "      <td>Africa</td>\n",
       "      <td>1992</td>\n",
       "      <td>46.100</td>\n",
       "      <td>8381163</td>\n",
       "      <td>1210.884633</td>\n",
       "      <td>Zam</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1689</th>\n",
       "      <td>Zambia</td>\n",
       "      <td>Africa</td>\n",
       "      <td>1997</td>\n",
       "      <td>40.238</td>\n",
       "      <td>9417789</td>\n",
       "      <td>1071.353818</td>\n",
       "      <td>Zam</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1690</th>\n",
       "      <td>Zambia</td>\n",
       "      <td>Africa</td>\n",
       "      <td>2002</td>\n",
       "      <td>39.193</td>\n",
       "      <td>10595811</td>\n",
       "      <td>1071.613938</td>\n",
       "      <td>Zam</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1691</th>\n",
       "      <td>Zambia</td>\n",
       "      <td>Africa</td>\n",
       "      <td>2007</td>\n",
       "      <td>42.384</td>\n",
       "      <td>11746035</td>\n",
       "      <td>1271.211593</td>\n",
       "      <td>Zam</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1692</th>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>Africa</td>\n",
       "      <td>1952</td>\n",
       "      <td>48.451</td>\n",
       "      <td>3080907</td>\n",
       "      <td>406.884115</td>\n",
       "      <td>Zim</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1693</th>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>Africa</td>\n",
       "      <td>1957</td>\n",
       "      <td>50.469</td>\n",
       "      <td>3646340</td>\n",
       "      <td>518.764268</td>\n",
       "      <td>Zim</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1694</th>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>Africa</td>\n",
       "      <td>1962</td>\n",
       "      <td>52.358</td>\n",
       "      <td>4277736</td>\n",
       "      <td>527.272182</td>\n",
       "      <td>Zim</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1695</th>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>Africa</td>\n",
       "      <td>1967</td>\n",
       "      <td>53.995</td>\n",
       "      <td>4995432</td>\n",
       "      <td>569.795071</td>\n",
       "      <td>Zim</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1696</th>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>Africa</td>\n",
       "      <td>1972</td>\n",
       "      <td>55.635</td>\n",
       "      <td>5861135</td>\n",
       "      <td>799.362176</td>\n",
       "      <td>Zim</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1697</th>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>Africa</td>\n",
       "      <td>1977</td>\n",
       "      <td>57.674</td>\n",
       "      <td>6642107</td>\n",
       "      <td>685.587682</td>\n",
       "      <td>Zim</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1698</th>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>Africa</td>\n",
       "      <td>1982</td>\n",
       "      <td>60.363</td>\n",
       "      <td>7636524</td>\n",
       "      <td>788.855041</td>\n",
       "      <td>Zim</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1699</th>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>Africa</td>\n",
       "      <td>1987</td>\n",
       "      <td>62.351</td>\n",
       "      <td>9216418</td>\n",
       "      <td>706.157306</td>\n",
       "      <td>Zim</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1700</th>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>Africa</td>\n",
       "      <td>1992</td>\n",
       "      <td>60.377</td>\n",
       "      <td>10704340</td>\n",
       "      <td>693.420786</td>\n",
       "      <td>Zim</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1701</th>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>Africa</td>\n",
       "      <td>1997</td>\n",
       "      <td>46.809</td>\n",
       "      <td>11404948</td>\n",
       "      <td>792.449960</td>\n",
       "      <td>Zim</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1702</th>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>Africa</td>\n",
       "      <td>2002</td>\n",
       "      <td>39.989</td>\n",
       "      <td>11926563</td>\n",
       "      <td>672.038623</td>\n",
       "      <td>Zim</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1703</th>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>Africa</td>\n",
       "      <td>2007</td>\n",
       "      <td>43.487</td>\n",
       "      <td>12311143</td>\n",
       "      <td>469.709298</td>\n",
       "      <td>Zim</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1704 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          country continent  year  lifeExp       pop    gdpPercap country_abb\n",
       "0     Afghanistan      Asia  1952   28.801   8425333   779.445314         Afg\n",
       "1     Afghanistan      Asia  1957   30.332   9240934   820.853030         Afg\n",
       "2     Afghanistan      Asia  1962   31.997  10267083   853.100710         Afg\n",
       "3     Afghanistan      Asia  1967   34.020  11537966   836.197138         Afg\n",
       "4     Afghanistan      Asia  1972   36.088  13079460   739.981106         Afg\n",
       "5     Afghanistan      Asia  1977   38.438  14880372   786.113360         Afg\n",
       "6     Afghanistan      Asia  1982   39.854  12881816   978.011439         Afg\n",
       "7     Afghanistan      Asia  1987   40.822  13867957   852.395945         Afg\n",
       "8     Afghanistan      Asia  1992   41.674  16317921   649.341395         Afg\n",
       "9     Afghanistan      Asia  1997   41.763  22227415   635.341351         Afg\n",
       "10    Afghanistan      Asia  2002   42.129  25268405   726.734055         Afg\n",
       "11    Afghanistan      Asia  2007   43.828  31889923   974.580338         Afg\n",
       "12        Albania    Europe  1952   55.230   1282697  1601.056136         Alb\n",
       "13        Albania    Europe  1957   59.280   1476505  1942.284244         Alb\n",
       "14        Albania    Europe  1962   64.820   1728137  2312.888958         Alb\n",
       "15        Albania    Europe  1967   66.220   1984060  2760.196931         Alb\n",
       "16        Albania    Europe  1972   67.690   2263554  3313.422188         Alb\n",
       "17        Albania    Europe  1977   68.930   2509048  3533.003910         Alb\n",
       "18        Albania    Europe  1982   70.420   2780097  3630.880722         Alb\n",
       "19        Albania    Europe  1987   72.000   3075321  3738.932735         Alb\n",
       "20        Albania    Europe  1992   71.581   3326498  2497.437901         Alb\n",
       "21        Albania    Europe  1997   72.950   3428038  3193.054604         Alb\n",
       "22        Albania    Europe  2002   75.651   3508512  4604.211737         Alb\n",
       "23        Albania    Europe  2007   76.423   3600523  5937.029526         Alb\n",
       "24        Algeria    Africa  1952   43.077   9279525  2449.008185         Alg\n",
       "25        Algeria    Africa  1957   45.685  10270856  3013.976023         Alg\n",
       "26        Algeria    Africa  1962   48.303  11000948  2550.816880         Alg\n",
       "27        Algeria    Africa  1967   51.407  12760499  3246.991771         Alg\n",
       "28        Algeria    Africa  1972   54.518  14760787  4182.663766         Alg\n",
       "29        Algeria    Africa  1977   58.014  17152804  4910.416756         Alg\n",
       "...           ...       ...   ...      ...       ...          ...         ...\n",
       "1674  Yemen, Rep.      Asia  1982   49.113   9657618  1977.557010         Yem\n",
       "1675  Yemen, Rep.      Asia  1987   52.922  11219340  1971.741538         Yem\n",
       "1676  Yemen, Rep.      Asia  1992   55.599  13367997  1879.496673         Yem\n",
       "1677  Yemen, Rep.      Asia  1997   58.020  15826497  2117.484526         Yem\n",
       "1678  Yemen, Rep.      Asia  2002   60.308  18701257  2234.820827         Yem\n",
       "1679  Yemen, Rep.      Asia  2007   62.698  22211743  2280.769906         Yem\n",
       "1680       Zambia    Africa  1952   42.038   2672000  1147.388831         Zam\n",
       "1681       Zambia    Africa  1957   44.077   3016000  1311.956766         Zam\n",
       "1682       Zambia    Africa  1962   46.023   3421000  1452.725766         Zam\n",
       "1683       Zambia    Africa  1967   47.768   3900000  1777.077318         Zam\n",
       "1684       Zambia    Africa  1972   50.107   4506497  1773.498265         Zam\n",
       "1685       Zambia    Africa  1977   51.386   5216550  1588.688299         Zam\n",
       "1686       Zambia    Africa  1982   51.821   6100407  1408.678565         Zam\n",
       "1687       Zambia    Africa  1987   50.821   7272406  1213.315116         Zam\n",
       "1688       Zambia    Africa  1992   46.100   8381163  1210.884633         Zam\n",
       "1689       Zambia    Africa  1997   40.238   9417789  1071.353818         Zam\n",
       "1690       Zambia    Africa  2002   39.193  10595811  1071.613938         Zam\n",
       "1691       Zambia    Africa  2007   42.384  11746035  1271.211593         Zam\n",
       "1692     Zimbabwe    Africa  1952   48.451   3080907   406.884115         Zim\n",
       "1693     Zimbabwe    Africa  1957   50.469   3646340   518.764268         Zim\n",
       "1694     Zimbabwe    Africa  1962   52.358   4277736   527.272182         Zim\n",
       "1695     Zimbabwe    Africa  1967   53.995   4995432   569.795071         Zim\n",
       "1696     Zimbabwe    Africa  1972   55.635   5861135   799.362176         Zim\n",
       "1697     Zimbabwe    Africa  1977   57.674   6642107   685.587682         Zim\n",
       "1698     Zimbabwe    Africa  1982   60.363   7636524   788.855041         Zim\n",
       "1699     Zimbabwe    Africa  1987   62.351   9216418   706.157306         Zim\n",
       "1700     Zimbabwe    Africa  1992   60.377  10704340   693.420786         Zim\n",
       "1701     Zimbabwe    Africa  1997   46.809  11404948   792.449960         Zim\n",
       "1702     Zimbabwe    Africa  2002   39.989  11926563   672.038623         Zim\n",
       "1703     Zimbabwe    Africa  2007   43.487  12311143   469.709298         Zim\n",
       "\n",
       "[1704 rows x 7 columns]"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gapminder['country_abb'] = gapminder['country'].apply(lambda x: x[:3])\n",
    "gapminder"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### 2.3.4.12  刪除欄位\n",
    "\n",
    "dataframe.drop() : axis = 0 表示要刪除觀測值（row），axis = 1 表示要刪除欄位（column）。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "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>country</th>\n",
       "      <th>continent</th>\n",
       "      <th>year</th>\n",
       "      <th>lifeExp</th>\n",
       "      <th>pop</th>\n",
       "      <th>gdpPercap</th>\n",
       "      <th>country_abb</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>Asia</td>\n",
       "      <td>1957</td>\n",
       "      <td>30.332</td>\n",
       "      <td>9240934</td>\n",
       "      <td>820.853030</td>\n",
       "      <td>Afg</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>Asia</td>\n",
       "      <td>1962</td>\n",
       "      <td>31.997</td>\n",
       "      <td>10267083</td>\n",
       "      <td>853.100710</td>\n",
       "      <td>Afg</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>Asia</td>\n",
       "      <td>1967</td>\n",
       "      <td>34.020</td>\n",
       "      <td>11537966</td>\n",
       "      <td>836.197138</td>\n",
       "      <td>Afg</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>Asia</td>\n",
       "      <td>1972</td>\n",
       "      <td>36.088</td>\n",
       "      <td>13079460</td>\n",
       "      <td>739.981106</td>\n",
       "      <td>Afg</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>Asia</td>\n",
       "      <td>1977</td>\n",
       "      <td>38.438</td>\n",
       "      <td>14880372</td>\n",
       "      <td>786.113360</td>\n",
       "      <td>Afg</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>Asia</td>\n",
       "      <td>1982</td>\n",
       "      <td>39.854</td>\n",
       "      <td>12881816</td>\n",
       "      <td>978.011439</td>\n",
       "      <td>Afg</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>Asia</td>\n",
       "      <td>1987</td>\n",
       "      <td>40.822</td>\n",
       "      <td>13867957</td>\n",
       "      <td>852.395945</td>\n",
       "      <td>Afg</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>Asia</td>\n",
       "      <td>1992</td>\n",
       "      <td>41.674</td>\n",
       "      <td>16317921</td>\n",
       "      <td>649.341395</td>\n",
       "      <td>Afg</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>Asia</td>\n",
       "      <td>1997</td>\n",
       "      <td>41.763</td>\n",
       "      <td>22227415</td>\n",
       "      <td>635.341351</td>\n",
       "      <td>Afg</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>Asia</td>\n",
       "      <td>2002</td>\n",
       "      <td>42.129</td>\n",
       "      <td>25268405</td>\n",
       "      <td>726.734055</td>\n",
       "      <td>Afg</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>Asia</td>\n",
       "      <td>2007</td>\n",
       "      <td>43.828</td>\n",
       "      <td>31889923</td>\n",
       "      <td>974.580338</td>\n",
       "      <td>Afg</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Albania</td>\n",
       "      <td>Europe</td>\n",
       "      <td>1952</td>\n",
       "      <td>55.230</td>\n",
       "      <td>1282697</td>\n",
       "      <td>1601.056136</td>\n",
       "      <td>Alb</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Albania</td>\n",
       "      <td>Europe</td>\n",
       "      <td>1957</td>\n",
       "      <td>59.280</td>\n",
       "      <td>1476505</td>\n",
       "      <td>1942.284244</td>\n",
       "      <td>Alb</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Albania</td>\n",
       "      <td>Europe</td>\n",
       "      <td>1962</td>\n",
       "      <td>64.820</td>\n",
       "      <td>1728137</td>\n",
       "      <td>2312.888958</td>\n",
       "      <td>Alb</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Albania</td>\n",
       "      <td>Europe</td>\n",
       "      <td>1967</td>\n",
       "      <td>66.220</td>\n",
       "      <td>1984060</td>\n",
       "      <td>2760.196931</td>\n",
       "      <td>Alb</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>Albania</td>\n",
       "      <td>Europe</td>\n",
       "      <td>1972</td>\n",
       "      <td>67.690</td>\n",
       "      <td>2263554</td>\n",
       "      <td>3313.422188</td>\n",
       "      <td>Alb</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>Albania</td>\n",
       "      <td>Europe</td>\n",
       "      <td>1977</td>\n",
       "      <td>68.930</td>\n",
       "      <td>2509048</td>\n",
       "      <td>3533.003910</td>\n",
       "      <td>Alb</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>Albania</td>\n",
       "      <td>Europe</td>\n",
       "      <td>1982</td>\n",
       "      <td>70.420</td>\n",
       "      <td>2780097</td>\n",
       "      <td>3630.880722</td>\n",
       "      <td>Alb</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>Albania</td>\n",
       "      <td>Europe</td>\n",
       "      <td>1987</td>\n",
       "      <td>72.000</td>\n",
       "      <td>3075321</td>\n",
       "      <td>3738.932735</td>\n",
       "      <td>Alb</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>Albania</td>\n",
       "      <td>Europe</td>\n",
       "      <td>1992</td>\n",
       "      <td>71.581</td>\n",
       "      <td>3326498</td>\n",
       "      <td>2497.437901</td>\n",
       "      <td>Alb</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>Albania</td>\n",
       "      <td>Europe</td>\n",
       "      <td>1997</td>\n",
       "      <td>72.950</td>\n",
       "      <td>3428038</td>\n",
       "      <td>3193.054604</td>\n",
       "      <td>Alb</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>Albania</td>\n",
       "      <td>Europe</td>\n",
       "      <td>2002</td>\n",
       "      <td>75.651</td>\n",
       "      <td>3508512</td>\n",
       "      <td>4604.211737</td>\n",
       "      <td>Alb</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>Albania</td>\n",
       "      <td>Europe</td>\n",
       "      <td>2007</td>\n",
       "      <td>76.423</td>\n",
       "      <td>3600523</td>\n",
       "      <td>5937.029526</td>\n",
       "      <td>Alb</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>Algeria</td>\n",
       "      <td>Africa</td>\n",
       "      <td>1952</td>\n",
       "      <td>43.077</td>\n",
       "      <td>9279525</td>\n",
       "      <td>2449.008185</td>\n",
       "      <td>Alg</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>Algeria</td>\n",
       "      <td>Africa</td>\n",
       "      <td>1957</td>\n",
       "      <td>45.685</td>\n",
       "      <td>10270856</td>\n",
       "      <td>3013.976023</td>\n",
       "      <td>Alg</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>Algeria</td>\n",
       "      <td>Africa</td>\n",
       "      <td>1962</td>\n",
       "      <td>48.303</td>\n",
       "      <td>11000948</td>\n",
       "      <td>2550.816880</td>\n",
       "      <td>Alg</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>Algeria</td>\n",
       "      <td>Africa</td>\n",
       "      <td>1967</td>\n",
       "      <td>51.407</td>\n",
       "      <td>12760499</td>\n",
       "      <td>3246.991771</td>\n",
       "      <td>Alg</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>Algeria</td>\n",
       "      <td>Africa</td>\n",
       "      <td>1972</td>\n",
       "      <td>54.518</td>\n",
       "      <td>14760787</td>\n",
       "      <td>4182.663766</td>\n",
       "      <td>Alg</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>Algeria</td>\n",
       "      <td>Africa</td>\n",
       "      <td>1977</td>\n",
       "      <td>58.014</td>\n",
       "      <td>17152804</td>\n",
       "      <td>4910.416756</td>\n",
       "      <td>Alg</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>Algeria</td>\n",
       "      <td>Africa</td>\n",
       "      <td>1982</td>\n",
       "      <td>61.368</td>\n",
       "      <td>20033753</td>\n",
       "      <td>5745.160213</td>\n",
       "      <td>Alg</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1674</th>\n",
       "      <td>Yemen, Rep.</td>\n",
       "      <td>Asia</td>\n",
       "      <td>1982</td>\n",
       "      <td>49.113</td>\n",
       "      <td>9657618</td>\n",
       "      <td>1977.557010</td>\n",
       "      <td>Yem</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1675</th>\n",
       "      <td>Yemen, Rep.</td>\n",
       "      <td>Asia</td>\n",
       "      <td>1987</td>\n",
       "      <td>52.922</td>\n",
       "      <td>11219340</td>\n",
       "      <td>1971.741538</td>\n",
       "      <td>Yem</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1676</th>\n",
       "      <td>Yemen, Rep.</td>\n",
       "      <td>Asia</td>\n",
       "      <td>1992</td>\n",
       "      <td>55.599</td>\n",
       "      <td>13367997</td>\n",
       "      <td>1879.496673</td>\n",
       "      <td>Yem</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1677</th>\n",
       "      <td>Yemen, Rep.</td>\n",
       "      <td>Asia</td>\n",
       "      <td>1997</td>\n",
       "      <td>58.020</td>\n",
       "      <td>15826497</td>\n",
       "      <td>2117.484526</td>\n",
       "      <td>Yem</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1678</th>\n",
       "      <td>Yemen, Rep.</td>\n",
       "      <td>Asia</td>\n",
       "      <td>2002</td>\n",
       "      <td>60.308</td>\n",
       "      <td>18701257</td>\n",
       "      <td>2234.820827</td>\n",
       "      <td>Yem</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1679</th>\n",
       "      <td>Yemen, Rep.</td>\n",
       "      <td>Asia</td>\n",
       "      <td>2007</td>\n",
       "      <td>62.698</td>\n",
       "      <td>22211743</td>\n",
       "      <td>2280.769906</td>\n",
       "      <td>Yem</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1680</th>\n",
       "      <td>Zambia</td>\n",
       "      <td>Africa</td>\n",
       "      <td>1952</td>\n",
       "      <td>42.038</td>\n",
       "      <td>2672000</td>\n",
       "      <td>1147.388831</td>\n",
       "      <td>Zam</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1681</th>\n",
       "      <td>Zambia</td>\n",
       "      <td>Africa</td>\n",
       "      <td>1957</td>\n",
       "      <td>44.077</td>\n",
       "      <td>3016000</td>\n",
       "      <td>1311.956766</td>\n",
       "      <td>Zam</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1682</th>\n",
       "      <td>Zambia</td>\n",
       "      <td>Africa</td>\n",
       "      <td>1962</td>\n",
       "      <td>46.023</td>\n",
       "      <td>3421000</td>\n",
       "      <td>1452.725766</td>\n",
       "      <td>Zam</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1683</th>\n",
       "      <td>Zambia</td>\n",
       "      <td>Africa</td>\n",
       "      <td>1967</td>\n",
       "      <td>47.768</td>\n",
       "      <td>3900000</td>\n",
       "      <td>1777.077318</td>\n",
       "      <td>Zam</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1684</th>\n",
       "      <td>Zambia</td>\n",
       "      <td>Africa</td>\n",
       "      <td>1972</td>\n",
       "      <td>50.107</td>\n",
       "      <td>4506497</td>\n",
       "      <td>1773.498265</td>\n",
       "      <td>Zam</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1685</th>\n",
       "      <td>Zambia</td>\n",
       "      <td>Africa</td>\n",
       "      <td>1977</td>\n",
       "      <td>51.386</td>\n",
       "      <td>5216550</td>\n",
       "      <td>1588.688299</td>\n",
       "      <td>Zam</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1686</th>\n",
       "      <td>Zambia</td>\n",
       "      <td>Africa</td>\n",
       "      <td>1982</td>\n",
       "      <td>51.821</td>\n",
       "      <td>6100407</td>\n",
       "      <td>1408.678565</td>\n",
       "      <td>Zam</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1687</th>\n",
       "      <td>Zambia</td>\n",
       "      <td>Africa</td>\n",
       "      <td>1987</td>\n",
       "      <td>50.821</td>\n",
       "      <td>7272406</td>\n",
       "      <td>1213.315116</td>\n",
       "      <td>Zam</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1688</th>\n",
       "      <td>Zambia</td>\n",
       "      <td>Africa</td>\n",
       "      <td>1992</td>\n",
       "      <td>46.100</td>\n",
       "      <td>8381163</td>\n",
       "      <td>1210.884633</td>\n",
       "      <td>Zam</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1689</th>\n",
       "      <td>Zambia</td>\n",
       "      <td>Africa</td>\n",
       "      <td>1997</td>\n",
       "      <td>40.238</td>\n",
       "      <td>9417789</td>\n",
       "      <td>1071.353818</td>\n",
       "      <td>Zam</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1690</th>\n",
       "      <td>Zambia</td>\n",
       "      <td>Africa</td>\n",
       "      <td>2002</td>\n",
       "      <td>39.193</td>\n",
       "      <td>10595811</td>\n",
       "      <td>1071.613938</td>\n",
       "      <td>Zam</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1691</th>\n",
       "      <td>Zambia</td>\n",
       "      <td>Africa</td>\n",
       "      <td>2007</td>\n",
       "      <td>42.384</td>\n",
       "      <td>11746035</td>\n",
       "      <td>1271.211593</td>\n",
       "      <td>Zam</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1692</th>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>Africa</td>\n",
       "      <td>1952</td>\n",
       "      <td>48.451</td>\n",
       "      <td>3080907</td>\n",
       "      <td>406.884115</td>\n",
       "      <td>Zim</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1693</th>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>Africa</td>\n",
       "      <td>1957</td>\n",
       "      <td>50.469</td>\n",
       "      <td>3646340</td>\n",
       "      <td>518.764268</td>\n",
       "      <td>Zim</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1694</th>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>Africa</td>\n",
       "      <td>1962</td>\n",
       "      <td>52.358</td>\n",
       "      <td>4277736</td>\n",
       "      <td>527.272182</td>\n",
       "      <td>Zim</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1695</th>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>Africa</td>\n",
       "      <td>1967</td>\n",
       "      <td>53.995</td>\n",
       "      <td>4995432</td>\n",
       "      <td>569.795071</td>\n",
       "      <td>Zim</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1696</th>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>Africa</td>\n",
       "      <td>1972</td>\n",
       "      <td>55.635</td>\n",
       "      <td>5861135</td>\n",
       "      <td>799.362176</td>\n",
       "      <td>Zim</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1697</th>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>Africa</td>\n",
       "      <td>1977</td>\n",
       "      <td>57.674</td>\n",
       "      <td>6642107</td>\n",
       "      <td>685.587682</td>\n",
       "      <td>Zim</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1698</th>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>Africa</td>\n",
       "      <td>1982</td>\n",
       "      <td>60.363</td>\n",
       "      <td>7636524</td>\n",
       "      <td>788.855041</td>\n",
       "      <td>Zim</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1699</th>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>Africa</td>\n",
       "      <td>1987</td>\n",
       "      <td>62.351</td>\n",
       "      <td>9216418</td>\n",
       "      <td>706.157306</td>\n",
       "      <td>Zim</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1700</th>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>Africa</td>\n",
       "      <td>1992</td>\n",
       "      <td>60.377</td>\n",
       "      <td>10704340</td>\n",
       "      <td>693.420786</td>\n",
       "      <td>Zim</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1701</th>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>Africa</td>\n",
       "      <td>1997</td>\n",
       "      <td>46.809</td>\n",
       "      <td>11404948</td>\n",
       "      <td>792.449960</td>\n",
       "      <td>Zim</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1702</th>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>Africa</td>\n",
       "      <td>2002</td>\n",
       "      <td>39.989</td>\n",
       "      <td>11926563</td>\n",
       "      <td>672.038623</td>\n",
       "      <td>Zim</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1703</th>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>Africa</td>\n",
       "      <td>2007</td>\n",
       "      <td>43.487</td>\n",
       "      <td>12311143</td>\n",
       "      <td>469.709298</td>\n",
       "      <td>Zim</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1703 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          country continent  year  lifeExp       pop    gdpPercap country_abb\n",
       "1     Afghanistan      Asia  1957   30.332   9240934   820.853030         Afg\n",
       "2     Afghanistan      Asia  1962   31.997  10267083   853.100710         Afg\n",
       "3     Afghanistan      Asia  1967   34.020  11537966   836.197138         Afg\n",
       "4     Afghanistan      Asia  1972   36.088  13079460   739.981106         Afg\n",
       "5     Afghanistan      Asia  1977   38.438  14880372   786.113360         Afg\n",
       "6     Afghanistan      Asia  1982   39.854  12881816   978.011439         Afg\n",
       "7     Afghanistan      Asia  1987   40.822  13867957   852.395945         Afg\n",
       "8     Afghanistan      Asia  1992   41.674  16317921   649.341395         Afg\n",
       "9     Afghanistan      Asia  1997   41.763  22227415   635.341351         Afg\n",
       "10    Afghanistan      Asia  2002   42.129  25268405   726.734055         Afg\n",
       "11    Afghanistan      Asia  2007   43.828  31889923   974.580338         Afg\n",
       "12        Albania    Europe  1952   55.230   1282697  1601.056136         Alb\n",
       "13        Albania    Europe  1957   59.280   1476505  1942.284244         Alb\n",
       "14        Albania    Europe  1962   64.820   1728137  2312.888958         Alb\n",
       "15        Albania    Europe  1967   66.220   1984060  2760.196931         Alb\n",
       "16        Albania    Europe  1972   67.690   2263554  3313.422188         Alb\n",
       "17        Albania    Europe  1977   68.930   2509048  3533.003910         Alb\n",
       "18        Albania    Europe  1982   70.420   2780097  3630.880722         Alb\n",
       "19        Albania    Europe  1987   72.000   3075321  3738.932735         Alb\n",
       "20        Albania    Europe  1992   71.581   3326498  2497.437901         Alb\n",
       "21        Albania    Europe  1997   72.950   3428038  3193.054604         Alb\n",
       "22        Albania    Europe  2002   75.651   3508512  4604.211737         Alb\n",
       "23        Albania    Europe  2007   76.423   3600523  5937.029526         Alb\n",
       "24        Algeria    Africa  1952   43.077   9279525  2449.008185         Alg\n",
       "25        Algeria    Africa  1957   45.685  10270856  3013.976023         Alg\n",
       "26        Algeria    Africa  1962   48.303  11000948  2550.816880         Alg\n",
       "27        Algeria    Africa  1967   51.407  12760499  3246.991771         Alg\n",
       "28        Algeria    Africa  1972   54.518  14760787  4182.663766         Alg\n",
       "29        Algeria    Africa  1977   58.014  17152804  4910.416756         Alg\n",
       "30        Algeria    Africa  1982   61.368  20033753  5745.160213         Alg\n",
       "...           ...       ...   ...      ...       ...          ...         ...\n",
       "1674  Yemen, Rep.      Asia  1982   49.113   9657618  1977.557010         Yem\n",
       "1675  Yemen, Rep.      Asia  1987   52.922  11219340  1971.741538         Yem\n",
       "1676  Yemen, Rep.      Asia  1992   55.599  13367997  1879.496673         Yem\n",
       "1677  Yemen, Rep.      Asia  1997   58.020  15826497  2117.484526         Yem\n",
       "1678  Yemen, Rep.      Asia  2002   60.308  18701257  2234.820827         Yem\n",
       "1679  Yemen, Rep.      Asia  2007   62.698  22211743  2280.769906         Yem\n",
       "1680       Zambia    Africa  1952   42.038   2672000  1147.388831         Zam\n",
       "1681       Zambia    Africa  1957   44.077   3016000  1311.956766         Zam\n",
       "1682       Zambia    Africa  1962   46.023   3421000  1452.725766         Zam\n",
       "1683       Zambia    Africa  1967   47.768   3900000  1777.077318         Zam\n",
       "1684       Zambia    Africa  1972   50.107   4506497  1773.498265         Zam\n",
       "1685       Zambia    Africa  1977   51.386   5216550  1588.688299         Zam\n",
       "1686       Zambia    Africa  1982   51.821   6100407  1408.678565         Zam\n",
       "1687       Zambia    Africa  1987   50.821   7272406  1213.315116         Zam\n",
       "1688       Zambia    Africa  1992   46.100   8381163  1210.884633         Zam\n",
       "1689       Zambia    Africa  1997   40.238   9417789  1071.353818         Zam\n",
       "1690       Zambia    Africa  2002   39.193  10595811  1071.613938         Zam\n",
       "1691       Zambia    Africa  2007   42.384  11746035  1271.211593         Zam\n",
       "1692     Zimbabwe    Africa  1952   48.451   3080907   406.884115         Zim\n",
       "1693     Zimbabwe    Africa  1957   50.469   3646340   518.764268         Zim\n",
       "1694     Zimbabwe    Africa  1962   52.358   4277736   527.272182         Zim\n",
       "1695     Zimbabwe    Africa  1967   53.995   4995432   569.795071         Zim\n",
       "1696     Zimbabwe    Africa  1972   55.635   5861135   799.362176         Zim\n",
       "1697     Zimbabwe    Africa  1977   57.674   6642107   685.587682         Zim\n",
       "1698     Zimbabwe    Africa  1982   60.363   7636524   788.855041         Zim\n",
       "1699     Zimbabwe    Africa  1987   62.351   9216418   706.157306         Zim\n",
       "1700     Zimbabwe    Africa  1992   60.377  10704340   693.420786         Zim\n",
       "1701     Zimbabwe    Africa  1997   46.809  11404948   792.449960         Zim\n",
       "1702     Zimbabwe    Africa  2002   39.989  11926563   672.038623         Zim\n",
       "1703     Zimbabwe    Africa  2007   43.487  12311143   469.709298         Zim\n",
       "\n",
       "[1703 rows x 7 columns]"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gapminder = gapminder.drop(0,axis=0)\n",
    "#gapminder = gapminder.drop('country_abb',axis=1)\n",
    "gapminder"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### 2.3.4.12 資料加總 -- .sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "pop    6251013179\n",
       "dtype: int64"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gapminder[gapminder['year'] == 2007][['pop']].sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### 2.3.4.13  計算資料平均值-- .mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "lifeExp         67.007423\n",
       "gdpPercap    11680.071820\n",
       "dtype: float64"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gapminder[gapminder['year'] == 2007][['lifeExp', 'gdpPercap']].mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### 2.3.4.14  計算資料最大值-- .max()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "lifeExp         82.60300\n",
       "gdpPercap    49357.19017\n",
       "dtype: float64"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gapminder[gapminder['year'] == 2007][['lifeExp', 'gdpPercap']].max()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### 2.3.4.15  計算資料最小值-- .min()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "lifeExp       39.613000\n",
       "gdpPercap    277.551859\n",
       "dtype: float64"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gapminder[gapminder['year'] == 2007][['lifeExp', 'gdpPercap']].min()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### 2.3.4.16  計算資料標準差-- .std()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "lifeExp         12.073021\n",
       "gdpPercap    12859.937337\n",
       "dtype: float64"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gapminder[gapminder['year'] == 2007][['lifeExp', 'gdpPercap']].std()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### 2.3.4.17  計算資料中位數-- .median()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "lifeExp        71.935500\n",
       "gdpPercap    6124.371109\n",
       "dtype: float64"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gapminder[gapminder['year'] == 2007][['lifeExp', 'gdpPercap']].median()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### 2.3.4.18  計算資料分位數-- .quantile()\n",
    "\n",
    "* 計算分位數時，如果資料中有包含 NA，則會自動忽略此筆資料。\n",
    "* * 例如有 10 筆資料，其中一筆為 NA，則計算時只會拿 9 筆資料來計算分位數。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "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>lifeExp</th>\n",
       "      <th>gdpPercap</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0.25</th>\n",
       "      <td>57.16025</td>\n",
       "      <td>1624.842248</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.50</th>\n",
       "      <td>71.93550</td>\n",
       "      <td>6124.371109</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.75</th>\n",
       "      <td>76.41325</td>\n",
       "      <td>18008.835640</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       lifeExp     gdpPercap\n",
       "0.25  57.16025   1624.842248\n",
       "0.50  71.93550   6124.371109\n",
       "0.75  76.41325  18008.835640"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gapminder[gapminder['year'] == 2007][['lifeExp', 'gdpPercap']].quantile()\n",
    "gapminder[gapminder['year'] == 2007][['lifeExp', 'gdpPercap']].quantile(q=[0.25, 0.5, 0.75])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### 2.3.4.19  計算資料眾數-- .mode()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "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>lifeExp</th>\n",
       "      <th>gdpPercap</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>39.613</td>\n",
       "      <td>277.551859</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>42.082</td>\n",
       "      <td>414.507341</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>42.384</td>\n",
       "      <td>430.070692</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>42.568</td>\n",
       "      <td>469.709298</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>42.592</td>\n",
       "      <td>579.231743</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>42.731</td>\n",
       "      <td>619.676892</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>43.487</td>\n",
       "      <td>641.369524</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>43.828</td>\n",
       "      <td>690.805576</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>44.741</td>\n",
       "      <td>706.016537</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>45.678</td>\n",
       "      <td>752.749726</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>46.242</td>\n",
       "      <td>759.349910</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>46.388</td>\n",
       "      <td>823.685621</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>46.462</td>\n",
       "      <td>862.540756</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>46.859</td>\n",
       "      <td>863.088464</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>48.159</td>\n",
       "      <td>882.969944</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>48.303</td>\n",
       "      <td>926.141068</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>48.328</td>\n",
       "      <td>942.654211</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>49.339</td>\n",
       "      <td>944.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>49.580</td>\n",
       "      <td>974.580338</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>50.430</td>\n",
       "      <td>986.147879</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>50.651</td>\n",
       "      <td>1042.581557</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>50.728</td>\n",
       "      <td>1044.770126</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>51.542</td>\n",
       "      <td>1056.380121</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>51.579</td>\n",
       "      <td>1091.359778</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>52.295</td>\n",
       "      <td>1107.482182</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>52.517</td>\n",
       "      <td>1201.637154</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>52.906</td>\n",
       "      <td>1217.032994</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>52.947</td>\n",
       "      <td>1271.211593</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>54.110</td>\n",
       "      <td>1327.608910</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>54.467</td>\n",
       "      <td>1391.253792</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>112</th>\n",
       "      <td>78.242</td>\n",
       "      <td>22833.308510</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>113</th>\n",
       "      <td>78.273</td>\n",
       "      <td>23348.139730</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>114</th>\n",
       "      <td>78.332</td>\n",
       "      <td>25185.009110</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>115</th>\n",
       "      <td>78.400</td>\n",
       "      <td>25523.277100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>116</th>\n",
       "      <td>78.553</td>\n",
       "      <td>25768.257590</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>117</th>\n",
       "      <td>78.623</td>\n",
       "      <td>27538.411880</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>118</th>\n",
       "      <td>78.746</td>\n",
       "      <td>28569.719700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>119</th>\n",
       "      <td>78.782</td>\n",
       "      <td>28718.276840</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>120</th>\n",
       "      <td>78.885</td>\n",
       "      <td>28821.063700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>121</th>\n",
       "      <td>79.313</td>\n",
       "      <td>29796.048340</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>122</th>\n",
       "      <td>79.406</td>\n",
       "      <td>30470.016700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>123</th>\n",
       "      <td>79.425</td>\n",
       "      <td>31656.068060</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>124</th>\n",
       "      <td>79.441</td>\n",
       "      <td>32170.374420</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>125</th>\n",
       "      <td>79.483</td>\n",
       "      <td>33203.261280</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>126</th>\n",
       "      <td>79.762</td>\n",
       "      <td>33207.084400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>127</th>\n",
       "      <td>79.829</td>\n",
       "      <td>33692.605080</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>128</th>\n",
       "      <td>79.972</td>\n",
       "      <td>33859.748350</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>129</th>\n",
       "      <td>80.196</td>\n",
       "      <td>34435.367440</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>130</th>\n",
       "      <td>80.204</td>\n",
       "      <td>35278.418740</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>131</th>\n",
       "      <td>80.546</td>\n",
       "      <td>36126.492700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>132</th>\n",
       "      <td>80.653</td>\n",
       "      <td>36180.789190</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>133</th>\n",
       "      <td>80.657</td>\n",
       "      <td>36319.235010</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>134</th>\n",
       "      <td>80.745</td>\n",
       "      <td>36797.933320</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>135</th>\n",
       "      <td>80.884</td>\n",
       "      <td>37506.419070</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>136</th>\n",
       "      <td>80.941</td>\n",
       "      <td>39724.978670</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>137</th>\n",
       "      <td>81.235</td>\n",
       "      <td>40675.996350</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>138</th>\n",
       "      <td>81.701</td>\n",
       "      <td>42951.653090</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>139</th>\n",
       "      <td>81.757</td>\n",
       "      <td>47143.179640</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>140</th>\n",
       "      <td>82.208</td>\n",
       "      <td>47306.989780</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>141</th>\n",
       "      <td>82.603</td>\n",
       "      <td>49357.190170</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>142 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     lifeExp     gdpPercap\n",
       "0     39.613    277.551859\n",
       "1     42.082    414.507341\n",
       "2     42.384    430.070692\n",
       "3     42.568    469.709298\n",
       "4     42.592    579.231743\n",
       "5     42.731    619.676892\n",
       "6     43.487    641.369524\n",
       "7     43.828    690.805576\n",
       "8     44.741    706.016537\n",
       "9     45.678    752.749726\n",
       "10    46.242    759.349910\n",
       "11    46.388    823.685621\n",
       "12    46.462    862.540756\n",
       "13    46.859    863.088464\n",
       "14    48.159    882.969944\n",
       "15    48.303    926.141068\n",
       "16    48.328    942.654211\n",
       "17    49.339    944.000000\n",
       "18    49.580    974.580338\n",
       "19    50.430    986.147879\n",
       "20    50.651   1042.581557\n",
       "21    50.728   1044.770126\n",
       "22    51.542   1056.380121\n",
       "23    51.579   1091.359778\n",
       "24    52.295   1107.482182\n",
       "25    52.517   1201.637154\n",
       "26    52.906   1217.032994\n",
       "27    52.947   1271.211593\n",
       "28    54.110   1327.608910\n",
       "29    54.467   1391.253792\n",
       "..       ...           ...\n",
       "112   78.242  22833.308510\n",
       "113   78.273  23348.139730\n",
       "114   78.332  25185.009110\n",
       "115   78.400  25523.277100\n",
       "116   78.553  25768.257590\n",
       "117   78.623  27538.411880\n",
       "118   78.746  28569.719700\n",
       "119   78.782  28718.276840\n",
       "120   78.885  28821.063700\n",
       "121   79.313  29796.048340\n",
       "122   79.406  30470.016700\n",
       "123   79.425  31656.068060\n",
       "124   79.441  32170.374420\n",
       "125   79.483  33203.261280\n",
       "126   79.762  33207.084400\n",
       "127   79.829  33692.605080\n",
       "128   79.972  33859.748350\n",
       "129   80.196  34435.367440\n",
       "130   80.204  35278.418740\n",
       "131   80.546  36126.492700\n",
       "132   80.653  36180.789190\n",
       "133   80.657  36319.235010\n",
       "134   80.745  36797.933320\n",
       "135   80.884  37506.419070\n",
       "136   80.941  39724.978670\n",
       "137   81.235  40675.996350\n",
       "138   81.701  42951.653090\n",
       "139   81.757  47143.179640\n",
       "140   82.208  47306.989780\n",
       "141   82.603  49357.190170\n",
       "\n",
       "[142 rows x 2 columns]"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gapminder[gapminder['year'] == 2007][['lifeExp', 'gdpPercap']].mode()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### 2.3.4.19  計算欄位之間的線性關係強度和方向 -- .corr()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "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>lifeExp</th>\n",
       "      <th>gdpPercap</th>\n",
       "      <th>pop</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>lifeExp</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.678662</td>\n",
       "      <td>0.047553</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>gdpPercap</th>\n",
       "      <td>0.678662</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.055676</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>pop</th>\n",
       "      <td>0.047553</td>\n",
       "      <td>-0.055676</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            lifeExp  gdpPercap       pop\n",
       "lifeExp    1.000000   0.678662  0.047553\n",
       "gdpPercap  0.678662   1.000000 -0.055676\n",
       "pop        0.047553  -0.055676  1.000000"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gapminder[gapminder['year'] == 2007][['lifeExp', 'gdpPercap','pop']].corr()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fa8af1a60f0>"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gapminder.plot(x='pop', y='lifeExp', kind='scatter', figsize=(3,3)) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fa89cd4c828>"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 648x648 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "sns.set()\n",
    "\n",
    "# Draw a heatmap with the numeric values in each cell\n",
    "f, ax = plt.subplots(figsize=(9, 9))\n",
    "sns.heatmap(gapminder.corr(), annot=True, annot_kws={'fontsize': 12}, linewidths=.1, ax=ax)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### 2.3.4.20  資料排序 -- sort_values()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "gapminder.sort_values(axis=0, by='pop', ascending=False, na_position='first', inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "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>country</th>\n",
       "      <th>year</th>\n",
       "      <th>pop</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1302</th>\n",
       "      <td>Sao Tome and Principe</td>\n",
       "      <td>1982</td>\n",
       "      <td>98593</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>422</th>\n",
       "      <td>Djibouti</td>\n",
       "      <td>1962</td>\n",
       "      <td>89898</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1301</th>\n",
       "      <td>Sao Tome and Principe</td>\n",
       "      <td>1977</td>\n",
       "      <td>86796</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1300</th>\n",
       "      <td>Sao Tome and Principe</td>\n",
       "      <td>1972</td>\n",
       "      <td>76595</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>421</th>\n",
       "      <td>Djibouti</td>\n",
       "      <td>1957</td>\n",
       "      <td>71851</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1299</th>\n",
       "      <td>Sao Tome and Principe</td>\n",
       "      <td>1967</td>\n",
       "      <td>70787</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1298</th>\n",
       "      <td>Sao Tome and Principe</td>\n",
       "      <td>1962</td>\n",
       "      <td>65345</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>420</th>\n",
       "      <td>Djibouti</td>\n",
       "      <td>1952</td>\n",
       "      <td>63149</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1297</th>\n",
       "      <td>Sao Tome and Principe</td>\n",
       "      <td>1957</td>\n",
       "      <td>61325</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1296</th>\n",
       "      <td>Sao Tome and Principe</td>\n",
       "      <td>1952</td>\n",
       "      <td>60011</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    country  year    pop\n",
       "1302  Sao Tome and Principe  1982  98593\n",
       "422                Djibouti  1962  89898\n",
       "1301  Sao Tome and Principe  1977  86796\n",
       "1300  Sao Tome and Principe  1972  76595\n",
       "421                Djibouti  1957  71851\n",
       "1299  Sao Tome and Principe  1967  70787\n",
       "1298  Sao Tome and Principe  1962  65345\n",
       "420                Djibouti  1952  63149\n",
       "1297  Sao Tome and Principe  1957  61325\n",
       "1296  Sao Tome and Principe  1952  60011"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "display(gapminder.loc[:, ['country','year','pop']].tail(10))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3. Matplotlib\n",
    "<B>說明 :<B>\n",
    "    \n",
    "https://matplotlib.org/"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 3.0 安裝套件\n",
    "* Python 2+ : pip install matplotlib\n",
    "* Python 3+ : pip3 install matplotlib"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 3.1 引用套件"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import matplotlib\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 3.2 繪製圖\n",
    "* 折線圖\n",
    "* 長條圖\n",
    "* 散佈圖\n",
    "* 圓餅圖"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### 3.2.1 折線圖"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "X = [1,2,3,4]\n",
    "plt.plot(X) \n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "X = [1,2,3,4]\n",
    "Y = [1,4,9,16]\n",
    "plt.plot(X,Y)\n",
    "plt.ylabel('Frequency')\n",
    "plt.xlabel('x label')\n",
    "plt.title('Figure')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7fa89c581ef0>]"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(X,Y,'ro')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### 3.2.2 長條圖"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<BarContainer object of 4 artists>"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "X = [1,2,3,4]\n",
    "Y = [1,4,9,16]\n",
    "plt.bar(X,Y,color=\"red\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "\n",
    "N = 5\n",
    "menMeans = (20, 35, 30, 35, 27)\n",
    "womenMeans = (25, 32, 34, 20, 25)\n",
    "menStd = (2, 3, 4, 1, 2)\n",
    "womenStd = (3, 5, 2, 3, 3)\n",
    "ind = np.arange(N)    # the x locations for the groups\n",
    "width = 0.35       # the width of the bars\n",
    "\n",
    "p1 = plt.bar(ind, menMeans, width, yerr=menStd)\n",
    "p2 = plt.bar(ind, womenMeans, width,\n",
    "             bottom=menMeans, yerr=womenStd)\n",
    "\n",
    "plt.ylabel('Scores')\n",
    "plt.title('Scores by group and gender')\n",
    "plt.xticks(ind, ('G1', 'G2', 'G3', 'G4', 'G5'))\n",
    "plt.yticks(np.arange(0, 81, 10))\n",
    "plt.legend((p1[0], p2[0]), ('Men', 'Women'))\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### 3.2.3 散佈圖"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Figure')"
      ]
     },
     "execution_count": 93,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYUAAAELCAYAAAA2mZrgAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDMuMC4xLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvDW2N/gAAH7RJREFUeJzt3XtQVOfBBvBnD7o0BgnCrMuCTrBMaSgkg4YJYzKUEW/UgjgkjsRboqJTNdhcqGCTkQQxkzWMY6U0ho6TONYokwlKURA0FzTRJqbRRjGmlokxsoC4YIFooO6+3x8O7yfuAnvYq/D8/uKcPbvn2eWFZ/c9Z3c1QggBIiIiAIq3AxARke9gKRARkcRSICIiiaVAREQSS4GIiCSWAhERSSwFojuYTCZMnjwZFovF21GIvELD9ynQSJWcnIxr167Bz89Prjt8+DD0er0XUxF51yhvByDyph07duDxxx/3yL5u3bqFUaP4J0e+jdNHRHe4cuUKfvnLX+LWrVsAgB9++AGLFi3C5MmT8eyzz+K1115DTk4OAODzzz/Hr3/96z7XT05OxokTJwAAxcXFWLduHXJycjBlyhTs378fVqsVpaWlmDFjBhISEvD73/8e169f9+ydJBoAS4FoADk5OXjkkUfw+eef47nnnkNFRYWq63/44YdISUnBl19+ibS0NOzevRtHjx7F3/72Nxw/fhwPPPAACgoK3JSeSD2+lqURbe3atfKYwmOPPYY//vGP8jKTyYSzZ8/i3XffhVarRXx8PJKTk1XdflxcHGbMmAEA+NnPfoZ9+/Zh48aNCA0NBQA899xzmDZtGqeWyGdwFNKIVlJS0ueYwpUrV+TPV69exQMPPID77rtPrjMYDGhqanL49nv/+fcymUxYu3YtFOX/X6QrigKz2cwD3OQTWApE/dDpdPjvf/+LmzdvymK4sxDuu+8+/PTTT3LZYrGgra2tz21oNJo+y6GhoXj99dfx6KOPujE50dDxmAJRP8LDwxEbG4vi4mL09PTg9OnT+Pjjj+XlkyZNQnd3Nz755BP873//w1tvvYWenp4Bb/Ppp5/Gtm3b0NjYCABoa2vD0aNH3Xo/iNTgKwWiARQVFSEvLw8JCQl45JFHMGfOHPnGtrFjxyI/Px+vvPIKLBYLsrKybKaL7rZ06VIIIbB8+XJcvXoVISEhmDNnjjzuQORtfPMakQrPP/88fv7zn2PdunXejkLkFpw+IhrA119/jcuXL8NqteLYsWP48MMP+ayehjVOHxEN4Nq1a8jOzsb169cRGhqKV199Fb/61a+8HYvIbTh9REREEqePiIhIYikQEZHEUiAiIumeOdDc3v4jrFbfOvwREhIAs7nL2zFsMJc6zKUOc6njrVyKosG4cfervt49UwpWq/C5UgDgk5kA5lKLudRhLnV8NZc9nD4iIiKJpUBERBJLgYiIJJYCERFJ98yBZiKikeRkfTPK6xpg7uhGSKA/MpIiMTVm4E/hdQWWAhGRjzlZ34xd1RfQc8sKADB3dGNX9QUAcHsxcPqIiMjHlNc1yELo1XPLivK6Brfvm6VARORjzB3dqta7EkuBiMjHhAT6q1rvSiwFIiIfk5EUCe2ovv+etaMUZCRFun3fPNBMRORjeg8m8+wjIiICcLsYPFECd+P0ERERSSwFIiKSBi2F9vZ2rFy5ErNnz0ZaWhqee+45tLW1AQDOnDmDuXPnYvbs2Vi+fDnMZrPd27h58yaef/55zJw5EykpKfj4449dey+IiMglBi0FjUaDrKws1NTUoLKyEhMnTkRRURGsViv+8Ic/YOPGjaipqUF8fDyKiors3sbOnTsREBCAI0eOYMeOHXjllVfw448/uvzOEBGRcwYthaCgICQkJMjluLg4mEwmnDt3Dv7+/oiPjwcAZGZm4vDhw3Zvo7q6GgsWLAAAREREIDY2FseOHXNFfiIiciFVZx9ZrVbs3bsXycnJaGpqQlhYmLwsODgYVqsV169fR1BQUJ/rmUwmhIeHy2WDwYDm5mZVQUNCAlRt7yk63VhvR7CLudRhLnWYSx1fzWWPqlLYtGkTxowZg8WLF+PIkSPuymSX2dzlc19pp9ONRWtrp7dj2GAudZhLHeZSx1u5FEUzpCfTDp99ZDQa8f3332Pbtm1QFAUGgwEmk0le3tbWBkVRbF4lAEBYWBgaGxvlclNTE0JDPX/+LRERDcyhUti6dSvOnTuHkpISaLVaAEBsbCx++uknfPnllwCAffv2ISUlxe71U1JSUFZWBgC4dOkSzp49i8TERFfkJyIiFxp0+ujixYt4++23ERERgczMTADAhAkTUFJSgi1btiA/Px/d3d0IDw/Hm2++Ka+Xnp6O0tJS6PV6rFixAnl5eZg5cyYURUFBQQECAnzzGAER0UimEUL41kR9P3hMwXHMpQ5zqcNc6gzbYwpERDT8sRSIiEhiKRARkcRSICIiiaVAREQSS4GIiCSWAhERSSwFIiKSWApERCSxFIiISGIpEBGRxFIgIiKJpUBERBJLgYiIJJYCERFJLAUiIpJYCkREJA36dZwAYDQaUVNTg8bGRlRWViIqKgpXrlzB2rVr5TadnZ3o6urCF198YXP94uJivPfeexg/fjwAYMqUKcjPz3fRXSAiIldxqBSmT5+OpUuXYtGiRXLdhAkTUFFRIZc3b94Mi8XS723MmzcPubm5TkQlIiJ3c6gU4uPjB7y8p6cHlZWV2Llzp0tCERGRdzhUCoP56KOPoNfrERMT0+82hw4dwqeffgqdTofs7GxMnjxZ1T6G8gXUnqDTjfV2BLuYSx3mUoe51PHVXPa4pBQ++OADPPnkk/1enpmZid/97ncYPXo0PvvsM6xZswZVVVUYN26cw/swm7tgtQpXxHUZnW4sWls7vR3DBnOpw1zqMJc63sqlKJohPZl2+uyjlpYWnDp1Cmlpaf1uo9PpMHr0aADAE088AYPBgIsXLzq7ayIicjGnS2H//v1ISkoa8Fl/S0uL/Pmbb75BY2MjJk2a5OyuiYjIxRyaPiosLERtbS2uXbuGZcuWISgoCIcOHQJwuxRefvllm+usXLkS69atw8MPP4ytW7eivr4eiqJg9OjR2LJlC3Q6nWvvCREROU0jhPCtifp+8JiC45hLHeZSh7nUGXHHFIiIaPhgKRARkcRSICIiiaVAREQSS4GIiCSWAhERSSwFIiKSXPLZR0REAzlZ34zyugaYO7oREuiPjKRITI0J9XYssoOlQERudbK+GbuqL6DnlhUAYO7oxq7qCwDAYvBBnD4iIrcqr2uQhdCr55YV5XUNXkpEA2EpEJFbmTu6Va0n72IpEJFbhQT6q1pP3sVSICK3ykiKhHZU33812lEKMpIivZSIBsIDzUTkVr0Hk3n20b2BpUBEbjc1JpQlcI/g9BEREUksBSIikhyaPjIajaipqUFjYyMqKysRFRUFAEhOToZWq4W//+2zCHJycpCYmGhz/Zs3b2LDhg2or6+Hn58fcnNzMW3aNBfeDSJyt953Jbd1dCOYxwWGLYdKYfr06Vi6dCkWLVpkc9n27dtlSfRn586dCAgIwJEjR3Dp0iUsWrQItbW1uP/++4eWmog8iu9KHjkcmj6Kj4+HwWAY8k6qq6uxYMECAEBERARiY2Nx7NixId8eEXkW35U8cjh99lFOTg6EEHj00Ufx4osvIjAw0GYbk8mE8PBwuWwwGNDc3KxqP0P5AmpP0OnGejuCXcylDnMNrK2fdx+3dXT7TEbAdx6vu/lqLnucKoU9e/bAYDCgp6cHmzdvRkFBAYqKilyVrQ+zuQtWq3DLbQ+VTjcWra2d3o5hg7nUYa7BBQf62/1YiuBAf5/J6EuP1528lUtRNEN6Mu3U2Ue9U0parRYLFy7EV199ZXe7sLAwNDY2yuWmpiaEhnIekuhewXcljxxDLoUbN26gs/N2+wkhUFVVhejoaLvbpqSkoKysDABw6dIlnD171u5ZSkTkm6bGhOKZ3zyEkEB/aHD7c4ue+c1DPMg8DDk0fVRYWIja2lpcu3YNy5YtQ1BQEHbs2IHs7GxYLBZYrVZERkYiPz9fXic9PR2lpaXQ6/VYsWIF8vLyMHPmTCiKgoKCAgQE+OYxAiKyr/ddyb46TUOuoRFC+NZEfT94TMFxzKUOc6nDXOqMqGMKREQ0vLAUiIhIYikQEZHEUiAiIomlQEREEkuBiIgklgIREUksBSIiklgKREQksRSIiEhiKRARkcRSICIiiaVAREQSS4GIiCSWAhERSSwFIiKSWApERCQ59HWcRqMRNTU1aGxsRGVlJaKiotDe3o7169fj8uXL0Gq1ePDBB1FQUIDg4GCb6+fl5eHEiRMYN24cgNvf2bx69WrX3hMiInKaQ68Upk+fjj179iA8PFyu02g0yMrKQk1NDSorKzFx4kQUFRX1exurVq1CRUUFKioqWAhERD7KoVKIj4+HwWDosy4oKAgJCQlyOS4uDiaTybXpiIjIoxyaPhqM1WrF3r17kZyc3O8277zzDsrKyjBx4kS89NJLiIyMVLWPoXwBtSfodGO9HcEu5lKHudRhLnV8NZc9LimFTZs2YcyYMVi8eLHdy1944QXodDooioIDBw4gKysLR48ehZ+fn8P7MJu7YLUKV8R1GZ1uLFpbO70dwwZzqcNc6jCXOt7KpSiaIT2ZdvrsI6PRiO+//x7btm2Doti/Ob1eLy+bN28ebty4gebmZmd3TURELuZUKWzduhXnzp1DSUkJtFptv9u1tLTIn48fPw5FUaDX653ZNRERuYFD00eFhYWora3FtWvXsGzZMgQFBWHbtm14++23ERERgczMTADAhAkTUFJSAgBIT09HaWkp9Ho9cnNzYTabodFoEBAQgLfeegujRrlk5oqIiFxII4TwrYn6fvCYguOYSx3mUoe51BlxxxSIiGj4YCkQEZHEUiAiIomlQEREEkuBiIgklgIREUksBSIiklgKREQksRSIiEhiKRARkcRSICIiiaVAREQSS4GIiCSWAhERSSwFIiKSWApERCSxFIiISBr0OzGNRiNqamrQ2NiIyspKREVFAQC+++475OXl4fr16wgKCoLRaERERITN9S0WCwoLC3H8+HFoNBqsWrUK8+fPd/kdIVLrZH0zyusa0NbRjeBAf2QkRWJqTKi3YxF51aCvFKZPn449e/YgPDy8z/r8/HwsXLgQNTU1WLhwITZu3Gj3+pWVlbh8+TJqa2tRVlaG4uJiXLlyxTXpiYboZH0zdlVfgLmjGwKAuaMbu6ov4GR9s7ejEXnVoKUQHx8Pg8HQZ53ZbMb58+eRmpoKAEhNTcX58+fR1tZmc/2qqirMnz8fiqIgODgYM2bMwOHDh10Un2hoyusa0HPL2mddzy0ryusavJSIyDcMOn1kT1NTE/R6Pfz8/AAAfn5+GD9+PJqamhAcHGyzbVhYmFw2GAxoblb/bGwoX0DtCTrdWG9HsIu5BtbW0d3vel/JCPjO43U35lLHV3PZM6RS8AazuQtWq/B2jD50urFobe30dgwbzDW44EB/mO0UQ3Cgv89k9KXH607MpY63cimKZkhPpod09pHBYEBLSwssFguA2weTr169ajPN1LutyWSSy01NTQgN5cE88q6MpEhoR/Ud/tpRCjKSIr2UiMg3DKkUQkJCEB0djYMHDwIADh48iOjoaJupIwBISUnB+++/D6vVira2Nhw9ehSzZ892LjWRk6bGhOKZ3zyEkEB/aACEBPrjmd88xLOPaMQbdPqosLAQtbW1uHbtGpYtW4agoCAcOnQIr776KvLy8vCXv/wFgYGBMBqN8jorV67EunXr8PDDDyM9PR3/+te/MGvWLADA2rVrMXHiRPfdIyIHTY0JxdSYUJ+ddiDyBo0Qwrcm6vvBYwqOYy51mEsd5lJnRBxTICKi4YmlQEREEkuBiIgklgIREUksBSIiklgKREQksRSIiEhiKRARkcRSICIiiaVAREQSS4GIiCSWAhERSffMl+zQ8HWyvhnldQ0wd3QjJNAfGUmR/AhrIi9hKZBXnaxvxq7qC/L7ks0d3dhVfQEAWAxEXsDpI/Kq8roGWQi9em5ZUV7X4KVERCMbS4G8yt73JA+0nojcy6npoytXrmDt2rVyubOzE11dXfjiiy/6bFdcXIz33nsP48ePBwBMmTIF+fn5zuyahomQQH+7BRAS6O+FNETkVClMmDABFRUVcnnz5s2wWCx2t503bx5yc3Od2R0NQxlJkX2OKQCAdpSCjKRIL6YiGrlcdqC5p6cHlZWV2Llzp6tukkaA3oPJPPuIyDe4rBQ++ugj6PV6xMTE2L380KFD+PTTT6HT6ZCdnY3Jkye7atd0j5saE8oSIPIRGiGEcMUNrVy5EomJiVi6dKnNZa2trQgKCsLo0aPx2WefIScnB1VVVRg3bpwrdk1ERC7iklcKLS0tOHXqFLZs2WL3cp1OJ39+4oknYDAYcPHiRTz22GMO78Ns7oLV6pL+chmdbixaWzu9HcMGc6nDXOowlzreyqUoGoSEBKi+nktKYf/+/UhKSur3mX9LSwv0ej0A4JtvvkFjYyMmTZrkil27Dd9lS0QjkctK4eWXX+6zbuXKlVi3bh0efvhhbN26FfX19VAUBaNHj8aWLVv6vHrwNXyXLRGNVC4phZqaGpt1f/3rX+XPRqPRFbvxmIHeZctSIKLhjO9otoPvsiWikYqlYEd/76blu2yJaLhjKdiRkRQJ7ai+Dw3fZUtEIwE/OtsOvsuWiEYqlkI/+C5bIhqJOH1EREQSS4GIiCSWAhERSTymQACA3TUXUHfGBKsAFA2QFBeGJbMf8nYsIvIwlgJhd80FfHzaJJetAnKZxUA0snD6iFB3xqRqPRENXywFQn+fSO5jn1RORB7AUiAoGnXriWj4YikQkuLCVK0nouGLB5pJHkzm2UdExFIgALeLgSVARJw+IiIiyelXCsnJydBqtfD3v/1dAzk5OUhMTOyzzc2bN7FhwwbU19fDz88Pubm5mDZtmrO7JiIiF3PJ9NH27dsRFRXV7+U7d+5EQEAAjhw5gkuXLmHRokWora3F/fff74rdExGRi3hk+qi6uhoLFiwAAERERCA2NhbHjh3zxK6JiEgFl7xSyMnJgRACjz76KF588UUEBgb2udxkMiE8PFwuGwwGNDc3u2LXRETkQk6Xwp49e2AwGNDT04PNmzejoKAARUVFrsjWR0hIgMtv0xV0urHejmAXc6nDXOowlzq+mssep0vBYDAAALRaLRYuXIjVq1fbbBMWFobGxkYEBwcDAJqampCQkKBqP2ZzF6w+9rkLOt1YtLZ2ejuGDeZSh7nUYS51vJVLUTRDejLt1DGFGzduoLPz9p0VQqCqqgrR0dE226WkpKCsrAwAcOnSJZw9e9bmDCUiIvI+p14pmM1mZGdnw2KxwGq1IjIyEvn5+QCA9PR0lJaWQq/XY8WKFcjLy8PMmTOhKAoKCgoQEOCb00FERCOZU6UwceJEHDhwwO5lFRUV8ucxY8Zg+/btzuyKiIg8gO9oJiIiiaVAREQSPxCvHyfrm1Fe1wBzRzdCAv2RkRSJqTGh3o5FRORWLAU7TtY3Y1f1BfTcsgIAzB3d2FV9AQBYDEQ0rHH6yI7yugZZCL16bllRXtfgpURERJ7BUrDD3NGtaj0R0XDBUrAjJNBf1XoiouGCpWBHRlIktKP6PjTaUQoykiK9lIiIyDN4oNmO3oPJPPuIiEYalkI/psaEsgSIaMTh9BEREUksBSIiklgKREQk8ZjCMLK75gLqzphgFYCiAZLiwrBk9kPejkVE9xCWwjCxu+YCPj5tkstWAbnMYiAiR3H6aJioO2NStZ6IyB6WwjDR39dX+9jXWhORj3Nq+qi9vR3r16/H5cuXodVq8eCDD6KgoADBwcF9tsvLy8OJEycwbtw4ALe/s3n16tXO7JruomjsF4Ci8XwWIrp3OVUKGo0GWVlZSEhIAAAYjUYUFRXh9ddft9l21apVWLx4sTO7owEkxYX1OaZw53oiIkc5NX0UFBQkCwEA4uLiYDJxDtsblsx+CNMmh8lXBooGmDaZZx8RkTouO/vIarVi7969SE5Otnv5O++8g7KyMkycOBEvvfQSIiP54XKutmT2Q1gy+yHodGPR2trp7ThEdA/SCCFccijytddeQ0tLC/785z9DUfq+AGlpaYFOp4OiKDhw4AD+9Kc/4ejRo/Dz83PFromIyEVcUgpGoxHffvstduzYAa1WO+j2CQkJKC8vR3h4uMP7MJu7YPWxU2l89Rk5c6nDXOowlzreyqUoGoSEBKi+ntPTR1u3bsW5c+dQWlrabyG0tLRAr9cDAI4fPw5FUeSyu5ysb+ZHX3sQH2+i4cGpUrh48SLefvttREREIDMzEwAwYcIElJSUID09HaWlpdDr9cjNzYXZbIZGo0FAQADeeustjBrlvjdTn6xvxq7qC/J7ls0d3dhVfQEA+I/KDfh4Ew0fTv1n/sUvfoFvv/3W7mUVFRXy53fffdeZ3ahWXtcg/0H16rllRXldA/9JuQEfb6LhY1i+o9nc0a1qPTmHjzfR8DEsSyEk0F/VenIOH2+i4WNYlkJGUiS0o/reNe0oBRlJfG+EO/DxJho+huVHZ/fOY/NsGM/g4000fAzLUgBu/6PiPyXP4eNNNDwMy+kjIiIaGpYCERFJLAUiIpJYCkREJN0zB5oVH/0KMeZSh7nUYS51mMv5fbrso7OJiOjex+kjIiKSWApERCSxFIiISGIpEBGRxFIgIiKJpUBERBJLgYiIJJYCERFJLAUiIpJ85mMuvvvuO+Tl5eH69esICgqC0WhEREREn20sFgsKCwtx/PhxaDQarFq1CvPnz3dbpvb2dqxfvx6XL1+GVqvFgw8+iIKCAgQHB/fZLi8vDydOnMC4ceMAACkpKVi9erXbcgFAcnIytFot/P1vf+VlTk4OEhMT+2xz8+ZNbNiwAfX19fDz80Nubi6mTZvmtkxXrlzB2rVr5XJnZye6urrwxRdf9NmuuLgY7733HsaPHw8AmDJlCvLz812axWg0oqamBo2NjaisrERUVBQAx8YZ4L6xZi+Xo+MMcN9Y6+/xcmScAe4ba/ZyOTrOAPeNtYF+Z2fOnMHGjRvR3d2N8PBwvPnmmwgJCbG5DU//fTpM+IglS5aIAwcOCCGEOHDggFiyZInNNvv37xfLly8XFotFmM1mkZiYKH744Qe3ZWpvbxf/+Mc/5PIbb7whNmzYYLNdbm6u2L17t9ty2DNt2jTx7bffDrhNcXGxePnll4UQQnz33Xfi8ccfF11dXZ6IJ4QQorCwULz22ms267dv3y7eeOMNt+771KlTwmQy2TxOjowzIdw31uzlcnScCeG+sdbf4+XIOBPCfWOtv1x36m+cCeG+sdbf78xisYgZM2aIU6dOCSGEKCkpEXl5eXZvw9t/n/3xiekjs9mM8+fPIzU1FQCQmpqK8+fPo62trc92VVVVmD9/PhRFQXBwMGbMmIHDhw+7LVdQUBASEhLkclxcHEwmk9v252rV1dVYsGABACAiIgKxsbE4duyYR/bd09ODyspKPPnkkx7Z393i4+NhMBj6rHN0nAHuG2v2cvnCOLOXSw13jbXBcnlrnPX3Ozt37hz8/f0RHx8PAMjMzOx33Hjz73MgPlEKTU1N0Ov18PPzAwD4+flh/PjxaGpqstkuLCxMLhsMBjQ3N3sko9Vqxd69e5GcnGz38nfeeQdpaWlYs2YNGhoaPJIpJycHaWlpePXVV9HR0WFzuclkQnh4uFz25OP10UcfQa/XIyYmxu7lhw4dQlpaGpYvX47Tp097JJOj46x3W2+MtcHGGeD5sTbYOAO8N9YGG2eA+8fanb+zu8dNcHAwrFYrrl+/bnM9b/59DsQnSuFesGnTJowZMwaLFy+2ueyFF17AkSNHUFlZiVmzZiErKwsWi8Wtefbs2YO///3v+OCDDyCEQEFBgVv3p9YHH3zQ77O3zMxMfPjhh6isrMSKFSuwZs0atLe3ezihbxponAGeH2v38jgDPDPWBvud3Wt8ohQMBgNaWlrk4LZYLLh69arNy0aDwdDnZXVTUxNCQ93/ZfFGoxHff/89tm3bBkWxfcj0er1cP2/ePNy4ccPtjd/72Gi1WixcuBBfffWVzTZhYWFobGyUy556vFpaWnDq1CmkpaXZvVyn02H06NEAgCeeeAIGgwEXL150ey5Hx1nvtp4ea4ONM8DzY82RcQZ4Z6wNNs4A94+1u39nd4+btrY2KIqCoKAgm+t66+9zMD5RCiEhIYiOjsbBgwcBAAcPHkR0dLTN2RcpKSl4//33YbVa0dbWhqNHj2L27NluzbZ161acO3cOJSUl0Gq1drdpaWmRPx8/fhyKokCv17st040bN9DZ2QkAEEKgqqoK0dHRNtulpKSgrKwMAHDp0iWcPXvW7pkjrrZ//34kJSXJM2Tudufj9c0336CxsRGTJk1yey5Hxxng+bHmyDgDPDvWHB1ngHfG2mDjDHDvWLP3O4uNjcVPP/2EL7/8EgCwb98+pKSk2L2+t/4+B+Xd49z/7z//+Y946qmnxKxZs8RTTz0lGhoahBBCZGVlia+//loIIcStW7fExo0bxfTp08X06dPFvn373Jrp3//+t4iKihKzZs0Sc+fOFXPnzhVr1qwRQggxd+5c0dzcLIQQ4plnnhGpqakiLS1NPP300+L06dNuzXX58mWRnp4uUlNTxZw5c0R2drZoaWmxyfXjjz+K7OxsMWPGDDFr1ixx5MgRt+bqNWvWLFFXV9dn3Z2/x/Xr14vf/va3Ii0tTWRkZIhPPvnE5Rk2bdokEhMTRXR0tHj88cfFnDlzhBD9j7O7M7prrNnLNdA4E8IzY81eroHG2d253DXW+vs9CmF/nAnhmbE20O/sn//8p0hNTRUzZ84Uzz77rGhtbZXX84W/z8Hwm9eIiEjyiekjIiLyDSwFIiKSWApERCSxFIiISGIpEBGRxFIgIiKJpUBERBJLgYiIpP8DzqJ9g2CWRpQAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "X = [1,5,2,6,2,5,7,10,15,6,8,21,16]\n",
    "Y = [2,3,4,6,7,3,2,10,15,6,8,21,16]\n",
    "plt.scatter(X,Y)\n",
    "plt.title('Figure')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### 3.2.4 圓餅圖"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Pie chart, where the slices will be ordered and plotted counter-clockwise:\n",
    "labels = 'Frogs', 'Hogs', 'Dogs', 'Logs'\n",
    "sizes = [15, 30, 45, 10]\n",
    "explode = (0, 0.1, 0, 0)  # only \"explode\" the 2nd slice (i.e. 'Hogs')\n",
    "\n",
    "fig1, ax1 = plt.subplots()\n",
    "ax1.pie(sizes, explode=explode, labels=labels, autopct='%.f%%')\n",
    "ax1.axis('equal')  # Equal aspect ratio ensures that pie is drawn as a circle.\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 0.98, 'Categorical Plotting')"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 648x216 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "data = {'apples': 10, 'oranges': 15, 'lemons': 5, 'limes': 20}\n",
    "names = list(data.keys())\n",
    "values = list(data.values())\n",
    "\n",
    "fig, axs = plt.subplots(1, 3, figsize=(9, 3), sharey=True)\n",
    "axs[0].bar(names, values)\n",
    "axs[1].scatter(names, values)\n",
    "axs[2].plot(names, values)\n",
    "fig.suptitle('Categorical Plotting')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}