Homework/Day_15_HW.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "WpY62-EDcs8F"
   },
   "source": [
    "作業目標:<br>\n",
    "1. 熟悉分組計算"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "pXYQs1_9a6He"
   },
   "source": [
    "作業重點: <br>\n",
    "1. 分組計算也可以加入apply自定義的函數"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "vysasgqxcupS"
   },
   "source": [
    "題目 : 運用下列分數資料分析<br>\n",
    "1. 找出全年級各科成績最高分與最低分?<br>\n",
    "2. 找出數學班平均最高的班級?<br>\n",
    "3. 分析全校女生與男生國文平均差幾分?<br>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>student_id</th>\n",
       "      <th>math_score</th>\n",
       "      <th>english_score</th>\n",
       "      <th>chinese_score</th>\n",
       "      <th>sex</th>\n",
       "      <th>class</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>50</td>\n",
       "      <td>80</td>\n",
       "      <td>70</td>\n",
       "      <td>boy</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>60</td>\n",
       "      <td>45</td>\n",
       "      <td>50</td>\n",
       "      <td>boy</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>98</td>\n",
       "      <td>43</td>\n",
       "      <td>55</td>\n",
       "      <td>boy</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>70</td>\n",
       "      <td>69</td>\n",
       "      <td>89</td>\n",
       "      <td>boy</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>56</td>\n",
       "      <td>79</td>\n",
       "      <td>60</td>\n",
       "      <td>girl</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>6</td>\n",
       "      <td>60</td>\n",
       "      <td>68</td>\n",
       "      <td>55</td>\n",
       "      <td>girl</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>7</td>\n",
       "      <td>45</td>\n",
       "      <td>70</td>\n",
       "      <td>77</td>\n",
       "      <td>girl</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>8</td>\n",
       "      <td>55</td>\n",
       "      <td>77</td>\n",
       "      <td>76</td>\n",
       "      <td>girl</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>9</td>\n",
       "      <td>25</td>\n",
       "      <td>57</td>\n",
       "      <td>60</td>\n",
       "      <td>girl</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>10</td>\n",
       "      <td>88</td>\n",
       "      <td>40</td>\n",
       "      <td>43</td>\n",
       "      <td>girl</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>11</td>\n",
       "      <td>25</td>\n",
       "      <td>60</td>\n",
       "      <td>45</td>\n",
       "      <td>boy</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>12</td>\n",
       "      <td>80</td>\n",
       "      <td>60</td>\n",
       "      <td>23</td>\n",
       "      <td>boy</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>13</td>\n",
       "      <td>20</td>\n",
       "      <td>90</td>\n",
       "      <td>66</td>\n",
       "      <td>girl</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>14</td>\n",
       "      <td>50</td>\n",
       "      <td>50</td>\n",
       "      <td>50</td>\n",
       "      <td>girl</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>15</td>\n",
       "      <td>89</td>\n",
       "      <td>67</td>\n",
       "      <td>77</td>\n",
       "      <td>girl</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    student_id  math_score  english_score  chinese_score   sex  class\n",
       "0            1          50             80             70   boy      1\n",
       "1            2          60             45             50   boy      2\n",
       "2            3          98             43             55   boy      1\n",
       "3            4          70             69             89   boy      2\n",
       "4            5          56             79             60  girl      1\n",
       "5            6          60             68             55  girl      2\n",
       "6            7          45             70             77  girl      1\n",
       "7            8          55             77             76  girl      2\n",
       "8            9          25             57             60  girl      1\n",
       "9           10          88             40             43  girl      3\n",
       "10          11          25             60             45   boy      3\n",
       "11          12          80             60             23   boy      3\n",
       "12          13          20             90             66  girl      3\n",
       "13          14          50             50             50  girl      3\n",
       "14          15          89             67             77  girl      3"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "score_df = pd.DataFrame( [[1,50,80,70,'boy',1],\n",
    "                          [2,60,45,50,'boy',2],\n",
    "                          [3,98,43,55,'boy',1],\n",
    "                          [4,70,69,89,'boy',2],\n",
    "                          [5,56,79,60,'girl',1],\n",
    "                          [6,60,68,55,'girl',2],\n",
    "                          [7,45,70,77,'girl',1],\n",
    "                          [8,55,77,76,'girl',2],\n",
    "                          [9,25,57,60,'girl',1],\n",
    "                          [10,88,40,43,'girl',3],\n",
    "                          [11,25,60,45,'boy',3],\n",
    "                          [12,80,60,23,'boy',3],\n",
    "                          [13,20,90,66,'girl',3],\n",
    "                          [14,50,50,50,'girl',3],\n",
    "                          [15,89,67,77,'girl',3]], columns=['student_id','math_score','english_score','chinese_score','sex','class'] )\n",
    "\n",
    "score_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "id": "Zfs1uSNPfNBL"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "各科最高分 : \n",
      " math_score       98\n",
      "english_score    90\n",
      "chinese_score    89\n",
      "dtype: object \n",
      "\n",
      "各科最低分 : \n",
      " math_score       20\n",
      "english_score    40\n",
      "chinese_score    23\n",
      "dtype: object\n"
     ]
    }
   ],
   "source": [
    "# 1.找出全年級各科成績最高分與最低分?\n",
    "\n",
    "print( '各科最高分 : \\n', score_df.max()[['math_score','english_score','chinese_score']], '\\n' )\n",
    "print( '各科最低分 : \\n', score_df.min()[['math_score','english_score','chinese_score']] )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "id": "iZdLLEzYfPnW"
   },
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Math_Mean</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>class</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>54.800000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>61.250000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>58.666667</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Math_Mean\n",
       "class           \n",
       "1      54.800000\n",
       "2      61.250000\n",
       "3      58.666667"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 2.找出數學班平均最高的班級?\n",
    "# Ans: 班級 2\n",
    "\n",
    "math_mean = score_df[ ['class','math_score'] ].groupby( by='class' ).mean()\n",
    "math_mean.columns = ['Math_Mean']\n",
    "math_mean"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "數學平均最高是班級 2\n"
     ]
    }
   ],
   "source": [
    "best_class = math_mean.idxmax()\n",
    "print( '數學平均最高是班級', best_class[0] )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "id": "CWxb-2TqfbNK"
   },
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>chinese_score</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sex</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>boy</th>\n",
       "      <td>55.333333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>girl</th>\n",
       "      <td>62.666667</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      chinese_score\n",
       "sex                \n",
       "boy       55.333333\n",
       "girl      62.666667"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 3.分析全校女生與男生國文平均差幾分?\n",
    "# Ans: 男生較女生低約 7.3 分\n",
    "\n",
    "mean_chinese = score_df[ ['sex','chinese_score'] ].groupby( by='sex' ).mean()\n",
    "mean_chinese"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "id": "aBC5K-q6jFvb"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-7.333333333333329"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mean_chinese.loc[ 'boy' ][0] - mean_chinese.loc[ 'girl' ][0]"
   ]
  }
 ],
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