D13_pandas 統計函式使用教學.ipynb

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   "cell_type": "markdown",
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    "id": "QSGBlxpAWNEz"
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   "source": [
    "作業目標:<br>\n",
    "1. 敘述統計量計算<br>\n",
    "2. 運用自定義函數apply"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "1Pl9tfEoWow-"
   },
   "source": [
    "作業重點:<br>\n",
    "1. 了解敘述統計量並解釋<br>\n",
    "2. 運用apply時須注意自定義函數寫法"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
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   "source": [
    "題目<br>\n",
    "對以下成績資料做分析<br>\n",
    "1. 6號學生(student_id=6)3科平均分數為何?<br>\n",
    "2. 6號學生3科平均分數是否有贏過班上一半的同學?<br>\n",
    "3. 由於班上同學成績不好，所以學校統一加分，加分方式為開根號乘以十，請問6號同學3科成績分別是?<br>\n",
    "4. 承上題，加分後各科班平均變多少?\n",
    "<br>\n",
    "score_df = pd.DataFrame([[1,56,66,70], \n",
    "              [2,90,45,34],\n",
    "              [3,45,32,55],\n",
    "              [4,70,77,89],\n",
    "              [5,56,80,70],\n",
    "              [6,60,54,55],\n",
    "              [7,45,70,79],\n",
    "              [8,34,77,76],\n",
    "              [9,25,87,60],\n",
    "              [10,88,40,43]],columns=['student_id','math_score','english_score','chinese_score'])"
   ]
  },
  {
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    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>math_score</th>\n",
       "      <th>english_score</th>\n",
       "      <th>chinese_score</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>student_id</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>56</td>\n",
       "      <td>66</td>\n",
       "      <td>70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>90</td>\n",
       "      <td>45</td>\n",
       "      <td>34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>45</td>\n",
       "      <td>32</td>\n",
       "      <td>55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>70</td>\n",
       "      <td>77</td>\n",
       "      <td>89</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>56</td>\n",
       "      <td>80</td>\n",
       "      <td>70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>60</td>\n",
       "      <td>54</td>\n",
       "      <td>55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>45</td>\n",
       "      <td>70</td>\n",
       "      <td>79</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>34</td>\n",
       "      <td>77</td>\n",
       "      <td>76</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>25</td>\n",
       "      <td>87</td>\n",
       "      <td>60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>88</td>\n",
       "      <td>40</td>\n",
       "      <td>43</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            math_score  english_score  chinese_score\n",
       "student_id                                          \n",
       "1                   56             66             70\n",
       "2                   90             45             34\n",
       "3                   45             32             55\n",
       "4                   70             77             89\n",
       "5                   56             80             70\n",
       "6                   60             54             55\n",
       "7                   45             70             79\n",
       "8                   34             77             76\n",
       "9                   25             87             60\n",
       "10                  88             40             43"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "score_df = pd.DataFrame([[1,56,66,70], \n",
    "              [2,90,45,34],\n",
    "              [3,45,32,55],\n",
    "              [4,70,77,89],\n",
    "              [5,56,80,70],\n",
    "              [6,60,54,55],\n",
    "              [7,45,70,79],\n",
    "              [8,34,77,76],\n",
    "              [9,25,87,60],\n",
    "              [10,88,40,43]],columns=['student_id','math_score','english_score','chinese_score'])\n",
    "score_df = score_df.set_index('student_id')\n",
    "score_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "qNfiH_CnY3Us"
   },
   "outputs": [],
   "source": [
    "#1.6號學生(student_id=6)3科平均分數為何?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "56.333333333333336"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "score_df.loc[6].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "O-TfIm7oaAao"
   },
   "outputs": [],
   "source": [
    "#2. 6號學生3科平均分數是否有贏過班上一半的同學"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "59.833333333333336"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#not yet\n",
    "score_df.mean(axis=1).median()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "QNA0WHCIapPB"
   },
   "outputs": [],
   "source": [
    "#由於班上同學成績不好，所以學校統一加分，加分方式為開根號乘以十，請問6號同學3科成績分別是?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "math_score       77.459667\n",
       "english_score    73.484692\n",
       "chinese_score    74.161985\n",
       "Name: 6, dtype: float64"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "score_df.loc[6].apply(lambda x : (x**(1/2))*10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "BG_bdbRFa6X4"
   },
   "outputs": [],
   "source": [
    "#承上題，加分後各科班平均變多少"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "id": "2ZtRukpObNZx"
   },
   "outputs": [],
   "source": [
    "score_df_new = score_df.apply(lambda x : (x**(1/2))*10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>math_score</th>\n",
       "      <th>english_score</th>\n",
       "      <th>chinese_score</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>student_id</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>74.833148</td>\n",
       "      <td>81.240384</td>\n",
       "      <td>83.666003</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>94.868330</td>\n",
       "      <td>67.082039</td>\n",
       "      <td>58.309519</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>67.082039</td>\n",
       "      <td>56.568542</td>\n",
       "      <td>74.161985</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>83.666003</td>\n",
       "      <td>87.749644</td>\n",
       "      <td>94.339811</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>74.833148</td>\n",
       "      <td>89.442719</td>\n",
       "      <td>83.666003</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>77.459667</td>\n",
       "      <td>73.484692</td>\n",
       "      <td>74.161985</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>67.082039</td>\n",
       "      <td>83.666003</td>\n",
       "      <td>88.881944</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>58.309519</td>\n",
       "      <td>87.749644</td>\n",
       "      <td>87.177979</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>50.000000</td>\n",
       "      <td>93.273791</td>\n",
       "      <td>77.459667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>93.808315</td>\n",
       "      <td>63.245553</td>\n",
       "      <td>65.574385</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            math_score  english_score  chinese_score\n",
       "student_id                                          \n",
       "1            74.833148      81.240384      83.666003\n",
       "2            94.868330      67.082039      58.309519\n",
       "3            67.082039      56.568542      74.161985\n",
       "4            83.666003      87.749644      94.339811\n",
       "5            74.833148      89.442719      83.666003\n",
       "6            77.459667      73.484692      74.161985\n",
       "7            67.082039      83.666003      88.881944\n",
       "8            58.309519      87.749644      87.177979\n",
       "9            50.000000      93.273791      77.459667\n",
       "10           93.808315      63.245553      65.574385"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "score_df_new"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "math_score       74.194221\n",
       "english_score    78.350301\n",
       "chinese_score    78.739928\n",
       "dtype: float64"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "score_df_new.mean(axis=0)"
   ]
  }
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