Book1_Ch22_Python_Codes/Bk1_Ch22_09.ipynb

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    "Chapter 22\n",
    "# 利用melt() 将宽格式转换为长格式\n",
    "Book_1《编程不难》 | 鸢尾花书：从加减乘除到机器学习  "
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   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
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    "data = {\n",
    "    'Student ID': ['1', '2', '3', '4'],\n",
    "    'Art':        [4, 3, 5, 4],\n",
    "    'Math':       [3, 4, 5, 3],\n",
    "    'Science':    [5, 4, 3, 4]}"
   ]
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Student ID</th>\n",
       "      <th>Art</th>\n",
       "      <th>Math</th>\n",
       "      <th>Science</th>\n",
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       "      <td>1</td>\n",
       "      <td>4</td>\n",
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       "      <td>5</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
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       "      <td>4</td>\n",
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       "  Student ID  Art  Math  Science\n",
       "0          1    4     3        5\n",
       "1          2    3     4        4\n",
       "2          3    5     5        3\n",
       "3          4    4     3        4"
      ]
     },
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   "source": [
    "df = pd.DataFrame(data)\n",
    "df"
   ]
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  {
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   "execution_count": 4,
   "id": "7c4bd88b-4efd-4342-9350-8a8d04fa470c",
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>Subject</th>\n",
       "      <th>Student ID</th>\n",
       "      <th>Art</th>\n",
       "      <th>Math</th>\n",
       "      <th>Science</th>\n",
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      "text/plain": [
       "Subject Student ID  Art  Math  Science\n",
       "0                1    4     3        5\n",
       "1                2    3     4        4\n",
       "2                3    5     5        3\n",
       "3                4    4     3        4"
      ]
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    "df.columns.names = ['Subject']\n",
    "df"
   ]
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   "execution_count": 5,
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Student ID</th>\n",
       "      <th>Subject</th>\n",
       "      <th>Score</th>\n",
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       "      <th>6</th>\n",
       "      <td>3</td>\n",
       "      <td>Math</td>\n",
       "      <td>5</td>\n",
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       "      <th>7</th>\n",
       "      <td>4</td>\n",
       "      <td>Math</td>\n",
       "      <td>3</td>\n",
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       "      <th>8</th>\n",
       "      <td>1</td>\n",
       "      <td>Science</td>\n",
       "      <td>5</td>\n",
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       "      <th>9</th>\n",
       "      <td>2</td>\n",
       "      <td>Science</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>3</td>\n",
       "      <td>Science</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>4</td>\n",
       "      <td>Science</td>\n",
       "      <td>4</td>\n",
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      "text/plain": [
       "   Student ID  Subject  Score\n",
       "0           1      Art      4\n",
       "1           2      Art      3\n",
       "2           3      Art      5\n",
       "3           4      Art      4\n",
       "4           1     Math      3\n",
       "5           2     Math      4\n",
       "6           3     Math      5\n",
       "7           4     Math      3\n",
       "8           1  Science      5\n",
       "9           2  Science      4\n",
       "10          3  Science      3\n",
       "11          4  Science      4"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "melted_df = df.melt(id_vars='Student ID', \n",
    "                    var_name='Subject', \n",
    "                    value_vars=['Art','Math','Science'], \n",
    "                    value_name='Score')\n",
    "melted_df"
   ]
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