{
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     "start_time": "2025-04-02T07:57:55.984261Z"
    }
   },
   "cell_type": "code",
   "source": "import pandas as pd",
   "id": "3244cf38b10be81b",
   "outputs": [],
   "execution_count": 1
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-04-02T08:00:15.267189Z",
     "start_time": "2025-04-02T08:00:15.229542Z"
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   "cell_type": "code",
   "source": [
    "data = pd.read_csv('data/某地区房屋销售数据 (1).csv', encoding='gbk')\n",
    "data['new_postcode'] = data['地区邮编'].apply(lambda x: str(x)[:2])\n",
    "data.head(3)"
   ],
   "id": "d973cf9fe6ac90a6",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "          房屋出售时间  地区邮编    房屋价格   房屋类型  配套房间数 new_postcode\n",
       "0  2010/1/4 0:00  2615  435000  house      3           26\n",
       "1  2010/1/5 0:00  2904  712000  house      4           29\n",
       "2  2010/1/6 0:00  2617  435000  house      4           26"
      ],
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>房屋出售时间</th>\n",
       "      <th>地区邮编</th>\n",
       "      <th>房屋价格</th>\n",
       "      <th>房屋类型</th>\n",
       "      <th>配套房间数</th>\n",
       "      <th>new_postcode</th>\n",
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       "      <th>0</th>\n",
       "      <td>2010/1/4 0:00</td>\n",
       "      <td>2615</td>\n",
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       "      <td>26</td>\n",
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       "      <th>1</th>\n",
       "      <td>2010/1/5 0:00</td>\n",
       "      <td>2904</td>\n",
       "      <td>712000</td>\n",
       "      <td>house</td>\n",
       "      <td>4</td>\n",
       "      <td>29</td>\n",
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       "      <th>2</th>\n",
       "      <td>2010/1/6 0:00</td>\n",
       "      <td>2617</td>\n",
       "      <td>435000</td>\n",
       "      <td>house</td>\n",
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       "      <td>26</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 4
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-04-02T08:00:25.320359Z",
     "start_time": "2025-04-02T08:00:25.301349Z"
    }
   },
   "cell_type": "code",
   "source": [
    "# 1、求出不同地区和不同房间数的房价，使用pivot_table函数\n",
    "data.pivot_table(values='房屋价格', index='new_postcode', columns='配套房间数', aggfunc='mean')"
   ],
   "id": "c9d4b29b2fbd4334",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "配套房间数                0              1              2             3  \\\n",
       "new_postcode                                                         \n",
       "26            564125.0  343189.962401  457595.588277  624204.46900   \n",
       "29            528000.0  292934.514286  381675.627240  475210.25609   \n",
       "\n",
       "配套房间数                     4             5  \n",
       "new_postcode                               \n",
       "26            810389.319007  1.037034e+06  \n",
       "29            651102.874716  7.995584e+05  "
      ],
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       "      <th></th>\n",
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       "      <td>1.037034e+06</td>\n",
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     "execution_count": 5,
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   ],
   "execution_count": 5
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-04-02T08:04:02.430064Z",
     "start_time": "2025-04-02T08:04:02.415284Z"
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   },
   "cell_type": "code",
   "source": [
    "# 2、不同地区哪种类型的房产房价最贵，使用pivot_table函数\n",
    "data.pivot_table(values='房屋价格', index='new_postcode', columns='房屋类型', aggfunc='max')"
   ],
   "id": "a5e4f3321d168313",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "房屋类型            house     unit\n",
       "new_postcode                  \n",
       "26            8000000  2500000\n",
       "29            5425000   769500"
      ],
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     "execution_count": 7,
     "metadata": {},
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   ],
   "execution_count": 7
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  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-04-02T08:04:41.035870Z",
     "start_time": "2025-04-02T08:04:41.012959Z"
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   },
   "cell_type": "code",
   "source": [
    "# 3、不同类型房产和不同房间数的房价之间的比较，使用pivot_table函数\n",
    "data.pivot_table(values='房屋价格', index='房屋类型', columns='配套房间数', aggfunc='mean')"
   ],
   "id": "4ed9b36daea1c503",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "配套房间数              0              1              2              3  \\\n",
       "房屋类型                                                                \n",
       "house  677394.736842  353634.269663  489555.889339  560117.683516   \n",
       "unit   330850.000000  336570.325391  432502.153116  594535.982287   \n",
       "\n",
       "配套房间数              4             5  \n",
       "房屋类型                                \n",
       "house  730667.024375  9.290297e+05  \n",
       "unit   641736.842105  1.146333e+06  "
      ],
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      ]
     },
     "execution_count": 8,
     "metadata": {},
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    }
   ],
   "execution_count": 8
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-04-02T08:05:23.703349Z",
     "start_time": "2025-04-02T08:05:23.691916Z"
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   },
   "cell_type": "code",
   "source": [
    "# 4、不同地区不同房间数房屋销售情况交叉表，使用crosstab函数，参考例3-61\n",
    "pd.crosstab(data['new_postcode'], data['配套房间数'])"
   ],
   "id": "799d99489d93b2b5",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "配套房间数          0     1     2     3     4     5\n",
       "new_postcode                                  \n",
       "26            24  1383  2815  6371  4793  1007\n",
       "29             5   175   558  4557  4845   835"
      ],
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     "execution_count": 10,
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