Task06.ipynb

{
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  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<center><h1>第六章 连接</h1></center>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
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     "end_time": "2020-12-29T10:35:38.553532Z",
     "start_time": "2020-12-29T10:35:37.184961Z"
    }
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 一、关系型连接\n",
    "### 1. 连接的基本概念\n",
    "### 2. 值连接\n",
    "\n",
    "在上面示意图中的例子中，两张表根据某一列的值来连接，事实上还可以通过几列值的组合进行连接，这种基于值的连接在`pandas`中可以由`merge`函数实现，例如第一张图的左连接："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-12-27T15:17:03.583821Z",
     "start_time": "2020-12-27T15:17:03.489395Z"
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   "outputs": [
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Name</th>\n",
       "      <th>Age</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>San Zhang</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Si Li</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      "text/plain": [
       "        Name  Age\n",
       "0  San Zhang   20\n",
       "1      Si Li   30"
      ]
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     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1 = pd.DataFrame({'Name':['San Zhang','Si Li'], 'Age':[20,30]})\n",
    "df2 = pd.DataFrame({'Name':['Si Li','Wu Wang'], 'Gender':['F','M']})\n",
    "df1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
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    "ExecuteTime": {
     "end_time": "2020-12-27T15:17:09.126443Z",
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       "      <td>Si Li</td>\n",
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       "      <th>1</th>\n",
       "      <td>Wu Wang</td>\n",
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      "text/plain": [
       "      Name Gender\n",
       "0    Si Li      F\n",
       "1  Wu Wang      M"
      ]
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     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
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   "source": [
    "df2"
   ]
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   "execution_count": 4,
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       "      <th></th>\n",
       "      <th>Name</th>\n",
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       "      <th>Gender</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>San Zhang</td>\n",
       "      <td>20</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Si Li</td>\n",
       "      <td>30</td>\n",
       "      <td>F</td>\n",
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      "text/plain": [
       "        Name  Age Gender\n",
       "0  San Zhang   20    NaN\n",
       "1      Si Li   30      F"
      ]
     },
     "execution_count": 4,
     "metadata": {},
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   ],
   "source": [
    "df1.merge(df2, on = 'Name', how = 'left')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "如果两个表中想要连接的列不具备相同的列名，可以通过`left_on`和`right_on`指定："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-12-27T15:20:22.914217Z",
     "start_time": "2020-12-27T15:20:22.878574Z"
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       "      <th>df1_name</th>\n",
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       "      <td>San Zhang</td>\n",
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      "text/plain": [
       "    df1_name  Age\n",
       "0  San Zhang   20\n",
       "1      Si Li   30"
      ]
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     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1 = pd.DataFrame({'df1_name':['San Zhang','Si Li'], 'Age':[20,30]})\n",
    "df1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
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       "      <td>Wu Wang</td>\n",
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      "text/plain": [
       "  df2_name Gender\n",
       "0    Si Li      F\n",
       "1  Wu Wang      M"
      ]
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     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "df2 = pd.DataFrame({'df2_name':['Si Li','Wu Wang'], 'Gender':['F','M']})\n",
    "df2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
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     "end_time": "2020-12-27T15:23:15.934646Z",
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    {
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      "text/html": [
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>df1_name</th>\n",
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       "      <td>San Zhang</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <th>1</th>\n",
       "      <td>Si Li</td>\n",
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       "      <td>F</td>\n",
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      "text/plain": [
       "    df1_name  Age df2_name Gender\n",
       "0  San Zhang   20      NaN    NaN\n",
       "1      Si Li   30    Si Li      F"
      ]
     },
     "execution_count": 7,
     "metadata": {},
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   ],
   "source": [
    "df1.merge(df2, left_on='df1_name', right_on='df2_name', how = 'left')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "如果两个表中的列出现了重复的列名，那么可以通过`suffixes`参数指定。例如合并考试成绩的时候，第一个表记录了语文成绩，第二个是数学成绩："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
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     "end_time": "2020-12-27T15:25:49.790185Z",
     "start_time": "2020-12-27T15:25:49.752190Z"
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Name</th>\n",
       "      <th>Grade</th>\n",
       "    </tr>\n",
       "  </thead>\n",
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       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>San Zhang</td>\n",
       "      <td>70</td>\n",
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      "text/plain": [
       "        Name  Grade\n",
       "0  San Zhang     70"
      ]
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     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "df1 = pd.DataFrame({'Name':['San Zhang'],'Grade':[70]})\n",
    "df1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
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     "end_time": "2020-12-27T15:26:07.042634Z",
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       "        Name  Grade\n",
       "0  San Zhang     80"
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     "execution_count": 9,
     "metadata": {},
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   ],
   "source": [
    "df2 = pd.DataFrame({'Name':['San Zhang'],'Grade':[80]})\n",
    "df2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-12-27T15:27:55.921218Z",
     "start_time": "2020-12-27T15:27:55.868223Z"
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   "outputs": [
    {
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       "      <th>Name</th>\n",
       "      <th>Grade_Chinese</th>\n",
       "      <th>Grade_Math</th>\n",
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       "      <td>San Zhang</td>\n",
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      "text/plain": [
       "        Name  Grade_Chinese  Grade_Math\n",
       "0  San Zhang             70          80"
      ]
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     "execution_count": 10,
     "metadata": {},
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   ],
   "source": [
    "df1.merge(df2, on = 'Name', how = 'left', suffixes=['_Chinese', '_Math'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "在某些时候出现重复元素是麻烦的，例如两位同学来自不同的班级，但是姓名相同，这种时候就要指定`on`参数为多个列使得正确连接："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-12-27T15:28:17.366597Z",
     "start_time": "2020-12-27T15:28:17.325610Z"
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   "outputs": [
    {
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       "      <th>0</th>\n",
       "      <td>San Zhang</td>\n",
       "      <td>20</td>\n",
       "      <td>one</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>San Zhang</td>\n",
       "      <td>21</td>\n",
       "      <td>two</td>\n",
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      ],
      "text/plain": [
       "        Name  Age Class\n",
       "0  San Zhang   20   one\n",
       "1  San Zhang   21   two"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1 = pd.DataFrame({'Name':['San Zhang', 'San Zhang'],\n",
    "                    'Age':[20, 21],\n",
    "                    'Class':['one', 'two']})\n",
    "df1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-12-27T15:28:29.820933Z",
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   "outputs": [
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       "      <td>San Zhang</td>\n",
       "      <td>F</td>\n",
       "      <td>two</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>San Zhang</td>\n",
       "      <td>M</td>\n",
       "      <td>one</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Name Gender Class\n",
       "0  San Zhang      F   two\n",
       "1  San Zhang      M   one"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2 = pd.DataFrame({'Name':['San Zhang', 'San Zhang'],\n",
    "                    'Gender':['F', 'M'],\n",
    "                    'Class':['two', 'one']})\n",
    "df2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-12-27T15:29:04.984142Z",
     "start_time": "2020-12-27T15:29:04.910155Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "      <th></th>\n",
       "      <th>Name</th>\n",
       "      <th>Age</th>\n",
       "      <th>Class_x</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Class_y</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>San Zhang</td>\n",
       "      <td>20</td>\n",
       "      <td>one</td>\n",
       "      <td>F</td>\n",
       "      <td>two</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>San Zhang</td>\n",
       "      <td>20</td>\n",
       "      <td>one</td>\n",
       "      <td>M</td>\n",
       "      <td>one</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>San Zhang</td>\n",
       "      <td>21</td>\n",
       "      <td>two</td>\n",
       "      <td>F</td>\n",
       "      <td>two</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>San Zhang</td>\n",
       "      <td>21</td>\n",
       "      <td>two</td>\n",
       "      <td>M</td>\n",
       "      <td>one</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Name  Age Class_x Gender Class_y\n",
       "0  San Zhang   20     one      F     two\n",
       "1  San Zhang   20     one      M     one\n",
       "2  San Zhang   21     two      F     two\n",
       "3  San Zhang   21     two      M     one"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 错误的结果\n",
    "df1.merge(df2, on='Name', how='left')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-12-27T15:29:57.384281Z",
     "start_time": "2020-12-27T15:29:57.331320Z"
    },
    "code_folding": [
     0
    ]
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "      <th>Name</th>\n",
       "      <th>Age</th>\n",
       "      <th>Class</th>\n",
       "      <th>Gender</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>San Zhang</td>\n",
       "      <td>20</td>\n",
       "      <td>one</td>\n",
       "      <td>M</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>San Zhang</td>\n",
       "      <td>21</td>\n",
       "      <td>two</td>\n",
       "      <td>F</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      "text/plain": [
       "        Name  Age Class Gender\n",
       "0  San Zhang   20   one      M\n",
       "1  San Zhang   21   two      F"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 正确的结果\n",
    "df1.merge(df2, on=['Name', 'Class'], how='left')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "从上面的例子来看，在进行基于唯一性的连接下，如果键不是唯一的，那么结果就会产生问题。举例中的行数很少，但如果实际数据中有几十万到上百万行的进行合并时，如果想要保证唯一性，除了用`duplicated`检查是否重复外，`merge`中也提供了`validate`参数来检查连接的唯一性模式。这里共有三种模式，即一对一连接`1:1`，一对多连接`1:m`，多对一连接`m:1`连接，第一个是指左右表的键都是唯一的，后面两个分别指左表键唯一和右表键唯一。\n",
    "\n",
    "#### 【练一练】\n",
    "上面以多列为键的例子中，错误写法显然是一种多对多连接，而正确写法是一对一连接，请修改原表，使得以多列为键的正确写法能够通过`validate='1:m'`的检验，但不能通过`validate='m:1'`的检验。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-12-27T15:39:14.934832Z",
     "start_time": "2020-12-27T15:39:14.834846Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "      <th>Name</th>\n",
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       "      <th>Class</th>\n",
       "      <th>Gender</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>San Zhang</td>\n",
       "      <td>20</td>\n",
       "      <td>one</td>\n",
       "      <td>M</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>San Zhang</td>\n",
       "      <td>21</td>\n",
       "      <td>two</td>\n",
       "      <td>F</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Name  Age Class Gender\n",
       "0  San Zhang   20   one      M\n",
       "1  San Zhang   21   two      F"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1.merge(df2,on=['Name','Class'],how='left', validate=\"1:1\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-12-27T15:41:46.955974Z",
     "start_time": "2020-12-27T15:41:46.850959Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>San Zhang</td>\n",
       "      <td>20</td>\n",
       "      <td>one</td>\n",
       "      <td>F</td>\n",
       "      <td>two</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>San Zhang</td>\n",
       "      <td>20</td>\n",
       "      <td>one</td>\n",
       "      <td>M</td>\n",
       "      <td>one</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>San Zhang</td>\n",
       "      <td>21</td>\n",
       "      <td>two</td>\n",
       "      <td>F</td>\n",
       "      <td>two</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>San Zhang</td>\n",
       "      <td>21</td>\n",
       "      <td>two</td>\n",
       "      <td>M</td>\n",
       "      <td>one</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Name  Age Class_x Gender Class_y\n",
       "0  San Zhang   20     one      F     two\n",
       "1  San Zhang   20     one      M     one\n",
       "2  San Zhang   21     two      F     two\n",
       "3  San Zhang   21     two      M     one"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 如果修改原表，初步考虑是将name和class进行拼接，但方法不具备推广性。上面是正确的结果，下面是错误的结果\n",
    "df1.merge(df2,on='Name',how='left', validate=\"m:m\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 【END】\n",
    "\n",
    "### 3. 索引连接\n",
    "\n",
    "所谓索引连接，就是把索引当作键，因此这和值连接本质上没有区别，`pandas`中利用`join`函数来处理索引连接，它的参数选择要少于`merge`，除了必须的`on`和`how`之外，可以对重复的列指定左右后缀`lsuffix`和`rsuffix`。其中，`on`参数指索引名，单层索引时省略参数表示按照当前索引连接。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-12-27T15:43:26.519840Z",
     "start_time": "2020-12-27T15:43:26.470849Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Age</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Name</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>San Zhang</th>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Si Li</th>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           Age\n",
       "Name          \n",
       "San Zhang   20\n",
       "Si Li       30"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1 = pd.DataFrame({'Age':[20,30]}, index=pd.Series(['San Zhang','Si Li'],name='Name'))\n",
    "df1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-12-27T15:43:37.807138Z",
     "start_time": "2020-12-27T15:43:37.743147Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Gender</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Name</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Si Li</th>\n",
       "      <td>F</td>\n",
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       "    <tr>\n",
       "      <th>Wu Wang</th>\n",
       "      <td>M</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Gender\n",
       "Name          \n",
       "Si Li        F\n",
       "Wu Wang      M"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2 = pd.DataFrame({'Gender':['F','M']}, index=pd.Series(['Si Li','Wu Wang'],name='Name'))\n",
    "df2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-12-27T15:43:51.826387Z",
     "start_time": "2020-12-27T15:43:51.770362Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
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       "      <th>Age</th>\n",
       "      <th>Gender</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Name</th>\n",
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       "      <th></th>\n",
       "    </tr>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>San Zhang</th>\n",
       "      <td>20</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Si Li</th>\n",
       "      <td>30</td>\n",
       "      <td>F</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           Age Gender\n",
       "Name                 \n",
       "San Zhang   20    NaN\n",
       "Si Li       30      F"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1.join(df2, how='left')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "仿照第2小节的例子，写出语文和数学分数合并的`join`版本："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-12-27T15:46:17.284290Z",
     "start_time": "2020-12-27T15:46:17.221288Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<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>Grade_Chinese</th>\n",
       "      <th>Grade_Math</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Name</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>San Zhang</th>\n",
       "      <td>70</td>\n",
       "      <td>80</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           Grade_Chinese  Grade_Math\n",
       "Name                                \n",
       "San Zhang             70          80"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1 = pd.DataFrame({'Grade':[70]}, index=pd.Series(['San Zhang'], name='Name'))\n",
    "df2 = pd.DataFrame({'Grade':[80]}, index=pd.Series(['San Zhang'], name='Name'))\n",
    "df1.join(df2, how='left', lsuffix='_Chinese', rsuffix='_Math')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "如果想要进行类似于`merge`中以多列为键的操作的时候，`join`需要使用多级索引，例如在`merge`中的最后一个例子可以如下写出："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-12-27T15:47:05.599653Z",
     "start_time": "2020-12-27T15:47:05.525666Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "\n",
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       "        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></th>\n",
       "      <th>Age</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Name</th>\n",
       "      <th>Class</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">San Zhang</th>\n",
       "      <th>one</th>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>two</th>\n",
       "      <td>21</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 Age\n",
       "Name      Class     \n",
       "San Zhang one     20\n",
       "          two     21"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1 = pd.DataFrame({'Age':[20,21]}, index=pd.MultiIndex.from_arrays([['San Zhang', 'San Zhang'],['one', 'two']], names=('Name','Class')))\n",
    "df1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-12-27T15:47:50.559514Z",
     "start_time": "2020-12-27T15:47:50.477530Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>Gender</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Name</th>\n",
       "      <th>Class</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">San Zhang</th>\n",
       "      <th>two</th>\n",
       "      <td>F</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>one</th>\n",
       "      <td>M</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                Gender\n",
       "Name      Class       \n",
       "San Zhang two        F\n",
       "          one        M"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2 = pd.DataFrame({'Gender':['F', 'M']}, index=pd.MultiIndex.from_arrays([['San Zhang', 'San Zhang'],['two', 'one']], names=('Name','Class')))\n",
    "df2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-12-27T15:48:02.074402Z",
     "start_time": "2020-12-27T15:48:02.028406Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "      <th></th>\n",
       "      <th>Age</th>\n",
       "      <th>Gender</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Name</th>\n",
       "      <th>Class</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">San Zhang</th>\n",
       "      <th>one</th>\n",
       "      <td>20</td>\n",
       "      <td>M</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>two</th>\n",
       "      <td>21</td>\n",
       "      <td>F</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 Age Gender\n",
       "Name      Class            \n",
       "San Zhang one     20      M\n",
       "          two     21      F"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1.join(df2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 二、方向连接\n",
    "### 1. concat\n",
    "\n",
    "前面介绍了关系型连接，其中最重要的参数是`on`和`how`，但有时候用户并不关心以哪一列为键来合并，只是希望把两个表或者多个表按照纵向或者横向拼接，为这种需求，`pandas`中提供了`concat`函数来实现。\n",
    "\n",
    "在`concat`中，最常用的有三个参数，它们是`axis, join, keys`，分别表示拼接方向，连接形式，以及在新表中指示来自于哪一张旧表的名字。这里需要特别注意，`join`和`keys`与之前提到的`join`函数和键的概念没有任何关系。\n",
    "\n",
    "在默认状态下的`axis=0`，表示纵向拼接多个表，常常用于多个样本的拼接；而`axis=1`表示横向拼接多个表，常用于多个字段或特征的拼接。\n",
    "\n",
    "例如，纵向合并各表中人的信息："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-12-27T15:51:24.845807Z",
     "start_time": "2020-12-27T15:51:24.790816Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Name</th>\n",
       "      <th>Age</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>San Zhang</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Si Li</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Wu Wang</td>\n",
       "      <td>40</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Name  Age\n",
       "0  San Zhang   20\n",
       "1      Si Li   30\n",
       "0    Wu Wang   40"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1 = pd.DataFrame({'Name':['San Zhang','Si Li'], 'Age':[20,30]})\n",
    "df2 = pd.DataFrame({'Name':['Wu Wang'], 'Age':[40]})\n",
    "pd.concat([df1, df2])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "横向合并各表中的字段："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-12-27T15:52:10.460517Z",
     "start_time": "2020-12-27T15:52:10.381516Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
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       "      <th></th>\n",
       "      <th>Name</th>\n",
       "      <th>Age</th>\n",
       "      <th>Grade</th>\n",
       "      <th>Gender</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>San Zhang</td>\n",
       "      <td>20</td>\n",
       "      <td>80</td>\n",
       "      <td>M</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Si Li</td>\n",
       "      <td>30</td>\n",
       "      <td>90</td>\n",
       "      <td>F</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Name  Age  Grade Gender\n",
       "0  San Zhang   20     80      M\n",
       "1      Si Li   30     90      F"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2 = pd.DataFrame({'Grade':[80, 90]})\n",
    "df3 = pd.DataFrame({'Gender':['M', 'F']})\n",
    "pd.concat([df1, df2, df3], 1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "虽然说`concat`是处理关系型合并的函数，但是它仍然是关于索引进行连接的。纵向拼接会根据列索引对其，默认状态下`join=outer`，表示保留所有的列，并将不存在的值设为缺失；`join=inner`，表示保留两个表都出现过的列。横向拼接则根据行索引对齐，`join`参数可以类似设置。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-12-27T15:54:05.985374Z",
     "start_time": "2020-12-27T15:54:05.911821Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>San Zhang</td>\n",
       "      <td>20.0</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Si Li</td>\n",
       "      <td>30.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Wu Wang</td>\n",
       "      <td>NaN</td>\n",
       "      <td>M</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Name   Age Gender\n",
       "0  San Zhang  20.0    NaN\n",
       "1      Si Li  30.0    NaN\n",
       "0    Wu Wang   NaN      M"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2 = pd.DataFrame({'Name':['Wu Wang'], 'Gender':['M']})\n",
    "pd.concat([df1, df2])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-12-27T15:54:47.432278Z",
     "start_time": "2020-12-27T15:54:47.346292Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
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       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
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       "      <td>San Zhang</td>\n",
       "      <td>20.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Si Li</td>\n",
       "      <td>30.0</td>\n",
       "      <td>80.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>90.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Name   Age  Grade\n",
       "0  San Zhang  20.0    NaN\n",
       "1      Si Li  30.0   80.0\n",
       "2        NaN   NaN   90.0"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2 = pd.DataFrame({'Grade':[80, 90]}, index=[1, 2])\n",
    "pd.concat([df1, df2], 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-12-27T15:59:19.677609Z",
     "start_time": "2020-12-27T15:59:19.637614Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Name</th>\n",
       "      <th>Age</th>\n",
       "      <th>Grade</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Si Li</td>\n",
       "      <td>30</td>\n",
       "      <td>80</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Name  Age  Grade\n",
       "1  Si Li   30     80"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 这里连接靠的是索引\n",
    "pd.concat([df1, df2], axis=1, join='inner')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "因此，当确认要使用多表直接的方向合并时，尤其是横向的合并，可以先用`reset_index`方法恢复默认整数索引再进行合并，防止出现由索引的误对齐和重复索引的笛卡尔积带来的错误结果。\n",
    "\n",
    "最后，`keys`参数的使用场景在于多个表合并后，用户仍然想要知道新表中的数据来自于哪个原表，这时可以通过`keys`参数产生多级索引进行标记。例如，第一个表中都是一班的同学，而第二个表中都是二班的同学，可以使用如下方式合并："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-12-27T16:03:08.288359Z",
     "start_time": "2020-12-27T16:03:08.232366Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>Name</th>\n",
       "      <th>Age</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">one</th>\n",
       "      <th>0</th>\n",
       "      <td>San Zhang</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Si Li</td>\n",
       "      <td>21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>two</th>\n",
       "      <th>0</th>\n",
       "      <td>Wu Wang</td>\n",
       "      <td>21</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Name  Age\n",
       "one 0  San Zhang   20\n",
       "    1      Si Li   21\n",
       "two 0    Wu Wang   21"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1 = pd.DataFrame({'Name':['San Zhang','Si Li'], 'Age':[20,21]})\n",
    "df2 = pd.DataFrame({'Name':['Wu Wang'],'Age':[21]})\n",
    "pd.concat([df1, df2], keys=['one', 'two'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2. 序列与表的合并\n",
    "\n",
    "利用`concat`可以实现多个表之间的方向拼接，如果想要把一个序列追加到表的行末或者列末，则可以分别使用`append`和`assign`方法。\n",
    "\n",
    "在`append`中，如果原表是默认整数序列的索引，那么可以使用`ignore_index=True`对新序列对应索引的自动标号，否则必须对`Series`指定`name`属性。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-12-27T16:04:37.112274Z",
     "start_time": "2020-12-27T16:04:37.085743Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Name    Wu Wang\n",
       "Age          21\n",
       "dtype: object"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s = pd.Series(['Wu Wang', 21], index = df1.columns)\n",
    "s"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-12-27T16:04:45.978042Z",
     "start_time": "2020-12-27T16:04:45.920052Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th>Age</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>San Zhang</td>\n",
       "      <td>20</td>\n",
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       "      <th>1</th>\n",
       "      <td>Si Li</td>\n",
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       "      <th>2</th>\n",
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      "text/plain": [
       "        Name  Age\n",
       "0  San Zhang   20\n",
       "1      Si Li   21\n",
       "2    Wu Wang   21"
      ]
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     "execution_count": 36,
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   "source": [
    "df1.append(s, ignore_index=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "对于`assign`而言，虽然可以利用其添加新的列，但一般通过`df['new_col'] = ...`的形式就可以等价地添加新列。同时，使用`[]`修改的缺点是它会直接在原表上进行改动，而`assign`返回的是一个临时副本："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-12-27T16:06:34.539062Z",
     "start_time": "2020-12-27T16:06:34.499067Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "      <th></th>\n",
       "      <th>Name</th>\n",
       "      <th>Age</th>\n",
       "      <th>Grade</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>San Zhang</td>\n",
       "      <td>20</td>\n",
       "      <td>80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Si Li</td>\n",
       "      <td>21</td>\n",
       "      <td>90</td>\n",
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      "text/plain": [
       "        Name  Age  Grade\n",
       "0  San Zhang   20     80\n",
       "1      Si Li   21     90"
      ]
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     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "s = pd.Series([80, 90])\n",
    "df1.assign(Grade=s)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-12-27T16:06:40.270367Z",
     "start_time": "2020-12-27T16:06:40.241375Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "  <tbody>\n",
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       "      <th>0</th>\n",
       "      <td>San Zhang</td>\n",
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       "      <td>Si Li</td>\n",
       "      <td>21</td>\n",
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       "</table>\n",
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      "text/plain": [
       "        Name  Age\n",
       "0  San Zhang   20\n",
       "1      Si Li   21"
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     "execution_count": 38,
     "metadata": {},
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   "source": [
    "# assign 是临时副本\n",
    "df1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-12-27T16:06:48.949338Z",
     "start_time": "2020-12-27T16:06:48.920347Z"
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   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "      <th></th>\n",
       "      <th>Name</th>\n",
       "      <th>Age</th>\n",
       "      <th>Grade</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>San Zhang</td>\n",
       "      <td>20</td>\n",
       "      <td>80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Si Li</td>\n",
       "      <td>21</td>\n",
       "      <td>90</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
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      "text/plain": [
       "        Name  Age  Grade\n",
       "0  San Zhang   20     80\n",
       "1      Si Li   21     90"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1['Grade'] = s\n",
    "df1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-12-27T16:07:49.848186Z",
     "start_time": "2020-12-27T16:07:49.830198Z"
    }
   },
   "source": [
    "## 三、类连接操作\n",
    "\n",
    "除了上述介绍的若干连接函数之外，`pandas`中还设计了一些函数能够对两个表进行某些操作，这里把它们统称为类连接操作。\n",
    "\n",
    "### 1. 比较\n",
    "\n",
    "`compare`是在`1.1.0`后引入的新函数，它能够比较两个表或者序列的不同处并将其汇总展示："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-12-29T10:36:11.547557Z",
     "start_time": "2020-12-29T10:36:11.224613Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
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       "      <th colspan=\"2\" halign=\"left\">Name</th>\n",
       "      <th colspan=\"2\" halign=\"left\">Class</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>self</th>\n",
       "      <th>other</th>\n",
       "      <th>self</th>\n",
       "      <th>other</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Si Li</td>\n",
       "      <td>Li Si</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>three</td>\n",
       "      <td>Three</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      ],
      "text/plain": [
       "    Name         Class       \n",
       "    self  other   self  other\n",
       "1  Si Li  Li Si    NaN    NaN\n",
       "2    NaN    NaN  three  Three"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1 = pd.DataFrame({'Name':['San Zhang', 'Si Li', 'Wu Wang'],\n",
    "                    'Age':[20, 21 ,21],\n",
    "                    'Class':['one', 'two', 'three']})\n",
    "df2 = pd.DataFrame({'Name':['San Zhang', 'Li Si', 'Wu Wang'],\n",
    "                    'Age':[20, 21 ,21],\n",
    "                    'Class':['one', 'two', 'Three']})\n",
    "df1.compare(df2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-12-27T16:09:58.797555Z",
     "start_time": "2020-12-27T16:09:46.296528Z"
    }
   },
   "source": [
    "结果中返回了不同值所在的行列，如果相同则会被填充为缺失值`NaN`，其中`other`和`self`分别指代传入的参数表和被调用的表自身。\n",
    "\n",
    "如果想要完整显示表中所有元素的比较情况，可以设置`keep_shape=True`："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df1.compare(df2, keep_shape=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2. 组合\n",
    "\n",
    "`combine`函数能够让两张表按照一定的规则进行组合，在进行规则比较时会自动进行列索引的对齐。对于传入的函数而言，每一次操作中输入的参数是来自两个表的同名`Series`，依次传入的列是两个表列名的并集，例如下面这个例子会依次传入`A,B,C,D`四组序列，每组为左右表的两个序列。同时，进行`A`列比较的时候，`s1`指代的就是一个全空的序列，因为它在被调用的表中并不存在，并且来自第一个表的序列索引会被`reindex`成两个索引的并集。具体的过程可以通过在传入的函数中插入适当的`print`方法查看。\n",
    "\n",
    "下面的例子表示选出对应索引位置较小的元素："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-12-27T16:21:04.900735Z",
     "start_time": "2020-12-27T16:21:04.850748Z"
    }
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   "outputs": [
    {
     "data": {
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       "   A  B  C\n",
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     "execution_count": 43,
     "metadata": {},
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   ],
   "source": [
    "def choose_min(s1, s2):\n",
    "    s2 = s2.reindex_like(s1)\n",
    "    res = s1.where(s1<s2, s2)\n",
    "    res = res.mask(s1.isna()) # isna表示是否为缺失值，返回布尔序列\n",
    "    return res\n",
    "df1 = pd.DataFrame({'A':[1,2], 'B':[3,4], 'C':[5,6]})\n",
    "df1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-12-27T16:21:17.227080Z",
     "start_time": "2020-12-27T16:21:17.184087Z"
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   "outputs": [
    {
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       "   B  C   D\n",
       "1  5  7   9\n",
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     "execution_count": 44,
     "metadata": {},
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   "source": [
    "df2 = pd.DataFrame({'B':[5,6], 'C':[7,8], 'D':[9,10]}, index=[1,2])\n",
    "df2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-12-27T16:21:34.059194Z",
     "start_time": "2020-12-27T16:21:33.951729Z"
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   "outputs": [
    {
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       "      <td>4.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>NaN</td>\n",
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       "      <th>2</th>\n",
       "      <td>NaN</td>\n",
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       "    A    B    C   D\n",
       "0 NaN  NaN  NaN NaN\n",
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     "execution_count": 45,
     "metadata": {},
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   ],
   "source": [
    "df1.combine(df2, choose_min)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 【练一练】\n",
    "请在上述代码的基础上修改，保留`df2`中4个未被`df1`替换的相应位置原始值。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-12-27T16:26:02.902465Z",
     "start_time": "2020-12-27T16:26:02.782479Z"
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       "      <th>D</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>NaN</td>\n",
       "      <td>4.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>9.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>NaN</td>\n",
       "      <td>6.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>10.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    A    B    C     D\n",
       "0 NaN  NaN  NaN   NaN\n",
       "1 NaN  4.0  6.0   9.0\n",
       "2 NaN  6.0  8.0  10.0"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def choose_min2(s1, s2):\n",
    "    s2 = s2.reindex_like(s1)\n",
    "    res = s1.where(s1<s2, s2)\n",
    "    res = res.mask(s1.isna(),s2) \n",
    "    return res\n",
    "df1.combine(df2, choose_min2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 【END】\n",
    "此外，设置`overtwrite`参数为`False`可以保留$\\color{red}{被调用表}$中未出现在传入的参数表中的列，而不会设置未缺失值："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-12-27T16:26:24.089062Z",
     "start_time": "2020-12-27T16:26:23.991075Z"
    }
   },
   "outputs": [
    {
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      "text/html": [
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       "     A    B    C   D\n",
       "0  1.0  NaN  NaN NaN\n",
       "1  2.0  4.0  6.0 NaN\n",
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     "execution_count": 48,
     "metadata": {},
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   ],
   "source": [
    "df1.combine(df2, choose_min, overwrite=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 【练一练】\n",
    "除了`combine`之外，`pandas`中还有一个`combine_first`方法，其功能是在对两张表组合时，若第二张表中的值在第一张表中对应索引位置的值不是缺失状态，那么就使用第一张表的值填充。下面给出一个例子，请用`combine`函数完成相同的功能。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-12-27T16:27:14.010163Z",
     "start_time": "2020-12-27T16:27:13.971167Z"
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       "   A    B\n",
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   "source": [
    "df1 = pd.DataFrame({'A':[1,2], 'B':[3,np.nan]})\n",
    "df1"
   ]
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  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-12-27T16:27:20.956142Z",
     "start_time": "2020-12-27T16:27:20.922156Z"
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       "   A  B\n",
       "1  5  7\n",
       "2  6  8"
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   "source": [
    "df2 = pd.DataFrame({'A':[5,6], 'B':[7,8]}, index=[1,2])\n",
    "df2"
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       "     A    B\n",
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     "metadata": {},
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   "source": [
    "df1.combine_first(df2)"
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  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-12-29T10:43:37.277097Z",
     "start_time": "2020-12-29T10:43:37.148106Z"
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   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "\n",
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       "      <th></th>\n",
       "      <th>Name</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>San Zhang</td>\n",
       "      <td>20</td>\n",
       "      <td>one</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Si Li</td>\n",
       "      <td>21</td>\n",
       "      <td>two</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Wu Wang</td>\n",
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       "      <td>three</td>\n",
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       "        Name  Age  Class\n",
       "0  San Zhang   20    one\n",
       "1      Si Li   21    two\n",
       "2    Wu Wang   21  three"
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     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "# 替换df2就行\n",
    "df1.combine(df2,lambda s1,s2 : s1.mask(s1.isna(),s2))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 四、练习\n",
    "### Ex1：美国疫情数据集\n",
    "\n",
    "现有美国4月12日至11月16日的疫情报表，请将`New York`的`Confirmed, Deaths, Recovered, Active`合并为一张表，索引为按如下方法生成的日期字符串序列"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-12-29T10:47:31.967723Z",
     "start_time": "2020-12-29T10:47:31.895742Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['04-12-2020', '04-13-2020', '04-14-2020', '04-15-2020', '04-16-2020']"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "date = pd.date_range('20200412', '20201116').to_series()\n",
    "date = date.dt.month.astype('string').str.zfill(2) +'-'+ date.dt.day.astype('string').str.zfill(2) +'-'+ '2020'\n",
    "date = date.tolist()\n",
    "date[:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-12-29T11:13:40.663640Z",
     "start_time": "2020-12-29T11:13:32.732040Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Confirmed</th>\n",
       "      <th>Deaths</th>\n",
       "      <th>Recovered</th>\n",
       "      <th>Active</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>04-12-2020</th>\n",
       "      <td>189033</td>\n",
       "      <td>9385</td>\n",
       "      <td>23887.0</td>\n",
       "      <td>179648.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>04-13-2020</th>\n",
       "      <td>195749</td>\n",
       "      <td>10058</td>\n",
       "      <td>23887.0</td>\n",
       "      <td>185691.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>04-14-2020</th>\n",
       "      <td>203020</td>\n",
       "      <td>10842</td>\n",
       "      <td>23887.0</td>\n",
       "      <td>192178.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>04-15-2020</th>\n",
       "      <td>214454</td>\n",
       "      <td>11617</td>\n",
       "      <td>23887.0</td>\n",
       "      <td>202837.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>04-16-2020</th>\n",
       "      <td>223691</td>\n",
       "      <td>14832</td>\n",
       "      <td>23887.0</td>\n",
       "      <td>208859.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Confirmed  Deaths  Recovered    Active\n",
       "04-12-2020     189033    9385    23887.0  179648.0\n",
       "04-13-2020     195749   10058    23887.0  185691.0\n",
       "04-14-2020     203020   10842    23887.0  192178.0\n",
       "04-15-2020     214454   11617    23887.0  202837.0\n",
       "04-16-2020     223691   14832    23887.0  208859.0"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "L = []\n",
    "for dt in date:\n",
    "    data = pd.read_csv(f'data/us_report/{dt}.csv', index_col='Province_State')\n",
    "    df = data.loc[['New York'],['Confirmed', 'Deaths', 'Recovered', 'Active']]\n",
    "    L.append(df)\n",
    "# L.index = data  -- 报错：'list' object attribute 'index' is read-only\n",
    "res = pd.concat(L)\n",
    "res.index = date\n",
    "res.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-12-29T11:14:25.347411Z",
     "start_time": "2020-12-29T11:14:25.333409Z"
    }
   },
   "outputs": [],
   "source": [
    "# 本打算自己写，遇到问题参考了部分答案，与答案效果类似； 也可以尝试用concat连接每个日期数据"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-12-29T11:02:11.676165Z",
     "start_time": "2020-12-29T11:02:10.864354Z"
    }
   },
   "source": [
    "### Ex2：实现join函数\n",
    "\n",
    "请实现带有`how`参数的`join`函数\n",
    "\n",
    "* 假设连接的两表无公共列\n",
    "* 调用方式为 `join(df1, df2, how=\"left\")`\n",
    "* 给出测试样例"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 目前的能力就是掉包，大致能看懂代码。就不粘贴了"
   ]
  }
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