Python 基础/PandasBasic.ipynb

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   "source": [
    "<h1>Table of Contents<span class=\"tocSkip\"></span></h1>\n",
    "<div class=\"toc\"><ul class=\"toc-item\"><li><span><a href=\"#Pandas-简介\" data-toc-modified-id=\"Pandas-简介-1\"><span class=\"toc-item-num\">1&nbsp;&nbsp;</span>Pandas 简介</a></span></li><li><span><a href=\"#帮助\" data-toc-modified-id=\"帮助-2\"><span class=\"toc-item-num\">2&nbsp;&nbsp;</span>帮助</a></span></li><li><span><a href=\"#Pandas-数据结构\" data-toc-modified-id=\"Pandas-数据结构-3\"><span class=\"toc-item-num\">3&nbsp;&nbsp;</span>Pandas 数据结构</a></span><ul class=\"toc-item\"><li><span><a href=\"#序列（Series）\" data-toc-modified-id=\"序列（Series）-3.1\"><span class=\"toc-item-num\">3.1&nbsp;&nbsp;</span>序列（Series）</a></span></li><li><span><a href=\"#数据框（DataFrame）\" data-toc-modified-id=\"数据框（DataFrame）-3.2\"><span class=\"toc-item-num\">3.2&nbsp;&nbsp;</span>数据框（DataFrame）</a></span></li></ul></li><li><span><a href=\"#文件-I-/-O\" data-toc-modified-id=\"文件-I-/-O-4\"><span class=\"toc-item-num\">4&nbsp;&nbsp;</span>文件 I / O</a></span><ul class=\"toc-item\"><li><span><a href=\"#读写-CSV\" data-toc-modified-id=\"读写-CSV-4.1\"><span class=\"toc-item-num\">4.1&nbsp;&nbsp;</span>读写 CSV</a></span></li><li><span><a href=\"#读写-Excel\" data-toc-modified-id=\"读写-Excel-4.2\"><span class=\"toc-item-num\">4.2&nbsp;&nbsp;</span>读写 Excel</a></span></li><li><span><a href=\"#读取数据库\" data-toc-modified-id=\"读取数据库-4.3\"><span class=\"toc-item-num\">4.3&nbsp;&nbsp;</span>读取数据库</a></span></li></ul></li><li><span><a href=\"#选择\" data-toc-modified-id=\"选择-5\"><span class=\"toc-item-num\">5&nbsp;&nbsp;</span>选择</a></span><ul class=\"toc-item\"><li><span><a href=\"#获取\" data-toc-modified-id=\"获取-5.1\"><span class=\"toc-item-num\">5.1&nbsp;&nbsp;</span>获取</a></span></li><li><span><a href=\"#选择，布尔索引-&amp;-设置\" data-toc-modified-id=\"选择，布尔索引-&amp;-设置-5.2\"><span class=\"toc-item-num\">5.2&nbsp;&nbsp;</span>选择，布尔索引 &amp; 设置</a></span><ul class=\"toc-item\"><li><span><a href=\"#位置\" data-toc-modified-id=\"位置-5.2.1\"><span class=\"toc-item-num\">5.2.1&nbsp;&nbsp;</span>位置</a></span></li><li><span><a href=\"#标签\" data-toc-modified-id=\"标签-5.2.2\"><span class=\"toc-item-num\">5.2.2&nbsp;&nbsp;</span>标签</a></span></li><li><span><a href=\"#布尔索引\" data-toc-modified-id=\"布尔索引-5.2.3\"><span class=\"toc-item-num\">5.2.3&nbsp;&nbsp;</span>布尔索引</a></span></li><li><span><a href=\"#设置\" data-toc-modified-id=\"设置-5.2.4\"><span class=\"toc-item-num\">5.2.4&nbsp;&nbsp;</span>设置</a></span></li></ul></li></ul></li><li><span><a href=\"#删除（dropping）\" data-toc-modified-id=\"删除（dropping）-6\"><span class=\"toc-item-num\">6&nbsp;&nbsp;</span>删除（dropping）</a></span></li><li><span><a href=\"#排序和排名\" data-toc-modified-id=\"排序和排名-7\"><span class=\"toc-item-num\">7&nbsp;&nbsp;</span>排序和排名</a></span></li><li><span><a href=\"#检索-Series-/-DataFrame-信息\" data-toc-modified-id=\"检索-Series-/-DataFrame-信息-8\"><span class=\"toc-item-num\">8&nbsp;&nbsp;</span>检索 Series / DataFrame 信息</a></span><ul class=\"toc-item\"><li><span><a href=\"#基本信息\" data-toc-modified-id=\"基本信息-8.1\"><span class=\"toc-item-num\">8.1&nbsp;&nbsp;</span>基本信息</a></span></li><li><span><a href=\"#摘要\" data-toc-modified-id=\"摘要-8.2\"><span class=\"toc-item-num\">8.2&nbsp;&nbsp;</span>摘要</a></span></li></ul></li><li><span><a href=\"#应用函数\" data-toc-modified-id=\"应用函数-9\"><span class=\"toc-item-num\">9&nbsp;&nbsp;</span>应用函数</a></span></li><li><span><a href=\"#数据对齐\" data-toc-modified-id=\"数据对齐-10\"><span class=\"toc-item-num\">10&nbsp;&nbsp;</span>数据对齐</a></span><ul class=\"toc-item\"><li><span><a href=\"#内部数据对齐\" data-toc-modified-id=\"内部数据对齐-10.1\"><span class=\"toc-item-num\">10.1&nbsp;&nbsp;</span>内部数据对齐</a></span></li><li><span><a href=\"#填充方法的算术运算\" data-toc-modified-id=\"填充方法的算术运算-10.2\"><span class=\"toc-item-num\">10.2&nbsp;&nbsp;</span>填充方法的算术运算</a></span></li></ul></li></ul></div>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Pandas 简介"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![Pandas](https://pandas.pydata.org/_static/pandas_logo.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Pandas 库基于 NumPy 构建，为 Python 编程语言提供易于使用的数据结构和数据分析工具。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "使用以下约定导入 Pandas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 帮助"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "help(pd.Series.loc)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Pandas 数据结构"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 序列（Series）"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "能够保存任何数据类型的一维标记数组"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "s = pd.Series([1, 3, 5, 7], index=['天', '地', '玄', '黄'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "天    1\n",
       "地    3\n",
       "玄    5\n",
       "黄    7\n",
       "dtype: int64"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 左侧一列为索引\n",
    "s"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 数据框（DataFrame）"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "不同类型列的二维标记数据结构，类似 Excel 表格"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "上面一行为列名"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "左侧一列为索引"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "| - | 姓 |  名  | 民族 | 姓别 | 年龄 |\n",
    "|:-:|:-:|:---:|:---:|:---:|:----:|\n",
    "| 1 | 贾 | 小武 |  汉  |  男  |  3   |\n",
    "| 2 | 贾 | 小久 |  汉  |  男  |  1   |\n",
    "| 3 | 张 | 小鸭 |  汉  |  女  |  -   |"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = {'姓': ['贾', '贾', '张'],\n",
    "        '名': ['小武', '小久', '小鸭́'],\n",
    "        '民族': ['汉', '汉', '汉'],\n",
    "        '年龄': [3, 1, None]}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'姓': ['贾', '贾', '张'],\n",
       " '名': ['小武', '小久', '小鸭́'],\n",
       " '民族': ['汉', '汉', '汉'],\n",
       " '年龄': [3, 1, None]}"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.DataFrame(data, columns=['姓', '名', '年龄'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\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>姓</th>\n",
       "      <th>名</th>\n",
       "      <th>年龄</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>贾</td>\n",
       "      <td>小武</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>贾</td>\n",
       "      <td>小久</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>张</td>\n",
       "      <td>小鸭́</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   姓    名   年龄\n",
       "0  贾   小武  3.0\n",
       "1  贾   小久  1.0\n",
       "2  张  小鸭́  NaN"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 文件 I / O "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 读写 CSV"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "pd.read_csv('file.csv', header=None, nrows=5)\n",
    "df.to_csv('myDataFrame.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 读写 Excel"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "pd.read_excel('file.xlsx')\n",
    "pd.to_excel('dir/myDataFrame.xlsx', sheet_name='Sheet1')\n",
    "xlsx = pd.ExcelFile('file.xls')\n",
    "df = pd.read_excel(xlsx, 'Sheet1')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 读取数据库"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sqlalchemy import create_engine\n",
    "engine = create_engine('sqlite:///:memory:')\n",
    "pd.read_sql(\"SELECT * FROM my_table;\", engine)\n",
    "pd.read_sql_table('my_table', engine)\n",
    "pd.read_sql_query(\"SELECT * FROM my_table;\", engine)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "read_sql() 是 read_sql_table() 和 read_sql_query() 的便捷包装器"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "pd.to_sql('myDf', engine)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 选择"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 获取"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 获取 1 个数据\n",
    "s['天']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "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",
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       "    }\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>姓</th>\n",
       "      <th>名</th>\n",
       "      <th>年龄</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>贾</td>\n",
       "      <td>小久</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>张</td>\n",
       "      <td>小鸭́</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   姓    名   年龄\n",
       "1  贾   小久  1.0\n",
       "2  张  小鸭́  NaN"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 获取 DataFrame 的子集\n",
    "df[1:]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 选择，布尔索引 & 设置"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 位置\n",
    "\n",
    "按行和列选择单个值"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>名</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>小武</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    名\n",
       "0  小武"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.iloc[[0], [1]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'小武'"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.iat[0, 1]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 标签\n",
    "\n",
    "按行和列标签选择单个值"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'贾'"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.loc[0, '姓']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'贾'"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.at[0, '姓']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 布尔索引"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "天    1\n",
       "dtype: int64"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s[~(s > 1)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "地    3\n",
       "玄    5\n",
       "黄    7\n",
       "dtype: int64"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s[(s < -1) | (s > 2)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>姓</th>\n",
       "      <th>名</th>\n",
       "      <th>年龄</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>贾</td>\n",
       "      <td>小武</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   姓   名   年龄\n",
       "0  贾  小武  3.0"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df['年龄'] > 1]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 设置\n",
    "\n",
    "将序列 s 的索引 '宇' 设置为 9"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "天    1\n",
       "地    3\n",
       "玄    5\n",
       "黄    7\n",
       "宇    9\n",
       "dtype: int64"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s['宇'] = 9\n",
    "s"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 删除（dropping）"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "从行中删除值（axis = 0）"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "玄    5\n",
       "黄    7\n",
       "宇    9\n",
       "dtype: int64"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s.drop(['天', '地'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "从列中删除值（axis = 1）"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "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>名</th>\n",
       "      <th>年龄</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>小武</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>小久</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>小鸭́</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     名   年龄\n",
       "0   小武  3.0\n",
       "1   小久  1.0\n",
       "2  小鸭́  NaN"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.drop('姓', axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 排序和排名"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "按轴标签排序"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "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>姓</th>\n",
       "      <th>名</th>\n",
       "      <th>年龄</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>贾</td>\n",
       "      <td>小武</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>贾</td>\n",
       "      <td>小久</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>张</td>\n",
       "      <td>小鸭́</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   姓    名   年龄\n",
       "0  贾   小武  3.0\n",
       "1  贾   小久  1.0\n",
       "2  张  小鸭́  NaN"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.sort_index()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "按轴的值排序"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "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>姓</th>\n",
       "      <th>名</th>\n",
       "      <th>年龄</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>贾</td>\n",
       "      <td>小久</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>贾</td>\n",
       "      <td>小武</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>张</td>\n",
       "      <td>小鸭́</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   姓    名   年龄\n",
       "1  贾   小久  1.0\n",
       "0  贾   小武  3.0\n",
       "2  张  小鸭́  NaN"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.sort_values(by='年龄')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "从小到大排序的下标"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "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>姓</th>\n",
       "      <th>名</th>\n",
       "      <th>年龄</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2.5</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2.5</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     姓    名   年龄\n",
       "0  2.5  2.0  2.0\n",
       "1  2.5  1.0  1.0\n",
       "2  1.0  3.0  NaN"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.rank()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 检索 Series / DataFrame 信息"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 基本信息"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.DataFrame([[1, 2], [4, 5], [7, 8]],\n",
    "                  index=['cobra', 'viper', 'sidewinder'],\n",
    "                  columns=['max_speed', 'shield'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "（行，列）"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(3, 2)"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "描述索引"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['cobra', 'viper', 'sidewinder'], dtype='object')"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.index"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "描述 DataFrame 列信息"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['max_speed', 'shield'], dtype='object')"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "DataFrame 信息"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Index: 3 entries, cobra to sidewinder\n",
      "Data columns (total 2 columns):\n",
      "max_speed    3 non-null int64\n",
      "shield       3 non-null int64\n",
      "dtypes: int64(2)\n",
      "memory usage: 72.0+ bytes\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "非 NA 值的数量"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "max_speed    3\n",
       "shield       3\n",
       "dtype: int64"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.count()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 摘要"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "总和"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "max_speed    12\n",
       "shield       15\n",
       "dtype: int64"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "累积值"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "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>max_speed</th>\n",
       "      <th>shield</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>cobra</th>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>viper</th>\n",
       "      <td>5</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sidewinder</th>\n",
       "      <td>12</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            max_speed  shield\n",
       "cobra               1       2\n",
       "viper               5       7\n",
       "sidewinder         12      15"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.cumsum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "最小值"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "max_speed    1\n",
       "shield       2\n",
       "dtype: int64"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.min()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "最大值"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "max_speed    7\n",
       "shield       8\n",
       "dtype: int64"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.max()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "最小索引值"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "max_speed    cobra\n",
       "shield       cobra\n",
       "dtype: object"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.idxmin()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "最大索引值"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "max_speed    sidewinder\n",
       "shield       sidewinder\n",
       "dtype: object"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.idxmax()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "摘要统计"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "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>max_speed</th>\n",
       "      <th>shield</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>4.0</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>2.5</td>\n",
       "      <td>3.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>4.0</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>5.5</td>\n",
       "      <td>6.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>7.0</td>\n",
       "      <td>8.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       max_speed  shield\n",
       "count        3.0     3.0\n",
       "mean         4.0     5.0\n",
       "std          3.0     3.0\n",
       "min          1.0     2.0\n",
       "25%          2.5     3.5\n",
       "50%          4.0     5.0\n",
       "75%          5.5     6.5\n",
       "max          7.0     8.0"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "均值"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "max_speed    4.0\n",
       "shield       5.0\n",
       "dtype: float64"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "中位数"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "max_speed    4.0\n",
       "shield       5.0\n",
       "dtype: float64"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.median()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 应用函数"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "f = lambda x: x*2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "应用函数"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe thead th {\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>max_speed</th>\n",
       "      <th>shield</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>cobra</th>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>viper</th>\n",
       "      <td>8</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sidewinder</th>\n",
       "      <td>14</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            max_speed  shield\n",
       "cobra               2       4\n",
       "viper               8      10\n",
       "sidewinder         14      16"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "df.apply(f)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "按元素应用函数"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>max_speed</th>\n",
       "      <th>shield</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>cobra</th>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>viper</th>\n",
       "      <td>8</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sidewinder</th>\n",
       "      <td>14</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            max_speed  shield\n",
       "cobra               2       4\n",
       "viper               8      10\n",
       "sidewinder         14      16"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.applymap(f)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 数据对齐"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 内部数据对齐"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "值 NA 在不重叠的索引中引入"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "s3 = pd.Series([7, -2, 3], index=['玄', '黄', '宇'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "地     NaN\n",
       "天     NaN\n",
       "宇    12.0\n",
       "玄    12.0\n",
       "黄     5.0\n",
       "dtype: float64"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s + s3"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 填充方法的算术运算"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "借助填充方法自行完成内部数据对齐"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "地     3.0\n",
       "天     1.0\n",
       "宇    12.0\n",
       "玄    12.0\n",
       "黄     5.0\n",
       "dtype: float64"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s.add(s3, fill_value=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "地    1.0\n",
       "天   -1.0\n",
       "宇    6.0\n",
       "玄   -2.0\n",
       "黄    9.0\n",
       "dtype: float64"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s.sub(s3, fill_value=2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "地    0.750000\n",
       "天    0.250000\n",
       "宇    3.000000\n",
       "玄    0.714286\n",
       "黄   -3.500000\n",
       "dtype: float64"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s.div(s3, fill_value=4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "地     9.0\n",
       "天     3.0\n",
       "宇    27.0\n",
       "玄    35.0\n",
       "黄   -14.0\n",
       "dtype: float64"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s.mul(s3, fill_value=3)"
   ]
  }
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