ch09-Pandas模块/09-03二维数据结构DataFrame对象.ipynb

{
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  {
   "cell_type": "markdown",
   "id": "ed1f74e7",
   "metadata": {},
   "source": [
    "# 二维数据结构DataFrame对象"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "717b29b8",
   "metadata": {},
   "source": [
    "DataFrame对象是一种二维带标记数据结构，不同列的数据类型可以不同。为了方便理解，可以将DataFrame对象看成一张Excel电子表格，或者是一个由多列Series对象构成的字典。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "b0182569",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "79178b2b",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "698481f5",
   "metadata": {},
   "source": [
    "## DataFrame对象的生成\n",
    "\n",
    "与Series类似，DataFrame对象也可以由多种类型的数据生成：\n",
    "- 由Series对象为值构成的字典。\n",
    "- 由一维数组或列表构成的字典。\n",
    "- 由字典构成的列表或数组。"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "2e7cb22e",
   "metadata": {},
   "source": [
    "### 使用Series对象构成的字典生成"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "821beb00",
   "metadata": {},
   "source": [
    "DataFrame对象可以从一组由Series对象为值构成的字典中生成。字典中的值除了Series对象，也可以是另一个字典，因为字典被转换为Series对象。\n",
    "\n",
    "假设有一个包含两个Series对象的字典d："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "98d20646",
   "metadata": {},
   "outputs": [],
   "source": [
    "s1 = pd.Series([1, 2, 3], index=['a', 'b', 'c'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "315ddecb",
   "metadata": {},
   "outputs": [],
   "source": [
    "s2 = pd.Series([1., 2., 3., 4.], index=['a', 'b', 'c', 'd'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "1e13fa34",
   "metadata": {},
   "outputs": [],
   "source": [
    "d = {\"one\": s1, \"two\": s2}"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "49f79ba0",
   "metadata": {},
   "source": [
    "可以用字典d构造一个DataFrame对象："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "79c735da",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.DataFrame(d)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "d65d98a8",
   "metadata": {},
   "outputs": [
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       "      <td>2.0</td>\n",
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       "      <th>c</th>\n",
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       "      <td>3.0</td>\n",
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       "      <th>d</th>\n",
       "      <td>NaN</td>\n",
       "      <td>4.0</td>\n",
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      "text/plain": [
       "   one  two\n",
       "a  1.0  1.0\n",
       "b  2.0  2.0\n",
       "c  3.0  3.0\n",
       "d  NaN  4.0"
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     "execution_count": 7,
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   "source": [
    "df"
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  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "e1f02d1b",
   "metadata": {},
   "source": [
    "与Series相比，DataFrame对象要区分不同的行和列，因此有行标记和列标记之分。默认情况下，df的列标记是传入字典的键，可以用属性`.columns`查看："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "fa4a04fb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['one', 'two'], dtype='object')"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "ff9acb82",
   "metadata": {},
   "source": [
    "行标记是两个Series对象标记的并集，Pandas会自动将两个Series对象的标记进行对齐："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "34b63bf8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['a', 'b', 'c', 'd'], dtype='object')"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.index"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "741871ba",
   "metadata": {},
   "source": [
    "在生成DataFrame时，也可以指定index和columns参数："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "227afaa7",
   "metadata": {},
   "outputs": [
    {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>one</th>\n",
       "      <th>two</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>d</th>\n",
       "      <td>NaN</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>b</th>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>a</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      "text/plain": [
       "   one  two\n",
       "d  NaN  4.0\n",
       "b  2.0  2.0\n",
       "a  1.0  1.0"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.DataFrame(d, index=[\"d\", \"b\", \"a\"])"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "a50f5bc2",
   "metadata": {},
   "source": [
    "Pandas会按照给定的顺序从传入的数据中寻找对应的值，如果该值不存在，则使用缺省值`np.nan`："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "492f3b05",
   "metadata": {},
   "outputs": [
    {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
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       "      <th>two</th>\n",
       "      <th>three</th>\n",
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       "  </thead>\n",
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       "    <tr>\n",
       "      <th>d</th>\n",
       "      <td>4.0</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>b</th>\n",
       "      <td>2.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>a</th>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
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      "text/plain": [
       "   two three\n",
       "d  4.0   NaN\n",
       "b  2.0   NaN\n",
       "a  1.0   NaN"
      ]
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     "execution_count": 11,
     "metadata": {},
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   "source": [
    "pd.DataFrame(d, index=['d', 'b', 'a'], columns=['two', 'three'])"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "2cef3d5d",
   "metadata": {},
   "source": [
    "### 使用一维数组构成的字典生成"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "eb0bb12a",
   "metadata": {},
   "source": [
    "DataFrame对象还可以使用由一维数组或列表构成的字典生成，这些数组和列表必须是等长的："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "a097d435",
   "metadata": {},
   "outputs": [],
   "source": [
    "d = {'one' : [1., 2., 3., 4.],\n",
    "     'two' : [4., 3., 2., 1.]}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "8d58e2f8",
   "metadata": {},
   "outputs": [
    {
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       "    <tr style=\"text-align: right;\">\n",
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       "      <th>one</th>\n",
       "      <th>two</th>\n",
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       "      <th>0</th>\n",
       "      <td>1.0</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4.0</td>\n",
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      "text/plain": [
       "   one  two\n",
       "0  1.0  4.0\n",
       "1  2.0  3.0\n",
       "2  3.0  2.0\n",
       "3  4.0  1.0"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.DataFrame(d)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "0333d58f",
   "metadata": {},
   "source": [
    "传入index参数时，该参数的长度也必须与列表长度一致："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "21661d1e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>one</th>\n",
       "      <th>two</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>a</th>\n",
       "      <td>1.0</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>b</th>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>c</th>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>d</th>\n",
       "      <td>4.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      ],
      "text/plain": [
       "   one  two\n",
       "a  1.0  4.0\n",
       "b  2.0  3.0\n",
       "c  3.0  2.0\n",
       "d  4.0  1.0"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.DataFrame(d, index=['a', 'b', 'c', 'd'])"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "9b2c149f",
   "metadata": {},
   "source": [
    "### 使用字典数组生成"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3f5efc2a",
   "metadata": {},
   "source": [
    "还可以使用字典构成的数组或列表进行构建："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "57d4e5b2",
   "metadata": {},
   "outputs": [],
   "source": [
    "data = [{'a': 1, 'b': 2}, {'a': 5, 'b': 10, 'c': 20}]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "fd002d26",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>5</td>\n",
       "      <td>10</td>\n",
       "      <td>20.0</td>\n",
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      "text/plain": [
       "   a   b     c\n",
       "0  1   2   NaN\n",
       "1  5  10  20.0"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.DataFrame(data)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "e6448551",
   "metadata": {},
   "source": [
    "与Series不同的是，字典的键对应的是列标记，行标记由数组或列表的大小决定。"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "28b0a139",
   "metadata": {},
   "source": [
    "### 使用二维数组生成"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e73d4783",
   "metadata": {},
   "source": [
    "还可以使用NumPy的二维数组生成："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "5e70aa1e",
   "metadata": {},
   "outputs": [],
   "source": [
    "a = np.array([[1,2,3], [4,5,6]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "a6fa5ce0",
   "metadata": {},
   "outputs": [
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      "text/plain": [
       "   0  1  2\n",
       "0  1  2  3\n",
       "1  4  5  6"
      ]
     },
     "execution_count": 18,
     "metadata": {},
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   ],
   "source": [
    "pd.DataFrame(a)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "6aed9e8c",
   "metadata": {},
   "source": [
    "## DataFrame对象的使用\n",
    "\n",
    "DataFrame对象不是二维NumPy数组，在使用方法上存在很大差异："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "9f32f6c3",
   "metadata": {},
   "outputs": [],
   "source": [
    "s1 = pd.Series([1, 2, 3], index=['a', 'b', 'c'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "239d3b03",
   "metadata": {},
   "outputs": [],
   "source": [
    "s2 = pd.Series([1., 2., 3., 4.], index=['a', 'b', 'c', 'd'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "e8776dc6",
   "metadata": {},
   "outputs": [],
   "source": [
    "d = {\"one\": s1, \"two\": s2}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "cdde7d53",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.DataFrame(d)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "5e17d0aa",
   "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>one</th>\n",
       "      <th>two</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>a</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>b</th>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>c</th>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>d</th>\n",
       "      <td>NaN</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   one  two\n",
       "a  1.0  1.0\n",
       "b  2.0  2.0\n",
       "c  3.0  3.0\n",
       "d  NaN  4.0"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "23e62817",
   "metadata": {},
   "source": [
    "### 列相关的操作"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "3950a0b2",
   "metadata": {},
   "source": [
    "DataFrame对象可以看成是一个由Series对象构成的字典，.columns属性对应字典的键，每一列对应字典的值："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "a3c973ea",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "a    1.0\n",
       "b    2.0\n",
       "c    3.0\n",
       "d    NaN\n",
       "Name: one, dtype: float64"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['one']"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "55ef0692",
   "metadata": {},
   "source": [
    "可以像字典一样增加新列："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "0f2ef75c",
   "metadata": {},
   "outputs": [],
   "source": [
    "df[\"three\"] = df[\"one\"] * df[\"two\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "05ac44bb",
   "metadata": {},
   "outputs": [],
   "source": [
    "df[\"flag\"] = df[\"one\"] > 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "7ecb4c9f",
   "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>one</th>\n",
       "      <th>two</th>\n",
       "      <th>three</th>\n",
       "      <th>flag</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>a</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>b</th>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>c</th>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>d</th>\n",
       "      <td>NaN</td>\n",
       "      <td>4.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   one  two  three   flag\n",
       "a  1.0  1.0    1.0  False\n",
       "b  2.0  2.0    4.0  False\n",
       "c  3.0  3.0    9.0   True\n",
       "d  NaN  4.0    NaN  False"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "1f62216a",
   "metadata": {},
   "source": [
    "增加新列时，如果新列的值是单一值，Pandas会按照行标记自动进行扩展："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "e156535d",
   "metadata": {},
   "outputs": [],
   "source": [
    "df[\"four\"] = 4"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "e220dded",
   "metadata": {},
   "source": [
    "DataFrame对象支持用del关键字或者.pop()方法删除列："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "8e744653",
   "metadata": {},
   "outputs": [],
   "source": [
    "del df[\"two\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "4770ad42",
   "metadata": {},
   "outputs": [],
   "source": [
    "three = df.pop(\"three\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "d20663a2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "a    1.0\n",
       "b    4.0\n",
       "c    9.0\n",
       "d    NaN\n",
       "Name: three, dtype: float64"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "three"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "8713ea5e",
   "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>one</th>\n",
       "      <th>flag</th>\n",
       "      <th>four</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>a</th>\n",
       "      <td>1.0</td>\n",
       "      <td>False</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>b</th>\n",
       "      <td>2.0</td>\n",
       "      <td>False</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>c</th>\n",
       "      <td>3.0</td>\n",
       "      <td>True</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>d</th>\n",
       "      <td>NaN</td>\n",
       "      <td>False</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   one   flag  four\n",
       "a  1.0  False     4\n",
       "b  2.0  False     4\n",
       "c  3.0   True     4\n",
       "d  NaN  False     4"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "18ec10aa",
   "metadata": {},
   "source": [
    "增加一个行标记不完全相同的新列时，Pandas只会保留该列中与原有行标记相同的部分，以保证原DataFrame对象的行标记不变化："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "07b61507",
   "metadata": {},
   "outputs": [],
   "source": [
    "df[\"foo\"] = pd.Series([1,2,3], index=[\"a\", \"d\", \"e\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "e45141a6",
   "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>one</th>\n",
       "      <th>flag</th>\n",
       "      <th>four</th>\n",
       "      <th>foo</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>a</th>\n",
       "      <td>1.0</td>\n",
       "      <td>False</td>\n",
       "      <td>4</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>b</th>\n",
       "      <td>2.0</td>\n",
       "      <td>False</td>\n",
       "      <td>4</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>c</th>\n",
       "      <td>3.0</td>\n",
       "      <td>True</td>\n",
       "      <td>4</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>d</th>\n",
       "      <td>NaN</td>\n",
       "      <td>False</td>\n",
       "      <td>4</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   one   flag  four  foo\n",
       "a  1.0  False     4  1.0\n",
       "b  2.0  False     4  NaN\n",
       "c  3.0   True     4  NaN\n",
       "d  NaN  False     4  2.0"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "f53b8dd1",
   "metadata": {},
   "source": [
    "默认情况下，新列的插入位置都在DataFrame对象的最后。可以使用.insert()方法将其插入指定的位置："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "edad10b3",
   "metadata": {},
   "outputs": [],
   "source": [
    "df.insert(1, \"bar\", df[\"one\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "5b1967c2",
   "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>one</th>\n",
       "      <th>bar</th>\n",
       "      <th>flag</th>\n",
       "      <th>four</th>\n",
       "      <th>foo</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>a</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>False</td>\n",
       "      <td>4</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>b</th>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>False</td>\n",
       "      <td>4</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>c</th>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>True</td>\n",
       "      <td>4</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>d</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>False</td>\n",
       "      <td>4</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   one  bar   flag  four  foo\n",
       "a  1.0  1.0  False     4  1.0\n",
       "b  2.0  2.0  False     4  NaN\n",
       "c  3.0  3.0   True     4  NaN\n",
       "d  NaN  NaN  False     4  2.0"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "2441cfd5",
   "metadata": {},
   "source": [
    "### 行相关的操作\n",
    "\n",
    "DataFrame对象有两种常用的索引行的方式。可以用`.loc`属性索引行标记，返回一个Series对象："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "737b173b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "one       2.0\n",
       "bar       2.0\n",
       "flag    False\n",
       "four        4\n",
       "foo       NaN\n",
       "Name: b, dtype: object"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.loc[\"b\"]"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "cb199e17",
   "metadata": {},
   "source": [
    "也可以用.iloc属性索引位置，得到第二行数据："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "80f5c2ae",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "one       2.0\n",
       "bar       2.0\n",
       "flag    False\n",
       "four        4\n",
       "foo       NaN\n",
       "Name: b, dtype: object"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.iloc[1]"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "a41ed5e1",
   "metadata": {},
   "source": [
    "### 加法与减法操作\n",
    "\n",
    "DataFrame对象支持加法和减法的操作，并且按照行列标记对齐的原则进行计算："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "9b6e727a",
   "metadata": {},
   "outputs": [],
   "source": [
    "df1 = pd.DataFrame(np.random.randn(10, 4), columns=['A', 'B', 'C', 'D'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "f2b0db7d",
   "metadata": {},
   "outputs": [],
   "source": [
    "df2 = pd.DataFrame(np.random.randn(7, 3), columns=['A', 'B', 'C'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "ed6e0274",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<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>A</th>\n",
       "      <th>B</th>\n",
       "      <th>C</th>\n",
       "      <th>D</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-1.906552</td>\n",
       "      <td>-2.428495</td>\n",
       "      <td>1.131278</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-0.955872</td>\n",
       "      <td>-1.476556</td>\n",
       "      <td>-1.523796</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.766210</td>\n",
       "      <td>-0.162112</td>\n",
       "      <td>0.190370</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-2.866838</td>\n",
       "      <td>0.866281</td>\n",
       "      <td>1.340097</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-2.027247</td>\n",
       "      <td>0.972097</td>\n",
       "      <td>-0.807422</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.841079</td>\n",
       "      <td>0.101313</td>\n",
       "      <td>-1.701630</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0.318099</td>\n",
       "      <td>-0.037061</td>\n",
       "      <td>-1.878293</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          A         B         C   D\n",
       "0 -1.906552 -2.428495  1.131278 NaN\n",
       "1 -0.955872 -1.476556 -1.523796 NaN\n",
       "2  0.766210 -0.162112  0.190370 NaN\n",
       "3 -2.866838  0.866281  1.340097 NaN\n",
       "4 -2.027247  0.972097 -0.807422 NaN\n",
       "5  0.841079  0.101313 -1.701630 NaN\n",
       "6  0.318099 -0.037061 -1.878293 NaN\n",
       "7       NaN       NaN       NaN NaN\n",
       "8       NaN       NaN       NaN NaN\n",
       "9       NaN       NaN       NaN NaN"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1 + df2"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "f0a10763",
   "metadata": {},
   "source": [
    "DataFrame对象还可以与Series对象进行加减操作。与NumPy中的广播机制类似，Pandas会先将Series对象的标记与DataFrame对象的列标记中对应的部分拿出来，然后使用广播机制将Series对象沿着行标记进行扩展："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "898bd8b9",
   "metadata": {},
   "outputs": [
    {
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       "      <th>A</th>\n",
       "      <th>B</th>\n",
       "      <th>C</th>\n",
       "      <th>D</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.034677</td>\n",
       "      <td>-1.447889</td>\n",
       "      <td>0.239673</td>\n",
       "      <td>0.897156</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-0.216450</td>\n",
       "      <td>-0.052522</td>\n",
       "      <td>0.237849</td>\n",
       "      <td>0.806303</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.260522</td>\n",
       "      <td>0.590821</td>\n",
       "      <td>0.231546</td>\n",
       "      <td>-2.164184</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-1.264539</td>\n",
       "      <td>0.947130</td>\n",
       "      <td>0.601591</td>\n",
       "      <td>-0.753204</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-1.113126</td>\n",
       "      <td>0.063686</td>\n",
       "      <td>-0.379063</td>\n",
       "      <td>-0.275933</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.596109</td>\n",
       "      <td>-0.516650</td>\n",
       "      <td>-1.177866</td>\n",
       "      <td>0.075800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>1.386725</td>\n",
       "      <td>-0.328219</td>\n",
       "      <td>-1.303265</td>\n",
       "      <td>-0.790358</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>1.225454</td>\n",
       "      <td>0.923503</td>\n",
       "      <td>0.715214</td>\n",
       "      <td>-0.144048</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>-0.982050</td>\n",
       "      <td>-0.026315</td>\n",
       "      <td>1.963732</td>\n",
       "      <td>0.638793</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>0.715773</td>\n",
       "      <td>-0.767911</td>\n",
       "      <td>-0.379927</td>\n",
       "      <td>-1.533615</td>\n",
       "    </tr>\n",
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       "</table>\n",
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      ],
      "text/plain": [
       "          A         B         C         D\n",
       "0  0.034677 -1.447889  0.239673  0.897156\n",
       "1 -0.216450 -0.052522  0.237849  0.806303\n",
       "2  0.260522  0.590821  0.231546 -2.164184\n",
       "3 -1.264539  0.947130  0.601591 -0.753204\n",
       "4 -1.113126  0.063686 -0.379063 -0.275933\n",
       "5  0.596109 -0.516650 -1.177866  0.075800\n",
       "6  1.386725 -0.328219 -1.303265 -0.790358\n",
       "7  1.225454  0.923503  0.715214 -0.144048\n",
       "8 -0.982050 -0.026315  1.963732  0.638793\n",
       "9  0.715773 -0.767911 -0.379927 -1.533615"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "0726b1fc",
   "metadata": {},
   "outputs": [
    {
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       "      <th>A</th>\n",
       "      <th>B</th>\n",
       "      <th>C</th>\n",
       "      <th>D</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-0.251127</td>\n",
       "      <td>1.395367</td>\n",
       "      <td>-0.001824</td>\n",
       "      <td>-0.090853</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.225845</td>\n",
       "      <td>2.038710</td>\n",
       "      <td>-0.008127</td>\n",
       "      <td>-3.061340</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-1.299216</td>\n",
       "      <td>2.395019</td>\n",
       "      <td>0.361919</td>\n",
       "      <td>-1.650360</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-1.147802</td>\n",
       "      <td>1.511575</td>\n",
       "      <td>-0.618736</td>\n",
       "      <td>-1.173089</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.561432</td>\n",
       "      <td>0.931239</td>\n",
       "      <td>-1.417538</td>\n",
       "      <td>-0.821356</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>1.352048</td>\n",
       "      <td>1.119670</td>\n",
       "      <td>-1.542938</td>\n",
       "      <td>-1.687514</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>1.190778</td>\n",
       "      <td>2.371392</td>\n",
       "      <td>0.475542</td>\n",
       "      <td>-1.041204</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>-1.016727</td>\n",
       "      <td>1.421574</td>\n",
       "      <td>1.724059</td>\n",
       "      <td>-0.258363</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>0.681096</td>\n",
       "      <td>0.679978</td>\n",
       "      <td>-0.619600</td>\n",
       "      <td>-2.430771</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      ],
      "text/plain": [
       "          A         B         C         D\n",
       "0  0.000000  0.000000  0.000000  0.000000\n",
       "1 -0.251127  1.395367 -0.001824 -0.090853\n",
       "2  0.225845  2.038710 -0.008127 -3.061340\n",
       "3 -1.299216  2.395019  0.361919 -1.650360\n",
       "4 -1.147802  1.511575 -0.618736 -1.173089\n",
       "5  0.561432  0.931239 -1.417538 -0.821356\n",
       "6  1.352048  1.119670 -1.542938 -1.687514\n",
       "7  1.190778  2.371392  0.475542 -1.041204\n",
       "8 -1.016727  1.421574  1.724059 -0.258363\n",
       "9  0.681096  0.679978 -0.619600 -2.430771"
      ]
     },
     "execution_count": 43,
     "metadata": {},
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    }
   ],
   "source": [
    "df1 - df1.iloc[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0fe7dcf0",
   "metadata": {},
   "outputs": [],
   "source": []
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