11. Pandas的axis参数怎么理解？.ipynb

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   "source": [
    "## Pandas的axis参数怎么理解？\n",
    "\n",
    "* axis=0或者\"index\"：  \n",
    "  - 如果是单行操作，就指的是某一行\n",
    "  - 如果是聚合操作，指的是跨行cross rows\n",
    "* axis=1或者\"columns\"：\n",
    "  - 如果是单列操作，就指的是某一列\n",
    "  - 如果是聚合操作，指的是跨列cross columns\n",
    "  \n",
    "***按哪个axis，就是这个axis要动起来(类似被for遍历)，其它的axis保持不动***"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.DataFrame(\n",
    "    np.arange(12).reshape(3,4),\n",
    "    columns=['A', 'B', 'C', 'D']\n",
    ")"
   ]
  },
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       "   A  B   C   D\n",
       "0  0  1   2   3\n",
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       "2  8  9  10  11"
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   "source": [
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1、单列drop，就是删除某一列"
   ]
  },
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       "   B   C   D\n",
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   "source": [
    "# 代表的就是删除某列\n",
    "df.drop(\"A\", axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2、单行drop，就是删除某一行"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
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       "   A  B   C   D\n",
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       "   A  B   C   D\n",
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    "# 代表的就是删除某行\n",
    "df.drop(1, axis=0)"
   ]
  },
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   "source": [
    "### 3、按axis=0/index执行mean聚合操作\n",
    "\n",
    "反直觉：输出的不是每行的结果，而是每列的结果"
   ]
  },
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      "text/plain": [
       "   A  B   C   D\n",
       "0  0  1   2   3\n",
       "1  4  5   6   7\n",
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   "source": [
    "df"
   ]
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  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "A    4.0\n",
       "B    5.0\n",
       "C    6.0\n",
       "D    7.0\n",
       "dtype: float64"
      ]
     },
     "execution_count": 8,
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   "source": [
    "# axis=0 or axis=index\n",
    "df.mean(axis=0)"
   ]
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   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<img src=\"./other_files/pandas-axis-index.png\" width=\"300\" />"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "***指定了按哪个axis，就是这个axis要动起来(类似被for遍历)，其它的axis保持不动***"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 4、按axis=1/columns执行mean聚合操作\n",
    "\n",
    "反直觉：输出的不是每行的结果，而是每列的结果"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
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      "text/plain": [
       "   A  B   C   D\n",
       "0  0  1   2   3\n",
       "1  4  5   6   7\n",
       "2  8  9  10  11"
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     "execution_count": 9,
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   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    1.5\n",
       "1    5.5\n",
       "2    9.5\n",
       "dtype: float64"
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     "execution_count": 10,
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   "source": [
    "# axis=1 or axis=columns\n",
    "df.mean(axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<img src=\"./other_files/pandas-axis-columns.png\" width=\"700\" />"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "***指定了按哪个axis，就是这个axis要动起来(类似被for遍历)，其它的axis保持不动***"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 5、再次举例，加深理解"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_sum_value(x):\n",
    "    return x[\"A\"] + x[\"B\"] + x[\"C\"] + x[\"D\"]\n",
    "\n",
    "df[\"sum_value\"] = df.apply(get_sum_value, axis=1)"
   ]
  },
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   "cell_type": "markdown",
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   "source": [
    "***指定了按哪个axis，就是这个axis要动起来(类似被for遍历)，其它的axis保持不动***"
   ]
  }
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