theory/python/eyes/12.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 第十二章  Pandas库"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 引子"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Numpy 在向量化的数值计算中表现优异"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "但是在处理更灵活、复杂的数据任务： \n",
    "  \n",
    "如为数据添加标签、处理缺失值、分组和透视表等方面  \n",
    "  \n",
    "Numpy显得力不从心"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**而基于Numpy构建的Pandas库，提供了使得数据分析变得更快更简单的高级数据结构和操作工具**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##   12.1 对象创建"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###  12.1.1 Pandas Series对象"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Series 是带标签数据的一维数组"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Series对象的创建**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "通用结构: pd.Series(data, index=index, dtype=dtype)\n",
    "  \n",
    "data：数据，可以是列表，字典或Numpy数组\n",
    "  \n",
    "index：索引，为可选参数\n",
    "\n",
    "dtype: 数据类型，为可选参数"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**1、用列表创建**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* index缺省，默认为整数序列"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    1.5\n",
       "1    3.0\n",
       "2    4.5\n",
       "3    6.0\n",
       "dtype: float64"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "\n",
    "data = pd.Series([1.5, 3, 4.5, 6])\n",
    "data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* 增加index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "a    1.5\n",
       "b    3.0\n",
       "c    4.5\n",
       "d    6.0\n",
       "dtype: float64"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = pd.Series([1.5, 3, 4.5, 6], index=[\"a\", \"b\", \"c\", \"d\"])\n",
    "data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* 增加数据类型\n",
    "  \n",
    "      缺省则从传入的数据自动判断"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "a    1\n",
       "b    2\n",
       "c    3\n",
       "d    4\n",
       "dtype: int64"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = pd.Series([1, 2, 3, 4], index=[\"a\", \"b\", \"c\", \"d\"])    \n",
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "a    1.0\n",
       "b    2.0\n",
       "c    3.0\n",
       "d    4.0\n",
       "dtype: float64"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = pd.Series([1, 2, 3, 4], index=[\"a\", \"b\", \"c\", \"d\"], dtype=\"float\")\n",
    "data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**注意：数据支持多种类型**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "a    1\n",
       "b    2\n",
       "c    3\n",
       "d    4\n",
       "dtype: object"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = pd.Series([1, 2, \"3\", 4], index=[\"a\", \"b\", \"c\", \"d\"])\n",
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[\"a\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'3'"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[\"c\"]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**数据类型可被强制改变**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "a    1.0\n",
       "b    2.0\n",
       "c    3.0\n",
       "d    4.0\n",
       "dtype: float64"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = pd.Series([1, 2, \"3\", 4], index=[\"a\", \"b\", \"c\", \"d\"], dtype=float)\n",
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3.0"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[\"c\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = pd.Series([1, 2, \"a\", 4], index=[\"a\", \"b\", \"c\", \"d\"], dtype=float)\n",
    "data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**2、用一维numpy数组创建**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    0\n",
       "1    1\n",
       "2    2\n",
       "3    3\n",
       "4    4\n",
       "dtype: int32"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import numpy as np\n",
    "\n",
    "x = np.arange(5)\n",
    "pd.Series(x)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**3、用字典创建**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* 默认以键为index 值为data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "BeiJing     2154\n",
       "ShangHai    2424\n",
       "ShenZhen    1303\n",
       "HangZhou     981\n",
       "dtype: int64"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "population_dict = {\"BeiJing\": 2154,\n",
    "                   \"ShangHai\": 2424,\n",
    "                   \"ShenZhen\": 1303,\n",
    "                   \"HangZhou\": 981 }\n",
    "population = pd.Series(population_dict)    \n",
    "population"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* 字典创建，如果指定index，则会到字典的键中筛选，找不到的，值设为NaN"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "BeiJing     2154.0\n",
       "HangZhou     981.0\n",
       "c              NaN\n",
       "d              NaN\n",
       "dtype: float64"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "population = pd.Series(population_dict, index=[\"BeiJing\", \"HangZhou\", \"c\", \"d\"])    \n",
    "population"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**4、data为标量的情况**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "100    5\n",
       "200    5\n",
       "300    5\n",
       "dtype: int64"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.Series(5, index=[100, 200, 300])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 12.1.2 Pandas DataFrame对象"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "DataFrame 是带标签数据的多维数组"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**DataFrame对象的创建**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "通用结构: pd.DataFrame(data, index=index, columns=columns)\n",
    "  \n",
    "data：数据，可以是列表，字典或Numpy数组\n",
    "  \n",
    "index：索引，为可选参数\n",
    "\n",
    "columns: 列标签，为可选参数"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**1、通过Series对象创建**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "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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       "    }\n",
       "\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>BeiJing</th>\n",
       "      <td>2154</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ShangHai</th>\n",
       "      <td>2424</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ShenZhen</th>\n",
       "      <td>1303</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HangZhou</th>\n",
       "      <td>981</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             0\n",
       "BeiJing   2154\n",
       "ShangHai  2424\n",
       "ShenZhen  1303\n",
       "HangZhou   981"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "population_dict = {\"BeiJing\": 2154,\n",
    "                   \"ShangHai\": 2424,\n",
    "                   \"ShenZhen\": 1303,\n",
    "                   \"HangZhou\": 981 }\n",
    "\n",
    "population = pd.Series(population_dict)    \n",
    "pd.DataFrame(population)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {
    "scrolled": true
   },
   "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>population</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>BeiJing</th>\n",
       "      <td>2154</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ShangHai</th>\n",
       "      <td>2424</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ShenZhen</th>\n",
       "      <td>1303</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HangZhou</th>\n",
       "      <td>981</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          population\n",
       "BeiJing         2154\n",
       "ShangHai        2424\n",
       "ShenZhen        1303\n",
       "HangZhou         981"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.DataFrame(population, columns=[\"population\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**2、通过Series对象字典创建**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "BeiJing     30320\n",
       "ShangHai    32680\n",
       "ShenZhen    24222\n",
       "HangZhou    13468\n",
       "dtype: int64"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "GDP_dict = {\"BeiJing\": 30320,\n",
    "            \"ShangHai\": 32680,\n",
    "            \"ShenZhen\": 24222,\n",
    "            \"HangZhou\": 13468 }\n",
    "\n",
    "GDP = pd.Series(GDP_dict)\n",
    "GDP"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "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>population</th>\n",
       "      <th>GDP</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>BeiJing</th>\n",
       "      <td>2154</td>\n",
       "      <td>30320</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ShangHai</th>\n",
       "      <td>2424</td>\n",
       "      <td>32680</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ShenZhen</th>\n",
       "      <td>1303</td>\n",
       "      <td>24222</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HangZhou</th>\n",
       "      <td>981</td>\n",
       "      <td>13468</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          population    GDP\n",
       "BeiJing         2154  30320\n",
       "ShangHai        2424  32680\n",
       "ShenZhen        1303  24222\n",
       "HangZhou         981  13468"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.DataFrame({\"population\": population,\n",
    "              \"GDP\": GDP})"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**注意：数量不够的会自动补齐**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "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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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>population</th>\n",
       "      <th>GDP</th>\n",
       "      <th>country</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>BeiJing</th>\n",
       "      <td>2154</td>\n",
       "      <td>30320</td>\n",
       "      <td>China</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ShangHai</th>\n",
       "      <td>2424</td>\n",
       "      <td>32680</td>\n",
       "      <td>China</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ShenZhen</th>\n",
       "      <td>1303</td>\n",
       "      <td>24222</td>\n",
       "      <td>China</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HangZhou</th>\n",
       "      <td>981</td>\n",
       "      <td>13468</td>\n",
       "      <td>China</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          population    GDP country\n",
       "BeiJing         2154  30320   China\n",
       "ShangHai        2424  32680   China\n",
       "ShenZhen        1303  24222   China\n",
       "HangZhou         981  13468   China"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.DataFrame({\"population\": population,\n",
    "              \"GDP\": GDP,\n",
    "              \"country\": \"China\"})"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**3、通过字典列表对象创建**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* 字典索引作为index，字典键作为columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[{'a': 0, 'b': 0}, {'a': 1, 'b': 2}, {'a': 2, 'b': 4}]"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "data = [{\"a\": i, \"b\": 2*i} for i in range(3)]\n",
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "      <td>0</td>\n",
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       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
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       "  </tbody>\n",
       "</table>\n",
       "</div>"
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      "text/plain": [
       "   a  b\n",
       "0  0  0\n",
       "1  1  2\n",
       "2  2  4"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = pd.DataFrame(data)\n",
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    0\n",
       "1    1\n",
       "2    2\n",
       "Name: a, dtype: int64"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data1 = data[\"a\"].copy()\n",
    "data1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    10\n",
       "1     1\n",
       "2     2\n",
       "Name: a, dtype: int64"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data1[0] = 10\n",
    "data1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "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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       "        text-align: right;\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
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       "      <th>a</th>\n",
       "      <th>b</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
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       "      <td>1</td>\n",
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       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   a  b\n",
       "0  0  0\n",
       "1  1  2\n",
       "2  2  4"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* 不存在的键，会默认值为NaN"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[{'a': 1, 'b': 1}, {'b': 3, 'c': 4}]"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = [{\"a\": 1, \"b\":1},{\"b\": 3, \"c\":4}]\n",
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "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>a</th>\n",
       "      <th>b</th>\n",
       "      <th>c</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>NaN</td>\n",
       "      <td>3</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     a  b    c\n",
       "0  1.0  1  NaN\n",
       "1  NaN  3  4.0"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.DataFrame(data)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**4、通过Numpy二维数组创建**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1, 6],\n",
       "       [2, 9],\n",
       "       [4, 0]])"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = np.random.randint(10, size=(3, 2))\n",
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "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>foo</th>\n",
       "      <th>bar</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>a</th>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>b</th>\n",
       "      <td>2</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>c</th>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   foo  bar\n",
       "a    1    6\n",
       "b    2    9\n",
       "c    4    0"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.DataFrame(data, columns=[\"foo\", \"bar\"], index=[\"a\", \"b\", \"c\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 12.2   DataFrame性质"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**1、属性**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {
    "scrolled": true
   },
   "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>pop</th>\n",
       "      <th>GDP</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>BeiJing</th>\n",
       "      <td>2154</td>\n",
       "      <td>30320</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ShangHai</th>\n",
       "      <td>2424</td>\n",
       "      <td>32680</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ShenZhen</th>\n",
       "      <td>1303</td>\n",
       "      <td>24222</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HangZhou</th>\n",
       "      <td>981</td>\n",
       "      <td>13468</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           pop    GDP\n",
       "BeiJing   2154  30320\n",
       "ShangHai  2424  32680\n",
       "ShenZhen  1303  24222\n",
       "HangZhou   981  13468"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = pd.DataFrame({\"pop\": population, \"GDP\": GDP})\n",
    "data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**（1）df.values  返回numpy数组表示的数据**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 2154, 30320],\n",
       "       [ 2424, 32680],\n",
       "       [ 1303, 24222],\n",
       "       [  981, 13468]], dtype=int64)"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.values"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**（2）df.index 返回行索引**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['BeiJing', 'ShangHai', 'ShenZhen', 'HangZhou'], dtype='object')"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.index"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**（3）df.columns 返回列索引**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['pop', 'GDP'], dtype='object')"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.columns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**（4）df.shape  形状**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(4, 2)"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**（5） pd.size 大小**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "8"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.size"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**（6）pd.dtypes 返回每列数据类型**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "pop    int64\n",
       "GDP    int64\n",
       "dtype: object"
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.dtypes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**2、索引**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {
    "scrolled": true
   },
   "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>pop</th>\n",
       "      <th>GDP</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>BeiJing</th>\n",
       "      <td>2154</td>\n",
       "      <td>30320</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ShangHai</th>\n",
       "      <td>2424</td>\n",
       "      <td>32680</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ShenZhen</th>\n",
       "      <td>1303</td>\n",
       "      <td>24222</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HangZhou</th>\n",
       "      <td>981</td>\n",
       "      <td>13468</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           pop    GDP\n",
       "BeiJing   2154  30320\n",
       "ShangHai  2424  32680\n",
       "ShenZhen  1303  24222\n",
       "HangZhou   981  13468"
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**（1）获取列**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* 字典式"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "BeiJing     2154\n",
       "ShangHai    2424\n",
       "ShenZhen    1303\n",
       "HangZhou     981\n",
       "Name: pop, dtype: int64"
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[\"pop\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "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>GDP</th>\n",
       "      <th>pop</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>BeiJing</th>\n",
       "      <td>30320</td>\n",
       "      <td>2154</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ShangHai</th>\n",
       "      <td>32680</td>\n",
       "      <td>2424</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ShenZhen</th>\n",
       "      <td>24222</td>\n",
       "      <td>1303</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HangZhou</th>\n",
       "      <td>13468</td>\n",
       "      <td>981</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            GDP   pop\n",
       "BeiJing   30320  2154\n",
       "ShangHai  32680  2424\n",
       "ShenZhen  24222  1303\n",
       "HangZhou  13468   981"
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[[\"GDP\", \"pop\"]]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* 对象属性式"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "BeiJing     30320\n",
       "ShangHai    32680\n",
       "ShenZhen    24222\n",
       "HangZhou    13468\n",
       "Name: GDP, dtype: int64"
      ]
     },
     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.GDP"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**（2）获取行**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* 绝对索引 df.loc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "pop     2154\n",
       "GDP    30320\n",
       "Name: BeiJing, dtype: int64"
      ]
     },
     "execution_count": 91,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.loc[\"BeiJing\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "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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       "\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>pop</th>\n",
       "      <th>GDP</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>BeiJing</th>\n",
       "      <td>2154</td>\n",
       "      <td>30320</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HangZhou</th>\n",
       "      <td>981</td>\n",
       "      <td>13468</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           pop    GDP\n",
       "BeiJing   2154  30320\n",
       "HangZhou   981  13468"
      ]
     },
     "execution_count": 92,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.loc[[\"BeiJing\", \"HangZhou\"]]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* 相对索引 df.iloc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "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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       "        text-align: right;\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>pop</th>\n",
       "      <th>GDP</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>BeiJing</th>\n",
       "      <td>2154</td>\n",
       "      <td>30320</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ShangHai</th>\n",
       "      <td>2424</td>\n",
       "      <td>32680</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ShenZhen</th>\n",
       "      <td>1303</td>\n",
       "      <td>24222</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HangZhou</th>\n",
       "      <td>981</td>\n",
       "      <td>13468</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           pop    GDP\n",
       "BeiJing   2154  30320\n",
       "ShangHai  2424  32680\n",
       "ShenZhen  1303  24222\n",
       "HangZhou   981  13468"
      ]
     },
     "execution_count": 93,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "pop     2154\n",
       "GDP    30320\n",
       "Name: BeiJing, dtype: int64"
      ]
     },
     "execution_count": 94,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.iloc[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "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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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>pop</th>\n",
       "      <th>GDP</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>ShangHai</th>\n",
       "      <td>2424</td>\n",
       "      <td>32680</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HangZhou</th>\n",
       "      <td>981</td>\n",
       "      <td>13468</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           pop    GDP\n",
       "ShangHai  2424  32680\n",
       "HangZhou   981  13468"
      ]
     },
     "execution_count": 95,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.iloc[[1, 3]]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**（3）获取标量**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {
    "scrolled": true
   },
   "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",
       "        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>pop</th>\n",
       "      <th>GDP</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>BeiJing</th>\n",
       "      <td>2154</td>\n",
       "      <td>30320</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ShangHai</th>\n",
       "      <td>2424</td>\n",
       "      <td>32680</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ShenZhen</th>\n",
       "      <td>1303</td>\n",
       "      <td>24222</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HangZhou</th>\n",
       "      <td>981</td>\n",
       "      <td>13468</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           pop    GDP\n",
       "BeiJing   2154  30320\n",
       "ShangHai  2424  32680\n",
       "ShenZhen  1303  24222\n",
       "HangZhou   981  13468"
      ]
     },
     "execution_count": 96,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "30320"
      ]
     },
     "execution_count": 97,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.loc[\"BeiJing\", \"GDP\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "30320"
      ]
     },
     "execution_count": 98,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.iloc[0, 1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "30320"
      ]
     },
     "execution_count": 99,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.values[0][1]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**（4）Series对象的索引**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "pandas.core.series.Series"
      ]
     },
     "execution_count": 100,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(data.GDP)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "BeiJing     30320\n",
       "ShangHai    32680\n",
       "ShenZhen    24222\n",
       "HangZhou    13468\n",
       "dtype: int64"
      ]
     },
     "execution_count": 101,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "GDP"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "30320"
      ]
     },
     "execution_count": 102,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "GDP[\"BeiJing\"]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**3、切片**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DatetimeIndex(['2019-01-01', '2019-01-02', '2019-01-03', '2019-01-04',\n",
       "               '2019-01-05', '2019-01-06'],\n",
       "              dtype='datetime64[ns]', freq='D')"
      ]
     },
     "execution_count": 103,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dates = pd.date_range(start='2019-01-01', periods=6)\n",
    "dates"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>B</th>\n",
       "      <th>C</th>\n",
       "      <th>D</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2019-01-01</th>\n",
       "      <td>-0.935378</td>\n",
       "      <td>-0.190742</td>\n",
       "      <td>0.925984</td>\n",
       "      <td>-0.818969</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-01-02</th>\n",
       "      <td>-0.234414</td>\n",
       "      <td>-1.194674</td>\n",
       "      <td>1.080779</td>\n",
       "      <td>-2.294395</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-01-03</th>\n",
       "      <td>-0.141572</td>\n",
       "      <td>0.058118</td>\n",
       "      <td>1.102248</td>\n",
       "      <td>1.207726</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-01-04</th>\n",
       "      <td>0.305088</td>\n",
       "      <td>0.535920</td>\n",
       "      <td>-0.978434</td>\n",
       "      <td>0.177251</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-01-05</th>\n",
       "      <td>0.313383</td>\n",
       "      <td>0.234041</td>\n",
       "      <td>0.163155</td>\n",
       "      <td>-0.296649</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-01-06</th>\n",
       "      <td>0.250613</td>\n",
       "      <td>-0.904400</td>\n",
       "      <td>-0.858240</td>\n",
       "      <td>-1.573342</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   A         B         C         D\n",
       "2019-01-01 -0.935378 -0.190742  0.925984 -0.818969\n",
       "2019-01-02 -0.234414 -1.194674  1.080779 -2.294395\n",
       "2019-01-03 -0.141572  0.058118  1.102248  1.207726\n",
       "2019-01-04  0.305088  0.535920 -0.978434  0.177251\n",
       "2019-01-05  0.313383  0.234041  0.163155 -0.296649\n",
       "2019-01-06  0.250613 -0.904400 -0.858240 -1.573342"
      ]
     },
     "execution_count": 104,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.DataFrame(np.random.randn(6,4), index=dates, columns=[\"A\", \"B\", \"C\", \"D\"])\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**（1）行切片**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    <tr>\n",
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       "    <tr>\n",
       "      <th>2019-01-02</th>\n",
       "      <td>-0.234414</td>\n",
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       "                   A         B         C         D\n",
       "2019-01-01 -0.935378 -0.190742  0.925984 -0.818969\n",
       "2019-01-02 -0.234414 -1.194674  1.080779 -2.294395\n",
       "2019-01-03 -0.141572  0.058118  1.102248  1.207726"
      ]
     },
     "execution_count": 105,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"2019-01-01\": \"2019-01-03\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [
    {
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       "      <th>D</th>\n",
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       "      <td>0.925984</td>\n",
       "      <td>-0.818969</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-01-02</th>\n",
       "      <td>-0.234414</td>\n",
       "      <td>-1.194674</td>\n",
       "      <td>1.080779</td>\n",
       "      <td>-2.294395</td>\n",
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       "    <tr>\n",
       "      <th>2019-01-03</th>\n",
       "      <td>-0.141572</td>\n",
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       "      <td>1.207726</td>\n",
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       "</div>"
      ],
      "text/plain": [
       "                   A         B         C         D\n",
       "2019-01-01 -0.935378 -0.190742  0.925984 -0.818969\n",
       "2019-01-02 -0.234414 -1.194674  1.080779 -2.294395\n",
       "2019-01-03 -0.141572  0.058118  1.102248  1.207726"
      ]
     },
     "execution_count": 106,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
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    "df.loc[\"2019-01-01\": \"2019-01-03\"]"
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   "metadata": {},
   "source": [
    "**（3）多种多样的取值**"
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       "                   A         B         C         D\n",
       "2019-01-01 -0.935378 -0.190742  0.925984 -0.818969\n",
       "2019-01-02 -0.234414 -1.194674  1.080779 -2.294395\n",
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   "source": [
    "* 行、列同时切片"
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       "                   C         D\n",
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    "* 行切片，列分散取值"
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       "                   A         C\n",
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       "2019-01-05  0.313383  0.163155\n",
       "2019-01-06  0.250613 -0.858240"
      ]
     },
     "execution_count": 114,
     "metadata": {},
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    "df.loc[\"2019-01-04\": \"2019-01-06\", [\"A\", \"C\"]]"
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   "execution_count": 115,
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       "                   A         C\n",
       "2019-01-04  0.305088 -0.978434\n",
       "2019-01-05  0.313383  0.163155\n",
       "2019-01-06  0.250613 -0.858240"
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     "execution_count": 115,
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   "source": [
    "df.iloc[3:, [0, 2]]"
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   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* 行分散取值，列切片"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.loc[[\"2019-01-02\", \"2019-01-06\"], \"C\": \"D\"]"
   ]
  },
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   "execution_count": 127,
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       "                   A         B         C\n",
       "2019-01-02 -0.234414 -1.194674  1.080779\n",
       "2019-01-06  0.250613 -0.904400 -0.858240"
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   "source": [
    "df.iloc[[1, 5], 0: 3]"
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   "metadata": {},
   "source": [
    "* 行、列均分散取值"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.loc[[\"2019-01-04\", \"2019-01-06\"], [\"A\", \"D\"]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
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       "                   A         D\n",
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   "source": [
    "**4、布尔索引**"
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       "                   A         B         C         D\n",
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       "                A      B      C      D\n",
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       "2019-01-03  False   True   True   True\n",
       "2019-01-04   True   True  False   True\n",
       "2019-01-05   True   True   True  False\n",
       "2019-01-06   True  False  False  False"
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     "execution_count": 134,
     "metadata": {},
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       "                   A         B         C         D\n",
       "2019-01-01       NaN       NaN  0.925984       NaN\n",
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       "2019-01-05  0.313383  0.234041  0.163155       NaN\n",
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     "data": {
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       "2019-01-01    False\n",
       "2019-01-02    False\n",
       "2019-01-03    False\n",
       "2019-01-04     True\n",
       "2019-01-05     True\n",
       "2019-01-06     True\n",
       "Freq: D, Name: A, dtype: bool"
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   "source": [
    "* isin（）方法"
   ]
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       "      <td>1.080779</td>\n",
       "      <td>-2.294395</td>\n",
       "      <td>one</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-01-03</th>\n",
       "      <td>-0.141572</td>\n",
       "      <td>0.058118</td>\n",
       "      <td>1.102248</td>\n",
       "      <td>1.207726</td>\n",
       "      <td>two</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-01-04</th>\n",
       "      <td>0.305088</td>\n",
       "      <td>0.535920</td>\n",
       "      <td>-0.978434</td>\n",
       "      <td>0.177251</td>\n",
       "      <td>three</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-01-05</th>\n",
       "      <td>0.313383</td>\n",
       "      <td>0.234041</td>\n",
       "      <td>0.163155</td>\n",
       "      <td>-0.296649</td>\n",
       "      <td>four</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-01-06</th>\n",
       "      <td>0.250613</td>\n",
       "      <td>-0.904400</td>\n",
       "      <td>-0.858240</td>\n",
       "      <td>-1.573342</td>\n",
       "      <td>three</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   A         B         C         D      E\n",
       "2019-01-01 -0.935378 -0.190742  0.925984 -0.818969    one\n",
       "2019-01-02 -0.234414 -1.194674  1.080779 -2.294395    one\n",
       "2019-01-03 -0.141572  0.058118  1.102248  1.207726    two\n",
       "2019-01-04  0.305088  0.535920 -0.978434  0.177251  three\n",
       "2019-01-05  0.313383  0.234041  0.163155 -0.296649   four\n",
       "2019-01-06  0.250613 -0.904400 -0.858240 -1.573342  three"
      ]
     },
     "execution_count": 138,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2 = df.copy()\n",
    "df2['E'] = ['one', 'one', 'two', 'three', 'four', 'three']\n",
    "df2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2019-01-01    False\n",
       "2019-01-02    False\n",
       "2019-01-03     True\n",
       "2019-01-04    False\n",
       "2019-01-05     True\n",
       "2019-01-06    False\n",
       "Freq: D, Name: E, dtype: bool"
      ]
     },
     "execution_count": 139,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ind = df2[\"E\"].isin([\"two\", \"four\"])\n",
    "ind     "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "metadata": {},
   "outputs": [
    {
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       "      <td>1.207726</td>\n",
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       "    <tr>\n",
       "      <th>2019-01-05</th>\n",
       "      <td>0.313383</td>\n",
       "      <td>0.234041</td>\n",
       "      <td>0.163155</td>\n",
       "      <td>-0.296649</td>\n",
       "      <td>four</td>\n",
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      "text/plain": [
       "                   A         B         C         D     E\n",
       "2019-01-03 -0.141572  0.058118  1.102248  1.207726   two\n",
       "2019-01-05  0.313383  0.234041  0.163155 -0.296649  four"
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     "execution_count": 140,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2[ind]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**（5）赋值**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* DataFrame 增加新列"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2019-01-01    1\n",
       "2019-01-02    2\n",
       "2019-01-03    3\n",
       "2019-01-04    4\n",
       "2019-01-05    5\n",
       "2019-01-06    6\n",
       "Freq: D, dtype: int64"
      ]
     },
     "execution_count": 141,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s1 = pd.Series([1, 2, 3, 4, 5, 6], index=pd.date_range('20190101', periods=6))\n",
    "s1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 142,
   "metadata": {
    "scrolled": true
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   "outputs": [
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       "    <tr>\n",
       "      <th>2019-01-05</th>\n",
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       "      <td>0.234041</td>\n",
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       "    <tr>\n",
       "      <th>2019-01-06</th>\n",
       "      <td>0.250613</td>\n",
       "      <td>-0.904400</td>\n",
       "      <td>-0.858240</td>\n",
       "      <td>-1.573342</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   A         B         C         D  E\n",
       "2019-01-01 -0.935378 -0.190742  0.925984 -0.818969  1\n",
       "2019-01-02 -0.234414 -1.194674  1.080779 -2.294395  2\n",
       "2019-01-03 -0.141572  0.058118  1.102248  1.207726  3\n",
       "2019-01-04  0.305088  0.535920 -0.978434  0.177251  4\n",
       "2019-01-05  0.313383  0.234041  0.163155 -0.296649  5\n",
       "2019-01-06  0.250613 -0.904400 -0.858240 -1.573342  6"
      ]
     },
     "execution_count": 142,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"E\"] = s1\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* 修改赋值"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 143,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>2019-01-03</th>\n",
       "      <td>-0.141572</td>\n",
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       "      <th>2019-01-04</th>\n",
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       "      <td>4</td>\n",
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       "    <tr>\n",
       "      <th>2019-01-05</th>\n",
       "      <td>0.313383</td>\n",
       "      <td>0.234041</td>\n",
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       "    <tr>\n",
       "      <th>2019-01-06</th>\n",
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       "      <td>-0.858240</td>\n",
       "      <td>-1.573342</td>\n",
       "      <td>6</td>\n",
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      "text/plain": [
       "                   A         B         C         D  E\n",
       "2019-01-01  0.000000 -0.190742  0.925984 -0.818969  1\n",
       "2019-01-02 -0.234414 -1.194674  1.080779 -2.294395  2\n",
       "2019-01-03 -0.141572  0.058118  1.102248  1.207726  3\n",
       "2019-01-04  0.305088  0.535920 -0.978434  0.177251  4\n",
       "2019-01-05  0.313383  0.234041  0.163155 -0.296649  5\n",
       "2019-01-06  0.250613 -0.904400 -0.858240 -1.573342  6"
      ]
     },
     "execution_count": 143,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.loc[\"2019-01-01\", \"A\"] = 0\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 145,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>4</td>\n",
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       "    <tr>\n",
       "      <th>2019-01-05</th>\n",
       "      <td>0.313383</td>\n",
       "      <td>0.234041</td>\n",
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       "      <td>-0.296649</td>\n",
       "      <td>5</td>\n",
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       "    <tr>\n",
       "      <th>2019-01-06</th>\n",
       "      <td>0.250613</td>\n",
       "      <td>-0.904400</td>\n",
       "      <td>-0.858240</td>\n",
       "      <td>-1.573342</td>\n",
       "      <td>6</td>\n",
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      "text/plain": [
       "                   A         B         C         D  E\n",
       "2019-01-01  0.000000  0.000000  0.925984 -0.818969  1\n",
       "2019-01-02 -0.234414 -1.194674  1.080779 -2.294395  2\n",
       "2019-01-03 -0.141572  0.058118  1.102248  1.207726  3\n",
       "2019-01-04  0.305088  0.535920 -0.978434  0.177251  4\n",
       "2019-01-05  0.313383  0.234041  0.163155 -0.296649  5\n",
       "2019-01-06  0.250613 -0.904400 -0.858240 -1.573342  6"
      ]
     },
     "execution_count": 145,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.iloc[0, 1] = 0\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 147,
   "metadata": {},
   "outputs": [
    {
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       "      <th>2019-01-04</th>\n",
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       "    <tr>\n",
       "      <th>2019-01-05</th>\n",
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       "      <th>2019-01-06</th>\n",
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       "                   A         B         C  D  E\n",
       "2019-01-01  0.000000  0.000000  0.925984  5  1\n",
       "2019-01-02 -0.234414 -1.194674  1.080779  5  2\n",
       "2019-01-03 -0.141572  0.058118  1.102248  5  3\n",
       "2019-01-04  0.305088  0.535920 -0.978434  5  4\n",
       "2019-01-05  0.313383  0.234041  0.163155  5  5\n",
       "2019-01-06  0.250613 -0.904400 -0.858240  5  6"
      ]
     },
     "execution_count": 147,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"D\"] = np.array([5]*len(df))   # 可简化成df[\"D\"] = 5\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* 修改index和columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 148,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
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       "      <th>B</th>\n",
       "      <th>C</th>\n",
       "      <th>D</th>\n",
       "      <th>E</th>\n",
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       "      <td>0.000000</td>\n",
       "      <td>0.925984</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-0.234414</td>\n",
       "      <td>-1.194674</td>\n",
       "      <td>1.080779</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-0.141572</td>\n",
       "      <td>0.058118</td>\n",
       "      <td>1.102248</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.305088</td>\n",
       "      <td>0.535920</td>\n",
       "      <td>-0.978434</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.313383</td>\n",
       "      <td>0.234041</td>\n",
       "      <td>0.163155</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.250613</td>\n",
       "      <td>-0.904400</td>\n",
       "      <td>-0.858240</td>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          A         B         C  D  E\n",
       "0  0.000000  0.000000  0.925984  5  1\n",
       "1 -0.234414 -1.194674  1.080779  5  2\n",
       "2 -0.141572  0.058118  1.102248  5  3\n",
       "3  0.305088  0.535920 -0.978434  5  4\n",
       "4  0.313383  0.234041  0.163155  5  5\n",
       "5  0.250613 -0.904400 -0.858240  5  6"
      ]
     },
     "execution_count": 148,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.index = [i for i in range(len(df))]\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 149,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>0.925984</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-0.234414</td>\n",
       "      <td>-1.194674</td>\n",
       "      <td>1.080779</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-0.141572</td>\n",
       "      <td>0.058118</td>\n",
       "      <td>1.102248</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.305088</td>\n",
       "      <td>0.535920</td>\n",
       "      <td>-0.978434</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.313383</td>\n",
       "      <td>0.234041</td>\n",
       "      <td>0.163155</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.250613</td>\n",
       "      <td>-0.904400</td>\n",
       "      <td>-0.858240</td>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          0         1         2  3  4\n",
       "0  0.000000  0.000000  0.925984  5  1\n",
       "1 -0.234414 -1.194674  1.080779  5  2\n",
       "2 -0.141572  0.058118  1.102248  5  3\n",
       "3  0.305088  0.535920 -0.978434  5  4\n",
       "4  0.313383  0.234041  0.163155  5  5\n",
       "5  0.250613 -0.904400 -0.858240  5  6"
      ]
     },
     "execution_count": 149,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns = [i for i in range(df.shape[1])]\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 12.3 数值运算及统计分析"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**1、数据的查看**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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       "  <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>2019-01-01</th>\n",
       "      <td>-0.854043</td>\n",
       "      <td>0.412345</td>\n",
       "      <td>-2.296051</td>\n",
       "      <td>-0.048964</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-01-02</th>\n",
       "      <td>1.371364</td>\n",
       "      <td>-0.121454</td>\n",
       "      <td>-0.299653</td>\n",
       "      <td>1.095375</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-01-03</th>\n",
       "      <td>-0.714591</td>\n",
       "      <td>-1.103224</td>\n",
       "      <td>0.979250</td>\n",
       "      <td>0.319455</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-01-04</th>\n",
       "      <td>-1.397557</td>\n",
       "      <td>0.426008</td>\n",
       "      <td>0.233861</td>\n",
       "      <td>-1.651887</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-01-05</th>\n",
       "      <td>0.434026</td>\n",
       "      <td>0.459830</td>\n",
       "      <td>-0.095444</td>\n",
       "      <td>1.220302</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-01-06</th>\n",
       "      <td>-0.133876</td>\n",
       "      <td>0.074500</td>\n",
       "      <td>-1.028147</td>\n",
       "      <td>0.605402</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   A         B         C         D\n",
       "2019-01-01 -0.854043  0.412345 -2.296051 -0.048964\n",
       "2019-01-02  1.371364 -0.121454 -0.299653  1.095375\n",
       "2019-01-03 -0.714591 -1.103224  0.979250  0.319455\n",
       "2019-01-04 -1.397557  0.426008  0.233861 -1.651887\n",
       "2019-01-05  0.434026  0.459830 -0.095444  1.220302\n",
       "2019-01-06 -0.133876  0.074500 -1.028147  0.605402"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "dates = pd.date_range(start='2019-01-01', periods=6)\n",
    "df = pd.DataFrame(np.random.randn(6,4), index=dates, columns=[\"A\", \"B\", \"C\", \"D\"])\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**（1）查看前面的行**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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       "      <th>D</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2019-01-01</th>\n",
       "      <td>-0.854043</td>\n",
       "      <td>0.412345</td>\n",
       "      <td>-2.296051</td>\n",
       "      <td>-0.048964</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-01-02</th>\n",
       "      <td>1.371364</td>\n",
       "      <td>-0.121454</td>\n",
       "      <td>-0.299653</td>\n",
       "      <td>1.095375</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-01-03</th>\n",
       "      <td>-0.714591</td>\n",
       "      <td>-1.103224</td>\n",
       "      <td>0.979250</td>\n",
       "      <td>0.319455</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-01-04</th>\n",
       "      <td>-1.397557</td>\n",
       "      <td>0.426008</td>\n",
       "      <td>0.233861</td>\n",
       "      <td>-1.651887</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-01-05</th>\n",
       "      <td>0.434026</td>\n",
       "      <td>0.459830</td>\n",
       "      <td>-0.095444</td>\n",
       "      <td>1.220302</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   A         B         C         D\n",
       "2019-01-01 -0.854043  0.412345 -2.296051 -0.048964\n",
       "2019-01-02  1.371364 -0.121454 -0.299653  1.095375\n",
       "2019-01-03 -0.714591 -1.103224  0.979250  0.319455\n",
       "2019-01-04 -1.397557  0.426008  0.233861 -1.651887\n",
       "2019-01-05  0.434026  0.459830 -0.095444  1.220302"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()    # 默认5行"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th>C</th>\n",
       "      <th>D</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2019-01-01</th>\n",
       "      <td>-0.854043</td>\n",
       "      <td>0.412345</td>\n",
       "      <td>-2.296051</td>\n",
       "      <td>-0.048964</td>\n",
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       "    <tr>\n",
       "      <th>2019-01-02</th>\n",
       "      <td>1.371364</td>\n",
       "      <td>-0.121454</td>\n",
       "      <td>-0.299653</td>\n",
       "      <td>1.095375</td>\n",
       "    </tr>\n",
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       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   A         B         C         D\n",
       "2019-01-01 -0.854043  0.412345 -2.296051 -0.048964\n",
       "2019-01-02  1.371364 -0.121454 -0.299653  1.095375"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head(2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**（2）查看后面的行**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>D</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2019-01-02</th>\n",
       "      <td>1.371364</td>\n",
       "      <td>-0.121454</td>\n",
       "      <td>-0.299653</td>\n",
       "      <td>1.095375</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-01-03</th>\n",
       "      <td>-0.714591</td>\n",
       "      <td>-1.103224</td>\n",
       "      <td>0.979250</td>\n",
       "      <td>0.319455</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-01-04</th>\n",
       "      <td>-1.397557</td>\n",
       "      <td>0.426008</td>\n",
       "      <td>0.233861</td>\n",
       "      <td>-1.651887</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-01-05</th>\n",
       "      <td>0.434026</td>\n",
       "      <td>0.459830</td>\n",
       "      <td>-0.095444</td>\n",
       "      <td>1.220302</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-01-06</th>\n",
       "      <td>-0.133876</td>\n",
       "      <td>0.074500</td>\n",
       "      <td>-1.028147</td>\n",
       "      <td>0.605402</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   A         B         C         D\n",
       "2019-01-02  1.371364 -0.121454 -0.299653  1.095375\n",
       "2019-01-03 -0.714591 -1.103224  0.979250  0.319455\n",
       "2019-01-04 -1.397557  0.426008  0.233861 -1.651887\n",
       "2019-01-05  0.434026  0.459830 -0.095444  1.220302\n",
       "2019-01-06 -0.133876  0.074500 -1.028147  0.605402"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.tail()    # 默认5行"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "scrolled": false
   },
   "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>2019-01-04</th>\n",
       "      <td>-1.397557</td>\n",
       "      <td>0.426008</td>\n",
       "      <td>0.233861</td>\n",
       "      <td>-1.651887</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-01-05</th>\n",
       "      <td>0.434026</td>\n",
       "      <td>0.459830</td>\n",
       "      <td>-0.095444</td>\n",
       "      <td>1.220302</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-01-06</th>\n",
       "      <td>-0.133876</td>\n",
       "      <td>0.074500</td>\n",
       "      <td>-1.028147</td>\n",
       "      <td>0.605402</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   A         B         C         D\n",
       "2019-01-04 -1.397557  0.426008  0.233861 -1.651887\n",
       "2019-01-05  0.434026  0.459830 -0.095444  1.220302\n",
       "2019-01-06 -0.133876  0.074500 -1.028147  0.605402"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.tail(3) "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**（3）查看总体信息**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2019-01-01</th>\n",
       "      <td>-0.854043</td>\n",
       "      <td>0.412345</td>\n",
       "      <td>-2.296051</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-01-02</th>\n",
       "      <td>1.371364</td>\n",
       "      <td>-0.121454</td>\n",
       "      <td>-0.299653</td>\n",
       "      <td>1.095375</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-01-03</th>\n",
       "      <td>-0.714591</td>\n",
       "      <td>-1.103224</td>\n",
       "      <td>0.979250</td>\n",
       "      <td>0.319455</td>\n",
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       "    <tr>\n",
       "      <th>2019-01-04</th>\n",
       "      <td>-1.397557</td>\n",
       "      <td>0.426008</td>\n",
       "      <td>0.233861</td>\n",
       "      <td>-1.651887</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-01-05</th>\n",
       "      <td>0.434026</td>\n",
       "      <td>0.459830</td>\n",
       "      <td>-0.095444</td>\n",
       "      <td>1.220302</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-01-06</th>\n",
       "      <td>-0.133876</td>\n",
       "      <td>0.074500</td>\n",
       "      <td>-1.028147</td>\n",
       "      <td>0.605402</td>\n",
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       "  </tbody>\n",
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      "text/plain": [
       "                   A         B         C         D\n",
       "2019-01-01 -0.854043  0.412345 -2.296051       NaN\n",
       "2019-01-02  1.371364 -0.121454 -0.299653  1.095375\n",
       "2019-01-03 -0.714591 -1.103224  0.979250  0.319455\n",
       "2019-01-04 -1.397557  0.426008  0.233861 -1.651887\n",
       "2019-01-05  0.434026  0.459830 -0.095444  1.220302\n",
       "2019-01-06 -0.133876  0.074500 -1.028147  0.605402"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.iloc[0, 3] = np.nan\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "DatetimeIndex: 6 entries, 2019-01-01 to 2019-01-06\n",
      "Freq: D\n",
      "Data columns (total 4 columns):\n",
      "A    6 non-null float64\n",
      "B    6 non-null float64\n",
      "C    6 non-null float64\n",
      "D    5 non-null float64\n",
      "dtypes: float64(4)\n",
      "memory usage: 240.0 bytes\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**2、Numpy通用函数同样适用于Pandas**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**（1）向量化运算**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "scrolled": true
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    "x = pd.DataFrame(np.arange(4).reshape(1, 4))\n",
    "x"
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    "x+5"
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   "execution_count": 13,
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    "np.exp(x)"
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  {
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   "source": [
    "y = pd.DataFrame(np.arange(4,8).reshape(1, 4))\n",
    "y"
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  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
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   "source": [
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  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**（2）矩阵化运算**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
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       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>9</td>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "      <td>...</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>8</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>9</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>6</td>\n",
       "      <td>3</td>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>...</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>6</td>\n",
       "      <td>8</td>\n",
       "      <td>8</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>7</td>\n",
       "      <td>5</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>8</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>3</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>6</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "      <td>...</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>4</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>9</td>\n",
       "      <td>1</td>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "      <td>9</td>\n",
       "      <td>6</td>\n",
       "      <td>9</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "      <td>7</td>\n",
       "      <td>9</td>\n",
       "      <td>...</td>\n",
       "      <td>6</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>8</td>\n",
       "      <td>9</td>\n",
       "      <td>5</td>\n",
       "      <td>9</td>\n",
       "      <td>9</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>3</td>\n",
       "      <td>9</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>6</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>2</td>\n",
       "      <td>9</td>\n",
       "      <td>6</td>\n",
       "      <td>9</td>\n",
       "      <td>4</td>\n",
       "      <td>9</td>\n",
       "      <td>4</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>8</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>9</td>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>8</td>\n",
       "      <td>7</td>\n",
       "      <td>...</td>\n",
       "      <td>5</td>\n",
       "      <td>8</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>9</td>\n",
       "      <td>9</td>\n",
       "      <td>4</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>6</td>\n",
       "      <td>9</td>\n",
       "      <td>9</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>9</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>...</td>\n",
       "      <td>6</td>\n",
       "      <td>3</td>\n",
       "      <td>9</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>3</td>\n",
       "      <td>8</td>\n",
       "      <td>4</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>3</td>\n",
       "      <td>9</td>\n",
       "      <td>4</td>\n",
       "      <td>8</td>\n",
       "      <td>7</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>8</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>2</td>\n",
       "      <td>8</td>\n",
       "      <td>6</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>9</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>8</td>\n",
       "      <td>...</td>\n",
       "      <td>6</td>\n",
       "      <td>9</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "      <td>9</td>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "      <td>7</td>\n",
       "      <td>9</td>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "      <td>9</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>...</td>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "      <td>9</td>\n",
       "      <td>9</td>\n",
       "      <td>2</td>\n",
       "      <td>6</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>1</td>\n",
       "      <td>9</td>\n",
       "      <td>3</td>\n",
       "      <td>7</td>\n",
       "      <td>8</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>6</td>\n",
       "      <td>8</td>\n",
       "      <td>9</td>\n",
       "      <td>7</td>\n",
       "      <td>5</td>\n",
       "      <td>7</td>\n",
       "      <td>...</td>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "      <td>9</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>6</td>\n",
       "      <td>7</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>6</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>6</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>9</td>\n",
       "      <td>9</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>6</td>\n",
       "      <td>9</td>\n",
       "      <td>9</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>...</td>\n",
       "      <td>8</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>6</td>\n",
       "      <td>9</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>9</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>7</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>...</td>\n",
       "      <td>3</td>\n",
       "      <td>7</td>\n",
       "      <td>8</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>9</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>9</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>8</td>\n",
       "      <td>9</td>\n",
       "      <td>...</td>\n",
       "      <td>9</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>7</td>\n",
       "      <td>5</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>6</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>9</td>\n",
       "      <td>7</td>\n",
       "      <td>2</td>\n",
       "      <td>6</td>\n",
       "      <td>2</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>5</td>\n",
       "      <td>9</td>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>8</td>\n",
       "      <td>3</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>2</td>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>...</td>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "      <td>3</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>6</td>\n",
       "      <td>8</td>\n",
       "      <td>6</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>7</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>7</td>\n",
       "      <td>5</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>9</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>9</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>9</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "      <td>8</td>\n",
       "      <td>7</td>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>30 rows × 30 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    0   1   2   3   4   5   6   7   8   9   ...  20  21  22  23  24  25  26  \\\n",
       "0    6   3   7   4   6   9   2   6   7   4  ...   4   0   9   5   8   0   9   \n",
       "1    8   2   4   2   6   4   8   6   1   3  ...   2   0   3   1   7   3   1   \n",
       "2    3   5   1   9   1   9   3   7   6   8  ...   6   8   7   0   7   7   2   \n",
       "3    2   0   4   9   6   9   8   6   8   7  ...   0   2   4   2   0   4   9   \n",
       "4    9   9   2   6   0   3   3   4   6   6  ...   9   6   8   6   0   0   8   \n",
       "5    2   6   5   7   8   4   0   2   9   7  ...   2   0   4   0   7   0   0   \n",
       "6    6   4   0   0   2   1   4   9   5   6  ...   5   0   8   5   2   3   3   \n",
       "7    2   3   6   3   8   0   7   6   1   7  ...   3   0   1   0   4   4   6   \n",
       "8    2   2   3   7   5   7   0   7   3   0  ...   1   1   5   2   8   3   0   \n",
       "9    3   7   7   6   2   0   0   2   5   6  ...   4   2   3   2   0   0   4   \n",
       "10   4   7   0   4   2   0   3   4   6   0  ...   5   6   1   9   1   9   0   \n",
       "11   5   6   9   6   9   2   1   8   7   9  ...   6   5   2   8   9   5   9   \n",
       "12   3   9   5   5   4   0   7   4   4   6  ...   0   7   2   9   6   9   4   \n",
       "13   8   4   0   9   9   0   1   5   8   7  ...   5   8   4   0   3   4   9   \n",
       "14   3   0   4   6   9   9   5   4   3   1  ...   6   1   0   3   7   1   2   \n",
       "15   4   2   0   0   7   9   1   2   1   2  ...   6   3   9   4   1   7   3   \n",
       "16   3   9   4   8   7   2   0   2   3   1  ...   8   0   0   3   8   5   2   \n",
       "17   2   8   6   3   2   9   4   4   2   8  ...   6   9   4   2   6   1   8   \n",
       "18   5   6   7   9   8   1   9   1   4   4  ...   3   5   2   5   6   9   9   \n",
       "19   1   9   3   7   8   6   0   2   8   0  ...   4   3   2   2   3   8   1   \n",
       "20   4   5   5   2   6   8   9   7   5   7  ...   3   5   0   8   0   4   3   \n",
       "21   2   4   8   1   9   7   1   4   6   7  ...   0   1   8   2   0   4   6   \n",
       "22   4   5   2   4   6   4   4   4   9   9  ...   1   7   6   9   9   1   5   \n",
       "23   0   5   4   8   0   6   4   4   1   2  ...   8   5   0   7   6   9   2   \n",
       "24   9   7   0   9   0   3   7   4   1   5  ...   3   7   8   2   2   1   9   \n",
       "25   4   1   9   5   4   5   0   4   8   9  ...   9   3   0   7   0   2   3   \n",
       "26   6   7   1   9   7   2   6   2   6   1  ...   0   6   5   9   8   0   3   \n",
       "27   2   8   1   3   5   1   7   7   0   2  ...   8   0   4   5   4   5   5   \n",
       "28   6   8   6   2   2   7   4   3   7   5  ...   1   7   9   2   4   5   9   \n",
       "29   3   0   3   0   0   9   5   4   3   2  ...   1   3   0   4   8   0   8   \n",
       "\n",
       "    27  28  29  \n",
       "0    2   6   3  \n",
       "1    5   5   9  \n",
       "2    0   7   2  \n",
       "3    6   6   8  \n",
       "4    8   3   8  \n",
       "5    1   1   5  \n",
       "6    2   9   2  \n",
       "7    8   8   2  \n",
       "8    3   0   4  \n",
       "9    5   2   8  \n",
       "10   7   0   8  \n",
       "11   9   5   0  \n",
       "12   9   4   6  \n",
       "13   9   4   6  \n",
       "14   0   0   2  \n",
       "15   8   4   8  \n",
       "16   0   3   8  \n",
       "17   9   9   0  \n",
       "18   2   6   2  \n",
       "19   8   0   0  \n",
       "20   2   5   1  \n",
       "21   5   0   4  \n",
       "22   5   2   1  \n",
       "23   0   4   3  \n",
       "24   2   2   4  \n",
       "25   7   5   9  \n",
       "26   8   3   9  \n",
       "27   6   3   7  \n",
       "28   5   3   2  \n",
       "29   7   5   6  \n",
       "\n",
       "[30 rows x 30 columns]"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.random.seed(42)\n",
    "x = pd.DataFrame(np.random.randint(10, size=(30, 30)))\n",
    "x"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* 转置"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>7</td>\n",
       "      <td>5</td>\n",
       "      <td>9</td>\n",
       "      <td>7</td>\n",
       "      <td>5</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>9</td>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>7</td>\n",
       "      <td>6</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>8</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>3</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>8</td>\n",
       "      <td>4</td>\n",
       "      <td>8</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>9</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>...</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "      <td>3</td>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>8</td>\n",
       "      <td>2</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>...</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>5</td>\n",
       "      <td>7</td>\n",
       "      <td>3</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>9</td>\n",
       "      <td>3</td>\n",
       "      <td>7</td>\n",
       "      <td>4</td>\n",
       "      <td>8</td>\n",
       "      <td>4</td>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>8</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>...</td>\n",
       "      <td>8</td>\n",
       "      <td>2</td>\n",
       "      <td>9</td>\n",
       "      <td>7</td>\n",
       "      <td>2</td>\n",
       "      <td>7</td>\n",
       "      <td>9</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>8</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>9</td>\n",
       "      <td>6</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>8</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>7</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>9</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>9</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>9</td>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>...</td>\n",
       "      <td>3</td>\n",
       "      <td>6</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>9</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "      <td>9</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>8</td>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "      <td>...</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>7</td>\n",
       "      <td>8</td>\n",
       "      <td>6</td>\n",
       "      <td>5</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>6</td>\n",
       "      <td>5</td>\n",
       "      <td>7</td>\n",
       "      <td>6</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>9</td>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>...</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>3</td>\n",
       "      <td>9</td>\n",
       "      <td>2</td>\n",
       "      <td>8</td>\n",
       "      <td>8</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>8</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>9</td>\n",
       "      <td>9</td>\n",
       "      <td>7</td>\n",
       "      <td>2</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>30 rows × 30 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    0   1   2   3   4   5   6   7   8   9   ...  20  21  22  23  24  25  26  \\\n",
       "0    6   8   3   2   9   2   6   2   2   3  ...   4   2   4   0   9   4   6   \n",
       "1    3   2   5   0   9   6   4   3   2   7  ...   5   4   5   5   7   1   7   \n",
       "2    7   4   1   4   2   5   0   6   3   7  ...   5   8   2   4   0   9   1   \n",
       "3    4   2   9   9   6   7   0   3   7   6  ...   2   1   4   8   9   5   9   \n",
       "4    6   6   1   6   0   8   2   8   5   2  ...   6   9   6   0   0   4   7   \n",
       "5    9   4   9   9   3   4   1   0   7   0  ...   8   7   4   6   3   5   2   \n",
       "6    2   8   3   8   3   0   4   7   0   0  ...   9   1   4   4   7   0   6   \n",
       "7    6   6   7   6   4   2   9   6   7   2  ...   7   4   4   4   4   4   2   \n",
       "8    7   1   6   8   6   9   5   1   3   5  ...   5   6   9   1   1   8   6   \n",
       "9    4   3   8   7   6   7   6   7   0   6  ...   7   7   9   2   5   9   1   \n",
       "10   3   8   7   1   3   5   3   0   7   5  ...   4   0   2   6   4   1   9   \n",
       "11   7   1   4   0   6   7   6   8   3   5  ...   7   5   0   5   1   0   5   \n",
       "12   7   9   1   6   2   8   7   8   5   5  ...   9   0   4   1   2   9   2   \n",
       "13   2   8   4   6   5   3   0   1   7   2  ...   3   1   8   5   8   8   2   \n",
       "14   5   9   7   7   1   0   5   6   3   5  ...   9   0   0   1   6   9   8   \n",
       "15   4   4   9   4   9   0   7   9   2   7  ...   7   4   2   1   6   8   6   \n",
       "16   1   1   8   2   8   9   4   2   8   1  ...   9   9   3   1   5   8   4   \n",
       "17   7   3   8   7   4   3   3   6   2   4  ...   1   8   0   2   7   5   9   \n",
       "18   5   6   0   5   5   6   1   9   8   0  ...   4   5   0   1   3   7   6   \n",
       "19   1   7   8   2   3   1   5   8   1   0  ...   8   0   7   3   7   0   8   \n",
       "20   4   2   6   0   9   2   5   3   1   4  ...   3   0   1   8   3   9   0   \n",
       "21   0   0   8   2   6   0   0   0   1   2  ...   5   1   7   5   7   3   6   \n",
       "22   9   3   7   4   8   4   8   1   5   3  ...   0   8   6   0   8   0   5   \n",
       "23   5   1   0   2   6   0   5   0   2   2  ...   8   2   9   7   2   7   9   \n",
       "24   8   7   7   0   0   7   2   4   8   0  ...   0   0   9   6   2   0   8   \n",
       "25   0   3   7   4   0   0   3   4   3   0  ...   4   4   1   9   1   2   0   \n",
       "26   9   1   2   9   8   0   3   6   0   4  ...   3   6   5   2   9   3   3   \n",
       "27   2   5   0   6   8   1   2   8   3   5  ...   2   5   5   0   2   7   8   \n",
       "28   6   5   7   6   3   1   9   8   0   2  ...   5   0   2   4   2   5   3   \n",
       "29   3   9   2   8   8   5   2   2   4   8  ...   1   4   1   3   4   9   9   \n",
       "\n",
       "    27  28  29  \n",
       "0    2   6   3  \n",
       "1    8   8   0  \n",
       "2    1   6   3  \n",
       "3    3   2   0  \n",
       "4    5   2   0  \n",
       "5    1   7   9  \n",
       "6    7   4   5  \n",
       "7    7   3   4  \n",
       "8    0   7   3  \n",
       "9    2   5   2  \n",
       "10   9   1   0  \n",
       "11   8   3   5  \n",
       "12   4   3   1  \n",
       "13   5   5   7  \n",
       "14   3   5   9  \n",
       "15   9   0   4  \n",
       "16   1   7   6  \n",
       "17   7   5   9  \n",
       "18   5   2   1  \n",
       "19   4   8   7  \n",
       "20   8   1   1  \n",
       "21   0   7   3  \n",
       "22   4   9   0  \n",
       "23   5   2   4  \n",
       "24   4   4   8  \n",
       "25   5   5   0  \n",
       "26   5   9   8  \n",
       "27   6   5   7  \n",
       "28   3   3   5  \n",
       "29   7   2   6  \n",
       "\n",
       "[30 rows x 30 columns]"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "z = x.T\n",
    "z"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>0</th>\n",
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       "      <td>9</td>\n",
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       "    </tr>\n",
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       "      <th>3</th>\n",
       "      <td>6</td>\n",
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       "      <td>7</td>\n",
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       "      <td>...</td>\n",
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       "    </tr>\n",
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       "      <th>4</th>\n",
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       "      <td>0</td>\n",
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       "      <td>6</td>\n",
       "      <td>2</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>8</td>\n",
       "      <td>9</td>\n",
       "      <td>5</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>9</td>\n",
       "      <td>3</td>\n",
       "      <td>9</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>6</td>\n",
       "      <td>8</td>\n",
       "      <td>8</td>\n",
       "      <td>9</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>2</td>\n",
       "      <td>8</td>\n",
       "      <td>...</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>6</td>\n",
       "      <td>7</td>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>8</td>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "      <td>7</td>\n",
       "      <td>5</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>9</td>\n",
       "      <td>6</td>\n",
       "      <td>9</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>9</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>...</td>\n",
       "      <td>6</td>\n",
       "      <td>7</td>\n",
       "      <td>9</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>9</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>6</td>\n",
       "      <td>8</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>2</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>...</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>6</td>\n",
       "      <td>7</td>\n",
       "      <td>6</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>4</td>\n",
       "      <td>7</td>\n",
       "      <td>6</td>\n",
       "      <td>2</td>\n",
       "      <td>9</td>\n",
       "      <td>5</td>\n",
       "      <td>9</td>\n",
       "      <td>9</td>\n",
       "      <td>...</td>\n",
       "      <td>4</td>\n",
       "      <td>9</td>\n",
       "      <td>3</td>\n",
       "      <td>9</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>9</td>\n",
       "      <td>9</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>8</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>9</td>\n",
       "      <td>8</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>7</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>...</td>\n",
       "      <td>8</td>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>9</td>\n",
       "      <td>6</td>\n",
       "      <td>4</td>\n",
       "      <td>9</td>\n",
       "      <td>8</td>\n",
       "      <td>7</td>\n",
       "      <td>5</td>\n",
       "      <td>...</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>8</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>7</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>...</td>\n",
       "      <td>8</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>7</td>\n",
       "      <td>5</td>\n",
       "      <td>9</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>9</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>...</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>3</td>\n",
       "      <td>8</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>5</td>\n",
       "      <td>9</td>\n",
       "      <td>6</td>\n",
       "      <td>4</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>...</td>\n",
       "      <td>3</td>\n",
       "      <td>6</td>\n",
       "      <td>8</td>\n",
       "      <td>6</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>6</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>6</td>\n",
       "      <td>7</td>\n",
       "      <td>2</td>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>9</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>9</td>\n",
       "      <td>8</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>7</td>\n",
       "      <td>9</td>\n",
       "      <td>9</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>6</td>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>6</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>9</td>\n",
       "      <td>8</td>\n",
       "      <td>5</td>\n",
       "      <td>9</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>9</td>\n",
       "      <td>8</td>\n",
       "      <td>6</td>\n",
       "      <td>3</td>\n",
       "      <td>9</td>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>...</td>\n",
       "      <td>2</td>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>9</td>\n",
       "      <td>4</td>\n",
       "      <td>8</td>\n",
       "      <td>8</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>2</td>\n",
       "      <td>8</td>\n",
       "      <td>6</td>\n",
       "      <td>4</td>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>6</td>\n",
       "      <td>7</td>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "      <td>8</td>\n",
       "      <td>7</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>6</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>6</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>9</td>\n",
       "      <td>3</td>\n",
       "      <td>9</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>8</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>9</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>8</td>\n",
       "      <td>3</td>\n",
       "      <td>7</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>7</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>9</td>\n",
       "      <td>4</td>\n",
       "      <td>7</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>9</td>\n",
       "      <td>4</td>\n",
       "      <td>6</td>\n",
       "      <td>9</td>\n",
       "      <td>3</td>\n",
       "      <td>...</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "      <td>9</td>\n",
       "      <td>2</td>\n",
       "      <td>7</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>9</td>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>2</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "      <td>...</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "      <td>6</td>\n",
       "      <td>4</td>\n",
       "      <td>6</td>\n",
       "      <td>9</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>7</td>\n",
       "      <td>2</td>\n",
       "      <td>...</td>\n",
       "      <td>9</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>7</td>\n",
       "      <td>8</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>8</td>\n",
       "      <td>3</td>\n",
       "      <td>7</td>\n",
       "      <td>3</td>\n",
       "      <td>9</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>6</td>\n",
       "      <td>8</td>\n",
       "      <td>3</td>\n",
       "      <td>6</td>\n",
       "      <td>4</td>\n",
       "      <td>9</td>\n",
       "      <td>7</td>\n",
       "      <td>...</td>\n",
       "      <td>6</td>\n",
       "      <td>7</td>\n",
       "      <td>8</td>\n",
       "      <td>5</td>\n",
       "      <td>7</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>30 rows × 30 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    0   1   2   3   4   5   6   7   8   9   ...  20  21  22  23  24  25  26  \\\n",
       "0    5   8   9   5   0   0   1   7   6   9  ...   1   7   0   6   9   9   7   \n",
       "1    0   1   8   8   3   9   8   7   3   6  ...   9   2   0   4   9   2   7   \n",
       "2    6   9   3   7   7   4   5   9   3   6  ...   7   7   1   1   3   0   8   \n",
       "3    6   2   5   7   8   4   4   7   7   4  ...   0   1   9   8   2   3   1   \n",
       "4    6   0   9   2   6   6   2   7   7   0  ...   1   5   4   0   7   8   9   \n",
       "5    9   3   9   1   4   4   6   8   8   9  ...   1   8   7   0   3   4   2   \n",
       "6    1   2   4   3   0   6   0   7   2   8  ...   4   3   3   6   7   3   5   \n",
       "7    4   0   3   3   8   3   5   6   7   5  ...   1   7   3   1   6   6   9   \n",
       "8    0   0   2   9   6   0   6   7   0   3  ...   6   7   9   5   4   9   5   \n",
       "9    6   8   7   7   7   2   6   0   5   2  ...   7   0   6   2   4   3   6   \n",
       "10   0   6   4   7   6   2   9   5   9   9  ...   4   9   3   9   1   2   5   \n",
       "11   2   3   9   9   4   4   8   2   1   6  ...   0   5   9   8   6   6   0   \n",
       "12   0   1   6   0   6   1   6   4   2   5  ...   8   8   0   7   2   0   7   \n",
       "13   5   1   5   9   6   4   9   8   7   5  ...   2   4   3   2   0   0   4   \n",
       "14   0   3   8   5   3   1   4   7   3   2  ...   8   5   5   7   5   9   1   \n",
       "15   3   3   6   1   3   0   5   0   5   2  ...   7   1   7   7   3   8   3   \n",
       "16   0   6   5   9   6   4   6   6   2   2  ...   3   6   8   6   5   1   3   \n",
       "17   6   7   2   8   0   1   8   6   0   0  ...   5   6   2   5   4   3   0   \n",
       "18   9   4   4   0   9   8   7   7   6   1  ...   7   9   9   7   1   1   4   \n",
       "19   4   1   1   5   1   2   6   2   3   3  ...   0   0   0   9   8   5   9   \n",
       "20   9   8   6   3   9   9   0   8   1   6  ...   2   9   0   1   3   9   4   \n",
       "21   2   8   6   4   9   0   5   5   6   1  ...   6   7   5   6   8   7   4   \n",
       "22   0   3   5   9   0   3   6   5   1   1  ...   6   2   5   3   9   3   9   \n",
       "23   7   7   0   8   6   1   2   0   4   4  ...   1   9   6   0   2   8   3   \n",
       "24   6   0   4   2   3   1   0   5   7   0  ...   1   1   2   7   5   2   9   \n",
       "25   5   0   2   1   4   9   4   6   9   3  ...   5   5   3   5   9   2   7   \n",
       "26   9   8   1   8   1   6   2   6   1   8  ...   2   5   1   2   5   3   3   \n",
       "27   1   8   6   4   6   9   5   4   7   2  ...   9   3   1   5   1   1   7   \n",
       "28   0   7   7   4   3   2   7   8   5   2  ...   0   2   8   3   7   3   9   \n",
       "29   8   0   2   6   8   3   6   4   9   7  ...   6   7   8   5   7   2   5   \n",
       "\n",
       "    27  28  29  \n",
       "0    6   9   1  \n",
       "1    7   9   8  \n",
       "2    6   4   5  \n",
       "3    2   7   2  \n",
       "4    5   7   0  \n",
       "5    0   3   5  \n",
       "6    3   2   4  \n",
       "7    6   9   6  \n",
       "8    2   5   6  \n",
       "9    7   6   3  \n",
       "10   4   0   8  \n",
       "11   4   7   3  \n",
       "12   1   1   9  \n",
       "13   2   5   0  \n",
       "14   3   9   3  \n",
       "15   0   6   3  \n",
       "16   2   6   3  \n",
       "17   6   2   1  \n",
       "18   6   5   6  \n",
       "19   3   4   0  \n",
       "20   8   8   8  \n",
       "21   2   4   0  \n",
       "22   5   1   9  \n",
       "23   7   2   5  \n",
       "24   4   7   3  \n",
       "25   4   1   6  \n",
       "26   6   1   8  \n",
       "27   1   2   6  \n",
       "28   2   3   8  \n",
       "29   3   4   5  \n",
       "\n",
       "[30 rows x 30 columns]"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.random.seed(1)\n",
    "y = pd.DataFrame(np.random.randint(10, size=(30, 30)))\n",
    "y"
   ]
  },
  {
   "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>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>5</th>\n",
       "      <th>6</th>\n",
       "      <th>7</th>\n",
       "      <th>8</th>\n",
       "      <th>9</th>\n",
       "      <th>...</th>\n",
       "      <th>20</th>\n",
       "      <th>21</th>\n",
       "      <th>22</th>\n",
       "      <th>23</th>\n",
       "      <th>24</th>\n",
       "      <th>25</th>\n",
       "      <th>26</th>\n",
       "      <th>27</th>\n",
       "      <th>28</th>\n",
       "      <th>29</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>616</td>\n",
       "      <td>560</td>\n",
       "      <td>723</td>\n",
       "      <td>739</td>\n",
       "      <td>612</td>\n",
       "      <td>457</td>\n",
       "      <td>681</td>\n",
       "      <td>799</td>\n",
       "      <td>575</td>\n",
       "      <td>590</td>\n",
       "      <td>...</td>\n",
       "      <td>523</td>\n",
       "      <td>739</td>\n",
       "      <td>613</td>\n",
       "      <td>580</td>\n",
       "      <td>668</td>\n",
       "      <td>602</td>\n",
       "      <td>733</td>\n",
       "      <td>585</td>\n",
       "      <td>657</td>\n",
       "      <td>700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>520</td>\n",
       "      <td>438</td>\n",
       "      <td>691</td>\n",
       "      <td>600</td>\n",
       "      <td>612</td>\n",
       "      <td>455</td>\n",
       "      <td>666</td>\n",
       "      <td>764</td>\n",
       "      <td>707</td>\n",
       "      <td>592</td>\n",
       "      <td>...</td>\n",
       "      <td>555</td>\n",
       "      <td>681</td>\n",
       "      <td>503</td>\n",
       "      <td>679</td>\n",
       "      <td>641</td>\n",
       "      <td>506</td>\n",
       "      <td>779</td>\n",
       "      <td>494</td>\n",
       "      <td>633</td>\n",
       "      <td>590</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>557</td>\n",
       "      <td>570</td>\n",
       "      <td>786</td>\n",
       "      <td>807</td>\n",
       "      <td>690</td>\n",
       "      <td>469</td>\n",
       "      <td>804</td>\n",
       "      <td>828</td>\n",
       "      <td>704</td>\n",
       "      <td>573</td>\n",
       "      <td>...</td>\n",
       "      <td>563</td>\n",
       "      <td>675</td>\n",
       "      <td>712</td>\n",
       "      <td>758</td>\n",
       "      <td>793</td>\n",
       "      <td>672</td>\n",
       "      <td>754</td>\n",
       "      <td>550</td>\n",
       "      <td>756</td>\n",
       "      <td>638</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>605</td>\n",
       "      <td>507</td>\n",
       "      <td>664</td>\n",
       "      <td>701</td>\n",
       "      <td>660</td>\n",
       "      <td>496</td>\n",
       "      <td>698</td>\n",
       "      <td>806</td>\n",
       "      <td>651</td>\n",
       "      <td>575</td>\n",
       "      <td>...</td>\n",
       "      <td>582</td>\n",
       "      <td>685</td>\n",
       "      <td>668</td>\n",
       "      <td>586</td>\n",
       "      <td>629</td>\n",
       "      <td>534</td>\n",
       "      <td>678</td>\n",
       "      <td>484</td>\n",
       "      <td>591</td>\n",
       "      <td>626</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>599</td>\n",
       "      <td>681</td>\n",
       "      <td>753</td>\n",
       "      <td>873</td>\n",
       "      <td>721</td>\n",
       "      <td>563</td>\n",
       "      <td>754</td>\n",
       "      <td>770</td>\n",
       "      <td>620</td>\n",
       "      <td>654</td>\n",
       "      <td>...</td>\n",
       "      <td>633</td>\n",
       "      <td>747</td>\n",
       "      <td>661</td>\n",
       "      <td>677</td>\n",
       "      <td>726</td>\n",
       "      <td>649</td>\n",
       "      <td>716</td>\n",
       "      <td>610</td>\n",
       "      <td>735</td>\n",
       "      <td>706</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>422</td>\n",
       "      <td>354</td>\n",
       "      <td>602</td>\n",
       "      <td>627</td>\n",
       "      <td>613</td>\n",
       "      <td>396</td>\n",
       "      <td>617</td>\n",
       "      <td>627</td>\n",
       "      <td>489</td>\n",
       "      <td>423</td>\n",
       "      <td>...</td>\n",
       "      <td>456</td>\n",
       "      <td>572</td>\n",
       "      <td>559</td>\n",
       "      <td>537</td>\n",
       "      <td>499</td>\n",
       "      <td>384</td>\n",
       "      <td>589</td>\n",
       "      <td>436</td>\n",
       "      <td>574</td>\n",
       "      <td>507</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>359</td>\n",
       "      <td>446</td>\n",
       "      <td>599</td>\n",
       "      <td>599</td>\n",
       "      <td>481</td>\n",
       "      <td>357</td>\n",
       "      <td>577</td>\n",
       "      <td>572</td>\n",
       "      <td>451</td>\n",
       "      <td>464</td>\n",
       "      <td>...</td>\n",
       "      <td>449</td>\n",
       "      <td>550</td>\n",
       "      <td>495</td>\n",
       "      <td>532</td>\n",
       "      <td>633</td>\n",
       "      <td>554</td>\n",
       "      <td>663</td>\n",
       "      <td>476</td>\n",
       "      <td>565</td>\n",
       "      <td>602</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>531</td>\n",
       "      <td>520</td>\n",
       "      <td>698</td>\n",
       "      <td>590</td>\n",
       "      <td>607</td>\n",
       "      <td>537</td>\n",
       "      <td>665</td>\n",
       "      <td>696</td>\n",
       "      <td>571</td>\n",
       "      <td>472</td>\n",
       "      <td>...</td>\n",
       "      <td>576</td>\n",
       "      <td>588</td>\n",
       "      <td>551</td>\n",
       "      <td>665</td>\n",
       "      <td>652</td>\n",
       "      <td>527</td>\n",
       "      <td>742</td>\n",
       "      <td>528</td>\n",
       "      <td>650</td>\n",
       "      <td>599</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>449</td>\n",
       "      <td>322</td>\n",
       "      <td>547</td>\n",
       "      <td>533</td>\n",
       "      <td>593</td>\n",
       "      <td>399</td>\n",
       "      <td>584</td>\n",
       "      <td>638</td>\n",
       "      <td>587</td>\n",
       "      <td>424</td>\n",
       "      <td>...</td>\n",
       "      <td>402</td>\n",
       "      <td>596</td>\n",
       "      <td>523</td>\n",
       "      <td>523</td>\n",
       "      <td>447</td>\n",
       "      <td>362</td>\n",
       "      <td>561</td>\n",
       "      <td>386</td>\n",
       "      <td>529</td>\n",
       "      <td>484</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>373</td>\n",
       "      <td>433</td>\n",
       "      <td>525</td>\n",
       "      <td>601</td>\n",
       "      <td>522</td>\n",
       "      <td>345</td>\n",
       "      <td>551</td>\n",
       "      <td>521</td>\n",
       "      <td>434</td>\n",
       "      <td>447</td>\n",
       "      <td>...</td>\n",
       "      <td>508</td>\n",
       "      <td>498</td>\n",
       "      <td>438</td>\n",
       "      <td>478</td>\n",
       "      <td>459</td>\n",
       "      <td>418</td>\n",
       "      <td>488</td>\n",
       "      <td>407</td>\n",
       "      <td>503</td>\n",
       "      <td>496</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>500</td>\n",
       "      <td>427</td>\n",
       "      <td>574</td>\n",
       "      <td>607</td>\n",
       "      <td>667</td>\n",
       "      <td>477</td>\n",
       "      <td>652</td>\n",
       "      <td>656</td>\n",
       "      <td>615</td>\n",
       "      <td>477</td>\n",
       "      <td>...</td>\n",
       "      <td>622</td>\n",
       "      <td>702</td>\n",
       "      <td>531</td>\n",
       "      <td>610</td>\n",
       "      <td>558</td>\n",
       "      <td>532</td>\n",
       "      <td>598</td>\n",
       "      <td>471</td>\n",
       "      <td>582</td>\n",
       "      <td>561</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>664</td>\n",
       "      <td>694</td>\n",
       "      <td>772</td>\n",
       "      <td>841</td>\n",
       "      <td>779</td>\n",
       "      <td>574</td>\n",
       "      <td>730</td>\n",
       "      <td>810</td>\n",
       "      <td>711</td>\n",
       "      <td>608</td>\n",
       "      <td>...</td>\n",
       "      <td>591</td>\n",
       "      <td>760</td>\n",
       "      <td>616</td>\n",
       "      <td>638</td>\n",
       "      <td>721</td>\n",
       "      <td>676</td>\n",
       "      <td>846</td>\n",
       "      <td>678</td>\n",
       "      <td>754</td>\n",
       "      <td>708</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>545</td>\n",
       "      <td>547</td>\n",
       "      <td>687</td>\n",
       "      <td>701</td>\n",
       "      <td>721</td>\n",
       "      <td>576</td>\n",
       "      <td>689</td>\n",
       "      <td>724</td>\n",
       "      <td>710</td>\n",
       "      <td>532</td>\n",
       "      <td>...</td>\n",
       "      <td>674</td>\n",
       "      <td>684</td>\n",
       "      <td>648</td>\n",
       "      <td>694</td>\n",
       "      <td>710</td>\n",
       "      <td>564</td>\n",
       "      <td>757</td>\n",
       "      <td>571</td>\n",
       "      <td>671</td>\n",
       "      <td>656</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>574</td>\n",
       "      <td>586</td>\n",
       "      <td>723</td>\n",
       "      <td>750</td>\n",
       "      <td>691</td>\n",
       "      <td>494</td>\n",
       "      <td>696</td>\n",
       "      <td>787</td>\n",
       "      <td>667</td>\n",
       "      <td>523</td>\n",
       "      <td>...</td>\n",
       "      <td>618</td>\n",
       "      <td>681</td>\n",
       "      <td>568</td>\n",
       "      <td>682</td>\n",
       "      <td>715</td>\n",
       "      <td>644</td>\n",
       "      <td>756</td>\n",
       "      <td>557</td>\n",
       "      <td>690</td>\n",
       "      <td>604</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>502</td>\n",
       "      <td>382</td>\n",
       "      <td>645</td>\n",
       "      <td>557</td>\n",
       "      <td>570</td>\n",
       "      <td>403</td>\n",
       "      <td>538</td>\n",
       "      <td>677</td>\n",
       "      <td>500</td>\n",
       "      <td>501</td>\n",
       "      <td>...</td>\n",
       "      <td>369</td>\n",
       "      <td>650</td>\n",
       "      <td>507</td>\n",
       "      <td>576</td>\n",
       "      <td>546</td>\n",
       "      <td>531</td>\n",
       "      <td>554</td>\n",
       "      <td>437</td>\n",
       "      <td>616</td>\n",
       "      <td>463</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>510</td>\n",
       "      <td>505</td>\n",
       "      <td>736</td>\n",
       "      <td>651</td>\n",
       "      <td>649</td>\n",
       "      <td>510</td>\n",
       "      <td>719</td>\n",
       "      <td>733</td>\n",
       "      <td>694</td>\n",
       "      <td>557</td>\n",
       "      <td>...</td>\n",
       "      <td>605</td>\n",
       "      <td>717</td>\n",
       "      <td>574</td>\n",
       "      <td>642</td>\n",
       "      <td>678</td>\n",
       "      <td>576</td>\n",
       "      <td>755</td>\n",
       "      <td>455</td>\n",
       "      <td>598</td>\n",
       "      <td>654</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>567</td>\n",
       "      <td>376</td>\n",
       "      <td>614</td>\n",
       "      <td>612</td>\n",
       "      <td>643</td>\n",
       "      <td>514</td>\n",
       "      <td>598</td>\n",
       "      <td>724</td>\n",
       "      <td>547</td>\n",
       "      <td>464</td>\n",
       "      <td>...</td>\n",
       "      <td>456</td>\n",
       "      <td>639</td>\n",
       "      <td>520</td>\n",
       "      <td>560</td>\n",
       "      <td>569</td>\n",
       "      <td>442</td>\n",
       "      <td>596</td>\n",
       "      <td>517</td>\n",
       "      <td>659</td>\n",
       "      <td>532</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>626</td>\n",
       "      <td>716</td>\n",
       "      <td>828</td>\n",
       "      <td>765</td>\n",
       "      <td>740</td>\n",
       "      <td>603</td>\n",
       "      <td>809</td>\n",
       "      <td>852</td>\n",
       "      <td>692</td>\n",
       "      <td>591</td>\n",
       "      <td>...</td>\n",
       "      <td>664</td>\n",
       "      <td>716</td>\n",
       "      <td>655</td>\n",
       "      <td>721</td>\n",
       "      <td>742</td>\n",
       "      <td>612</td>\n",
       "      <td>819</td>\n",
       "      <td>593</td>\n",
       "      <td>744</td>\n",
       "      <td>712</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>600</td>\n",
       "      <td>559</td>\n",
       "      <td>667</td>\n",
       "      <td>664</td>\n",
       "      <td>641</td>\n",
       "      <td>556</td>\n",
       "      <td>624</td>\n",
       "      <td>815</td>\n",
       "      <td>638</td>\n",
       "      <td>564</td>\n",
       "      <td>...</td>\n",
       "      <td>581</td>\n",
       "      <td>701</td>\n",
       "      <td>559</td>\n",
       "      <td>677</td>\n",
       "      <td>710</td>\n",
       "      <td>554</td>\n",
       "      <td>748</td>\n",
       "      <td>597</td>\n",
       "      <td>614</td>\n",
       "      <td>657</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>445</td>\n",
       "      <td>431</td>\n",
       "      <td>661</td>\n",
       "      <td>681</td>\n",
       "      <td>641</td>\n",
       "      <td>552</td>\n",
       "      <td>690</td>\n",
       "      <td>719</td>\n",
       "      <td>602</td>\n",
       "      <td>474</td>\n",
       "      <td>...</td>\n",
       "      <td>515</td>\n",
       "      <td>637</td>\n",
       "      <td>576</td>\n",
       "      <td>620</td>\n",
       "      <td>572</td>\n",
       "      <td>512</td>\n",
       "      <td>599</td>\n",
       "      <td>455</td>\n",
       "      <td>622</td>\n",
       "      <td>538</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>523</td>\n",
       "      <td>569</td>\n",
       "      <td>784</td>\n",
       "      <td>725</td>\n",
       "      <td>713</td>\n",
       "      <td>501</td>\n",
       "      <td>740</td>\n",
       "      <td>772</td>\n",
       "      <td>638</td>\n",
       "      <td>640</td>\n",
       "      <td>...</td>\n",
       "      <td>589</td>\n",
       "      <td>775</td>\n",
       "      <td>664</td>\n",
       "      <td>686</td>\n",
       "      <td>726</td>\n",
       "      <td>672</td>\n",
       "      <td>747</td>\n",
       "      <td>548</td>\n",
       "      <td>723</td>\n",
       "      <td>645</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>487</td>\n",
       "      <td>465</td>\n",
       "      <td>553</td>\n",
       "      <td>639</td>\n",
       "      <td>517</td>\n",
       "      <td>449</td>\n",
       "      <td>592</td>\n",
       "      <td>609</td>\n",
       "      <td>454</td>\n",
       "      <td>398</td>\n",
       "      <td>...</td>\n",
       "      <td>492</td>\n",
       "      <td>567</td>\n",
       "      <td>534</td>\n",
       "      <td>404</td>\n",
       "      <td>554</td>\n",
       "      <td>417</td>\n",
       "      <td>561</td>\n",
       "      <td>466</td>\n",
       "      <td>498</td>\n",
       "      <td>492</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>479</td>\n",
       "      <td>449</td>\n",
       "      <td>574</td>\n",
       "      <td>686</td>\n",
       "      <td>583</td>\n",
       "      <td>377</td>\n",
       "      <td>566</td>\n",
       "      <td>614</td>\n",
       "      <td>563</td>\n",
       "      <td>455</td>\n",
       "      <td>...</td>\n",
       "      <td>453</td>\n",
       "      <td>539</td>\n",
       "      <td>491</td>\n",
       "      <td>501</td>\n",
       "      <td>596</td>\n",
       "      <td>520</td>\n",
       "      <td>722</td>\n",
       "      <td>478</td>\n",
       "      <td>565</td>\n",
       "      <td>501</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>483</td>\n",
       "      <td>386</td>\n",
       "      <td>476</td>\n",
       "      <td>526</td>\n",
       "      <td>550</td>\n",
       "      <td>426</td>\n",
       "      <td>492</td>\n",
       "      <td>585</td>\n",
       "      <td>536</td>\n",
       "      <td>482</td>\n",
       "      <td>...</td>\n",
       "      <td>322</td>\n",
       "      <td>541</td>\n",
       "      <td>438</td>\n",
       "      <td>456</td>\n",
       "      <td>487</td>\n",
       "      <td>408</td>\n",
       "      <td>502</td>\n",
       "      <td>426</td>\n",
       "      <td>474</td>\n",
       "      <td>481</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>523</td>\n",
       "      <td>551</td>\n",
       "      <td>658</td>\n",
       "      <td>767</td>\n",
       "      <td>537</td>\n",
       "      <td>444</td>\n",
       "      <td>663</td>\n",
       "      <td>731</td>\n",
       "      <td>576</td>\n",
       "      <td>577</td>\n",
       "      <td>...</td>\n",
       "      <td>522</td>\n",
       "      <td>590</td>\n",
       "      <td>525</td>\n",
       "      <td>664</td>\n",
       "      <td>691</td>\n",
       "      <td>548</td>\n",
       "      <td>635</td>\n",
       "      <td>526</td>\n",
       "      <td>641</td>\n",
       "      <td>538</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>652</td>\n",
       "      <td>656</td>\n",
       "      <td>738</td>\n",
       "      <td>753</td>\n",
       "      <td>853</td>\n",
       "      <td>508</td>\n",
       "      <td>752</td>\n",
       "      <td>815</td>\n",
       "      <td>669</td>\n",
       "      <td>576</td>\n",
       "      <td>...</td>\n",
       "      <td>694</td>\n",
       "      <td>833</td>\n",
       "      <td>693</td>\n",
       "      <td>606</td>\n",
       "      <td>575</td>\n",
       "      <td>616</td>\n",
       "      <td>704</td>\n",
       "      <td>559</td>\n",
       "      <td>728</td>\n",
       "      <td>672</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>578</td>\n",
       "      <td>577</td>\n",
       "      <td>744</td>\n",
       "      <td>856</td>\n",
       "      <td>699</td>\n",
       "      <td>497</td>\n",
       "      <td>779</td>\n",
       "      <td>800</td>\n",
       "      <td>733</td>\n",
       "      <td>587</td>\n",
       "      <td>...</td>\n",
       "      <td>630</td>\n",
       "      <td>754</td>\n",
       "      <td>704</td>\n",
       "      <td>834</td>\n",
       "      <td>760</td>\n",
       "      <td>680</td>\n",
       "      <td>765</td>\n",
       "      <td>592</td>\n",
       "      <td>731</td>\n",
       "      <td>629</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>554</td>\n",
       "      <td>494</td>\n",
       "      <td>665</td>\n",
       "      <td>689</td>\n",
       "      <td>630</td>\n",
       "      <td>574</td>\n",
       "      <td>695</td>\n",
       "      <td>703</td>\n",
       "      <td>636</td>\n",
       "      <td>599</td>\n",
       "      <td>...</td>\n",
       "      <td>554</td>\n",
       "      <td>685</td>\n",
       "      <td>532</td>\n",
       "      <td>658</td>\n",
       "      <td>649</td>\n",
       "      <td>554</td>\n",
       "      <td>693</td>\n",
       "      <td>577</td>\n",
       "      <td>634</td>\n",
       "      <td>668</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>498</td>\n",
       "      <td>552</td>\n",
       "      <td>659</td>\n",
       "      <td>784</td>\n",
       "      <td>552</td>\n",
       "      <td>492</td>\n",
       "      <td>690</td>\n",
       "      <td>775</td>\n",
       "      <td>544</td>\n",
       "      <td>551</td>\n",
       "      <td>...</td>\n",
       "      <td>567</td>\n",
       "      <td>636</td>\n",
       "      <td>518</td>\n",
       "      <td>599</td>\n",
       "      <td>742</td>\n",
       "      <td>521</td>\n",
       "      <td>733</td>\n",
       "      <td>533</td>\n",
       "      <td>605</td>\n",
       "      <td>604</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>513</td>\n",
       "      <td>491</td>\n",
       "      <td>563</td>\n",
       "      <td>642</td>\n",
       "      <td>477</td>\n",
       "      <td>367</td>\n",
       "      <td>589</td>\n",
       "      <td>647</td>\n",
       "      <td>516</td>\n",
       "      <td>484</td>\n",
       "      <td>...</td>\n",
       "      <td>428</td>\n",
       "      <td>574</td>\n",
       "      <td>504</td>\n",
       "      <td>548</td>\n",
       "      <td>553</td>\n",
       "      <td>483</td>\n",
       "      <td>540</td>\n",
       "      <td>407</td>\n",
       "      <td>547</td>\n",
       "      <td>455</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>30 rows × 30 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     0    1    2    3    4    5    6    7    8    9   ...   20   21   22   23  \\\n",
       "0   616  560  723  739  612  457  681  799  575  590  ...  523  739  613  580   \n",
       "1   520  438  691  600  612  455  666  764  707  592  ...  555  681  503  679   \n",
       "2   557  570  786  807  690  469  804  828  704  573  ...  563  675  712  758   \n",
       "3   605  507  664  701  660  496  698  806  651  575  ...  582  685  668  586   \n",
       "4   599  681  753  873  721  563  754  770  620  654  ...  633  747  661  677   \n",
       "5   422  354  602  627  613  396  617  627  489  423  ...  456  572  559  537   \n",
       "6   359  446  599  599  481  357  577  572  451  464  ...  449  550  495  532   \n",
       "7   531  520  698  590  607  537  665  696  571  472  ...  576  588  551  665   \n",
       "8   449  322  547  533  593  399  584  638  587  424  ...  402  596  523  523   \n",
       "9   373  433  525  601  522  345  551  521  434  447  ...  508  498  438  478   \n",
       "10  500  427  574  607  667  477  652  656  615  477  ...  622  702  531  610   \n",
       "11  664  694  772  841  779  574  730  810  711  608  ...  591  760  616  638   \n",
       "12  545  547  687  701  721  576  689  724  710  532  ...  674  684  648  694   \n",
       "13  574  586  723  750  691  494  696  787  667  523  ...  618  681  568  682   \n",
       "14  502  382  645  557  570  403  538  677  500  501  ...  369  650  507  576   \n",
       "15  510  505  736  651  649  510  719  733  694  557  ...  605  717  574  642   \n",
       "16  567  376  614  612  643  514  598  724  547  464  ...  456  639  520  560   \n",
       "17  626  716  828  765  740  603  809  852  692  591  ...  664  716  655  721   \n",
       "18  600  559  667  664  641  556  624  815  638  564  ...  581  701  559  677   \n",
       "19  445  431  661  681  641  552  690  719  602  474  ...  515  637  576  620   \n",
       "20  523  569  784  725  713  501  740  772  638  640  ...  589  775  664  686   \n",
       "21  487  465  553  639  517  449  592  609  454  398  ...  492  567  534  404   \n",
       "22  479  449  574  686  583  377  566  614  563  455  ...  453  539  491  501   \n",
       "23  483  386  476  526  550  426  492  585  536  482  ...  322  541  438  456   \n",
       "24  523  551  658  767  537  444  663  731  576  577  ...  522  590  525  664   \n",
       "25  652  656  738  753  853  508  752  815  669  576  ...  694  833  693  606   \n",
       "26  578  577  744  856  699  497  779  800  733  587  ...  630  754  704  834   \n",
       "27  554  494  665  689  630  574  695  703  636  599  ...  554  685  532  658   \n",
       "28  498  552  659  784  552  492  690  775  544  551  ...  567  636  518  599   \n",
       "29  513  491  563  642  477  367  589  647  516  484  ...  428  574  504  548   \n",
       "\n",
       "     24   25   26   27   28   29  \n",
       "0   668  602  733  585  657  700  \n",
       "1   641  506  779  494  633  590  \n",
       "2   793  672  754  550  756  638  \n",
       "3   629  534  678  484  591  626  \n",
       "4   726  649  716  610  735  706  \n",
       "5   499  384  589  436  574  507  \n",
       "6   633  554  663  476  565  602  \n",
       "7   652  527  742  528  650  599  \n",
       "8   447  362  561  386  529  484  \n",
       "9   459  418  488  407  503  496  \n",
       "10  558  532  598  471  582  561  \n",
       "11  721  676  846  678  754  708  \n",
       "12  710  564  757  571  671  656  \n",
       "13  715  644  756  557  690  604  \n",
       "14  546  531  554  437  616  463  \n",
       "15  678  576  755  455  598  654  \n",
       "16  569  442  596  517  659  532  \n",
       "17  742  612  819  593  744  712  \n",
       "18  710  554  748  597  614  657  \n",
       "19  572  512  599  455  622  538  \n",
       "20  726  672  747  548  723  645  \n",
       "21  554  417  561  466  498  492  \n",
       "22  596  520  722  478  565  501  \n",
       "23  487  408  502  426  474  481  \n",
       "24  691  548  635  526  641  538  \n",
       "25  575  616  704  559  728  672  \n",
       "26  760  680  765  592  731  629  \n",
       "27  649  554  693  577  634  668  \n",
       "28  742  521  733  533  605  604  \n",
       "29  553  483  540  407  547  455  \n",
       "\n",
       "[30 rows x 30 columns]"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x.dot(y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "218 µs ± 18.7 µs per loop (mean ± std. dev. of 7 runs, 1000 loops each)\n"
     ]
    }
   ],
   "source": [
    "%timeit x.dot(y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "81.1 µs ± 2.85 µs per loop (mean ± std. dev. of 7 runs, 10000 loops each)\n"
     ]
    }
   ],
   "source": [
    "%timeit np.dot(x, y)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* 执行相同运算，Numpy与Pandas的对比"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "x1 = np.array(x)\n",
    "x1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "y1 = np.array(y)\n",
    "y1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "22.1 µs ± 992 ns per loop (mean ± std. dev. of 7 runs, 10000 loops each)\n"
     ]
    }
   ],
   "source": [
    "%timeit x1.dot(y1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "22.6 µs ± 766 ns per loop (mean ± std. dev. of 7 runs, 10000 loops each)\n"
     ]
    }
   ],
   "source": [
    "%timeit np.dot(x1, y1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "42.9 µs ± 1.24 µs per loop (mean ± std. dev. of 7 runs, 10000 loops each)\n"
     ]
    }
   ],
   "source": [
    "%timeit np.dot(x.values, y.values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "x2 = list(x1)\n",
    "y2 = list(y1)\n",
    "x3 = []\n",
    "y3 = []\n",
    "for i in x2:\n",
    "    res = []\n",
    "    for j in i:\n",
    "        res.append(int(j))\n",
    "    x3.append(res)\n",
    "for i in y2:\n",
    "    res = []\n",
    "    for j in i:\n",
    "        res.append(int(j))\n",
    "    y3.append(res)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "def f(x, y):\n",
    "    res = []\n",
    "    for i in range(len(x)):\n",
    "        row = []\n",
    "        for j in range(len(y[0])):\n",
    "            sum_row = 0\n",
    "            for k in range(len(x[0])):\n",
    "                sum_row += x[i][k]*y[k][j]\n",
    "            row.append(sum_row)\n",
    "        res.append(row)\n",
    "    return res          "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "4.29 ms ± 207 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)\n"
     ]
    }
   ],
   "source": [
    "%timeit f(x3, y3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**一般来说，纯粹的计算在Numpy里执行的更快**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Numpy更侧重于计算，Pandas更侧重于数据处理"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**（3）广播运算**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <td>6</td>\n",
       "      <td>9</td>\n",
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       "      <th>2</th>\n",
       "      <td>2</td>\n",
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      "text/plain": [
       "   A  B  C\n",
       "0  6  3  7\n",
       "1  4  6  9\n",
       "2  2  6  7"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.random.seed(42)\n",
    "x = pd.DataFrame(np.random.randint(10, size=(3, 3)), columns=list(\"ABC\"))\n",
    "x"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* 按行广播"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "A    6\n",
       "B    3\n",
       "C    7\n",
       "Name: 0, dtype: int32"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x.iloc[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.333333</td>\n",
       "      <td>2.0</td>\n",
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       "          A    B         C\n",
       "0  1.000000  1.0  1.000000\n",
       "1  0.666667  2.0  1.285714\n",
       "2  0.333333  2.0  1.000000"
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     "execution_count": 46,
     "metadata": {},
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   "source": [
    "x/x.iloc[0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* 按列广播"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    6\n",
       "1    4\n",
       "2    2\n",
       "Name: A, dtype: int32"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x.A"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
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       "      <td>1.0</td>\n",
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       "     A    B         C\n",
       "0  1.0  0.5  1.166667\n",
       "1  1.0  1.5  2.250000\n",
       "2  1.0  3.0  3.500000"
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     "execution_count": 48,
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   "source": [
    "x.div(x.A, axis=0)             # add sub div mul"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
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      "text/plain": [
       "          A    B         C\n",
       "0  1.000000  1.0  1.000000\n",
       "1  0.666667  2.0  1.285714\n",
       "2  0.333333  2.0  1.000000"
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     "execution_count": 49,
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   "source": [
    "x.div(x.iloc[0], axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**3、新的用法**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**（1）索引对齐**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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      "text/plain": [
       "   A  B\n",
       "0  3  7\n",
       "1  2  1"
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     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "A = pd.DataFrame(np.random.randint(0, 20, size=(2, 2)), columns=list(\"AB\"))\n",
    "A"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "scrolled": false
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   "outputs": [
    {
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      "text/plain": [
       "   A  B  C\n",
       "0  7  5  1\n",
       "1  4  0  9\n",
       "2  5  8  0"
      ]
     },
     "execution_count": 51,
     "metadata": {},
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   ],
   "source": [
    "B = pd.DataFrame(np.random.randint(0, 10, size=(3, 3)), columns=list(\"ABC\"))\n",
    "B"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* pandas会自动对齐两个对象的索引，没有的值用np.nan表示"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>10.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>6.0</td>\n",
       "      <td>1.0</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>NaN</td>\n",
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      "text/plain": [
       "      A     B   C\n",
       "0  10.0  12.0 NaN\n",
       "1   6.0   1.0 NaN\n",
       "2   NaN   NaN NaN"
      ]
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     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
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   "source": [
    "A+B"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* 缺省值也可用fill_value来填充"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      A     B    C\n",
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   "source": [
    "A.add(B, fill_value=0)"
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  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th>0</th>\n",
       "      <td>21.0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>8.0</td>\n",
       "      <td>0.0</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\n",
       "0  21.0  35.0 NaN\n",
       "1   8.0   0.0 NaN\n",
       "2   NaN   NaN NaN"
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     "execution_count": 54,
     "metadata": {},
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   ],
   "source": [
    "A*B"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**（2）统计相关**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* 数据种类统计"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([2, 2, 2, 1, 2, 1, 1, 2, 1, 2, 2, 0, 2, 0, 2, 2, 0, 0, 2, 1])"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y = np.random.randint(3, size=20)\n",
    "y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 1, 2])"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.unique(y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Counter({2: 11, 1: 5, 0: 4})"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from collections import Counter\n",
    "Counter(y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "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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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>A</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    A\n",
       "0   2\n",
       "1   2\n",
       "2   2\n",
       "3   1\n",
       "4   2\n",
       "5   1\n",
       "6   1\n",
       "7   2\n",
       "8   1\n",
       "9   2\n",
       "10  2\n",
       "11  0\n",
       "12  2\n",
       "13  0\n",
       "14  2\n",
       "15  2\n",
       "16  0\n",
       "17  0\n",
       "18  2\n",
       "19  1"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y1 = pd.DataFrame(y, columns=[\"A\"])\n",
    "y1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 1, 2])"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.unique(y1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2    11\n",
       "1     5\n",
       "0     4\n",
       "Name: A, dtype: int64"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y1[\"A\"].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* 产生新的结果，并进行排序"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "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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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>population</th>\n",
       "      <th>GDP</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>BeiJing</th>\n",
       "      <td>2154</td>\n",
       "      <td>30320</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ShangHai</th>\n",
       "      <td>2424</td>\n",
       "      <td>32680</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ShenZhen</th>\n",
       "      <td>1303</td>\n",
       "      <td>24222</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HangZhou</th>\n",
       "      <td>981</td>\n",
       "      <td>13468</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          population    GDP\n",
       "BeiJing         2154  30320\n",
       "ShangHai        2424  32680\n",
       "ShenZhen        1303  24222\n",
       "HangZhou         981  13468"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "population_dict = {\"BeiJing\": 2154,\n",
    "                   \"ShangHai\": 2424,\n",
    "                   \"ShenZhen\": 1303,\n",
    "                   \"HangZhou\": 981 }\n",
    "population = pd.Series(population_dict) \n",
    "\n",
    "GDP_dict = {\"BeiJing\": 30320,\n",
    "            \"ShangHai\": 32680,\n",
    "            \"ShenZhen\": 24222,\n",
    "            \"HangZhou\": 13468 }\n",
    "GDP = pd.Series(GDP_dict)\n",
    "\n",
    "city_info = pd.DataFrame({\"population\": population,\"GDP\": GDP})\n",
    "city_info"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>population</th>\n",
       "      <th>GDP</th>\n",
       "      <th>per_GDP</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>BeiJing</th>\n",
       "      <td>2154</td>\n",
       "      <td>30320</td>\n",
       "      <td>14.076137</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ShangHai</th>\n",
       "      <td>2424</td>\n",
       "      <td>32680</td>\n",
       "      <td>13.481848</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ShenZhen</th>\n",
       "      <td>1303</td>\n",
       "      <td>24222</td>\n",
       "      <td>18.589409</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HangZhou</th>\n",
       "      <td>981</td>\n",
       "      <td>13468</td>\n",
       "      <td>13.728848</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          population    GDP    per_GDP\n",
       "BeiJing         2154  30320  14.076137\n",
       "ShangHai        2424  32680  13.481848\n",
       "ShenZhen        1303  24222  18.589409\n",
       "HangZhou         981  13468  13.728848"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "city_info[\"per_GDP\"] = city_info[\"GDP\"]/city_info[\"population\"]\n",
    "city_info"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "递增排序"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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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>population</th>\n",
       "      <th>GDP</th>\n",
       "      <th>per_GDP</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>ShangHai</th>\n",
       "      <td>2424</td>\n",
       "      <td>32680</td>\n",
       "      <td>13.481848</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HangZhou</th>\n",
       "      <td>981</td>\n",
       "      <td>13468</td>\n",
       "      <td>13.728848</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BeiJing</th>\n",
       "      <td>2154</td>\n",
       "      <td>30320</td>\n",
       "      <td>14.076137</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ShenZhen</th>\n",
       "      <td>1303</td>\n",
       "      <td>24222</td>\n",
       "      <td>18.589409</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          population    GDP    per_GDP\n",
       "ShangHai        2424  32680  13.481848\n",
       "HangZhou         981  13468  13.728848\n",
       "BeiJing         2154  30320  14.076137\n",
       "ShenZhen        1303  24222  18.589409"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "city_info.sort_values(by=\"per_GDP\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "递减排序"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {
    "scrolled": false
   },
   "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>population</th>\n",
       "      <th>GDP</th>\n",
       "      <th>per_GDP</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>ShenZhen</th>\n",
       "      <td>1303</td>\n",
       "      <td>24222</td>\n",
       "      <td>18.589409</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BeiJing</th>\n",
       "      <td>2154</td>\n",
       "      <td>30320</td>\n",
       "      <td>14.076137</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HangZhou</th>\n",
       "      <td>981</td>\n",
       "      <td>13468</td>\n",
       "      <td>13.728848</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ShangHai</th>\n",
       "      <td>2424</td>\n",
       "      <td>32680</td>\n",
       "      <td>13.481848</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          population    GDP    per_GDP\n",
       "ShenZhen        1303  24222  18.589409\n",
       "BeiJing         2154  30320  14.076137\n",
       "HangZhou         981  13468  13.728848\n",
       "ShangHai        2424  32680  13.481848"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "city_info.sort_values(by=\"per_GDP\", ascending=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**按轴进行排序**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {
    "scrolled": false
   },
   "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",
       "        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>C</th>\n",
       "      <th>B</th>\n",
       "      <th>A</th>\n",
       "      <th>D</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>13</td>\n",
       "      <td>17</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>19</td>\n",
       "      <td>14</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>11</td>\n",
       "      <td>7</td>\n",
       "      <td>14</td>\n",
       "      <td>2</td>\n",
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      "text/plain": [
       "    C   B   A  D\n",
       "2   3  13  17  8\n",
       "1   1  19  14  6\n",
       "0  11   7  14  2"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = pd.DataFrame(np.random.randint(20, size=(3, 4)), index=[2, 1, 0], columns=list(\"CBAD\"))\n",
    "data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "行排序"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th>C</th>\n",
       "      <th>B</th>\n",
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       "      <th>D</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>11</td>\n",
       "      <td>7</td>\n",
       "      <td>14</td>\n",
       "      <td>2</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>19</td>\n",
       "      <td>14</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>13</td>\n",
       "      <td>17</td>\n",
       "      <td>8</td>\n",
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      "text/plain": [
       "    C   B   A  D\n",
       "0  11   7  14  2\n",
       "1   1  19  14  6\n",
       "2   3  13  17  8"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.sort_index()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "列排序"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <td>17</td>\n",
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   "execution_count": 69,
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    "data.sort_index(axis=1, ascending=False)"
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   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* 统计方法"
   ]
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   "cell_type": "code",
   "execution_count": 70,
   "metadata": {
    "scrolled": true
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       "           A         B         C         D\n",
       "0   1.082198  3.557396 -3.060476  6.367969\n",
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     "metadata": {},
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   "source": [
    "df = pd.DataFrame(np.random.normal(2, 4, size=(6, 4)),columns=list(\"ABCD\"))\n",
    "df"
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  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "非空个数"
   ]
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  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "A    6\n",
       "B    6\n",
       "C    6\n",
       "D    6\n",
       "dtype: int64"
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     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
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    "df.count()"
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   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "求和"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "A    14.272224\n",
       "B     9.533497\n",
       "C     3.931385\n",
       "D    25.698741\n",
       "dtype: float64"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
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   "source": [
    "df.sum()"
   ]
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  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0     7.947086\n",
       "1    28.289408\n",
       "2    -1.427283\n",
       "3    -4.893571\n",
       "4    20.476806\n",
       "5     3.043402\n",
       "dtype: float64"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "df.sum(axis=1)"
   ]
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  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "最大值 最小值"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "A   -3.386712\n",
       "B   -4.333177\n",
       "C   -4.032613\n",
       "D   -0.152567\n",
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      ]
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     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
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   "source": [
    "df.min()"
   ]
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  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0     6.367969\n",
       "1    13.113252\n",
       "2     5.094802\n",
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     "execution_count": 75,
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     "output_type": "execute_result"
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   "execution_count": 77,
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       "           A         B         C         D\n",
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  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "A    1\n",
       "B    1\n",
       "C    4\n",
       "D    5\n",
       "dtype: int64"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
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    "df.idxmax()"
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  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "均值"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "A    2.378704\n",
       "B    1.588916\n",
       "C    0.655231\n",
       "D    4.283124\n",
       "dtype: float64"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "方差"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "A    34.980702\n",
       "B    19.110656\n",
       "C    18.948144\n",
       "D     6.726776\n",
       "dtype: float64"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.var()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "标准差"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "A    5.914449\n",
       "B    4.371574\n",
       "C    4.352947\n",
       "D    2.593603\n",
       "dtype: float64"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.std()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "中位数"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "A    1.126767\n",
       "B    1.531743\n",
       "C    0.176172\n",
       "D    5.273518\n",
       "dtype: float64"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.median()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "众数"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
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     "execution_count": 82,
     "metadata": {},
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    "data = pd.DataFrame(np.random.randint(5, size=(10, 2)), columns=list(\"AB\"))\n",
    "data"
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  {
   "cell_type": "code",
   "execution_count": 83,
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   "source": [
    "75%分位数"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "A    3.539202\n",
       "B    5.052594\n",
       "C    4.539740\n",
       "D    6.157738\n",
       "Name: 0.75, dtype: float64"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.quantile(0.75)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "一网打尽"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {
    "scrolled": true
   },
   "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>A</th>\n",
       "      <th>B</th>\n",
       "      <th>C</th>\n",
       "      <th>D</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>6.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>6.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>2.378704</td>\n",
       "      <td>1.588916</td>\n",
       "      <td>0.655231</td>\n",
       "      <td>4.283124</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>5.914449</td>\n",
       "      <td>4.371574</td>\n",
       "      <td>4.352947</td>\n",
       "      <td>2.593603</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>-3.386712</td>\n",
       "      <td>-4.333177</td>\n",
       "      <td>-4.032613</td>\n",
       "      <td>-0.152567</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>-1.256706</td>\n",
       "      <td>-1.265251</td>\n",
       "      <td>-2.925910</td>\n",
       "      <td>3.158284</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>1.126767</td>\n",
       "      <td>1.531743</td>\n",
       "      <td>0.176172</td>\n",
       "      <td>5.273518</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>3.539202</td>\n",
       "      <td>5.052594</td>\n",
       "      <td>4.539740</td>\n",
       "      <td>6.157738</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>13.113252</td>\n",
       "      <td>6.774559</td>\n",
       "      <td>5.577329</td>\n",
       "      <td>6.398588</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               A         B         C         D\n",
       "count   6.000000  6.000000  6.000000  6.000000\n",
       "mean    2.378704  1.588916  0.655231  4.283124\n",
       "std     5.914449  4.371574  4.352947  2.593603\n",
       "min    -3.386712 -4.333177 -4.032613 -0.152567\n",
       "25%    -1.256706 -1.265251 -2.925910  3.158284\n",
       "50%     1.126767  1.531743  0.176172  5.273518\n",
       "75%     3.539202  5.052594  4.539740  6.157738\n",
       "max    13.113252  6.774559  5.577329  6.398588"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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      "text/plain": [
       "   A  B  C  D\n",
       "0  a  a  c  d\n",
       "1  c  a  c  b\n",
       "2  a  a  d  c"
      ]
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     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "data_2 = pd.DataFrame([[\"a\", \"a\", \"c\", \"d\"],\n",
    "                       [\"c\", \"a\", \"c\", \"b\"],\n",
    "                       [\"a\", \"a\", \"d\", \"c\"]], columns=list(\"ABCD\"))\n",
    "data_2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>D</th>\n",
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       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>unique</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>top</th>\n",
       "      <td>a</td>\n",
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       "      <td>c</td>\n",
       "      <td>d</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>freq</th>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
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      "text/plain": [
       "        A  B  C  D\n",
       "count   3  3  3  3\n",
       "unique  2  1  2  3\n",
       "top     a  a  c  d\n",
       "freq    2  3  2  1"
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_2.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "相关性系数和协方差"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>A</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.831063</td>\n",
       "      <td>0.331060</td>\n",
       "      <td>0.510821</td>\n",
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       "    <tr>\n",
       "      <th>B</th>\n",
       "      <td>0.831063</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.179244</td>\n",
       "      <td>0.719112</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>C</th>\n",
       "      <td>0.331060</td>\n",
       "      <td>0.179244</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.450365</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>D</th>\n",
       "      <td>0.510821</td>\n",
       "      <td>0.719112</td>\n",
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       "      <td>1.000000</td>\n",
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       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          A         B         C         D\n",
       "A  1.000000  0.831063  0.331060  0.510821\n",
       "B  0.831063  1.000000  0.179244  0.719112\n",
       "C  0.331060  0.179244  1.000000 -0.450365\n",
       "D  0.510821  0.719112 -0.450365  1.000000"
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.corr()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "A    1.000000\n",
       "B    0.831063\n",
       "C    0.331060\n",
       "D    0.510821\n",
       "dtype: float64"
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.corrwith(df[\"A\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "自定义输出"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "apply（method）的用法：使用method方法默认对每一列进行相应的操作"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
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       "      <td>2.874553</td>\n",
       "      <td>5.527044</td>\n",
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       "      <th>2</th>\n",
       "      <td>-2.036341</td>\n",
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       "      <td>5.094802</td>\n",
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       "      <th>3</th>\n",
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       "      <td>2.537716</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4.328491</td>\n",
       "      <td>5.550994</td>\n",
       "      <td>5.577329</td>\n",
       "      <td>5.019991</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1.171336</td>\n",
       "      <td>-0.493910</td>\n",
       "      <td>-4.032613</td>\n",
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       "           A         B         C         D\n",
       "0   1.082198  3.557396 -3.060476  6.367969\n",
       "1  13.113252  6.774559  2.874553  5.527044\n",
       "2  -2.036341 -4.333177  5.094802 -0.152567\n",
       "3  -3.386712 -1.522365 -2.522209  2.537716\n",
       "4   4.328491  5.550994  5.577329  5.019991\n",
       "5   1.171336 -0.493910 -4.032613  6.398588"
      ]
     },
     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
    }
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   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [
    {
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       "      <th>1</th>\n",
       "      <td>14.195450</td>\n",
       "      <td>10.331955</td>\n",
       "      <td>-0.185923</td>\n",
       "      <td>11.895013</td>\n",
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       "      <th>2</th>\n",
       "      <td>12.159109</td>\n",
       "      <td>5.998778</td>\n",
       "      <td>4.908878</td>\n",
       "      <td>11.742447</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>8.772397</td>\n",
       "      <td>4.476413</td>\n",
       "      <td>2.386669</td>\n",
       "      <td>14.280162</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>13.100888</td>\n",
       "      <td>10.027406</td>\n",
       "      <td>7.963999</td>\n",
       "      <td>19.300153</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>14.272224</td>\n",
       "      <td>9.533497</td>\n",
       "      <td>3.931385</td>\n",
       "      <td>25.698741</td>\n",
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      ],
      "text/plain": [
       "           A          B         C          D\n",
       "0   1.082198   3.557396 -3.060476   6.367969\n",
       "1  14.195450  10.331955 -0.185923  11.895013\n",
       "2  12.159109   5.998778  4.908878  11.742447\n",
       "3   8.772397   4.476413  2.386669  14.280162\n",
       "4  13.100888  10.027406  7.963999  19.300153\n",
       "5  14.272224   9.533497  3.931385  25.698741"
      ]
     },
     "execution_count": 91,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.apply(np.cumsum)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-2.036341</td>\n",
       "      <td>-6.369518</td>\n",
       "      <td>-1.274717</td>\n",
       "      <td>-1.427283</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-3.386712</td>\n",
       "      <td>-4.909077</td>\n",
       "      <td>-7.431287</td>\n",
       "      <td>-4.893571</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4.328491</td>\n",
       "      <td>9.879485</td>\n",
       "      <td>15.456814</td>\n",
       "      <td>20.476806</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1.171336</td>\n",
       "      <td>0.677427</td>\n",
       "      <td>-3.355186</td>\n",
       "      <td>3.043402</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           A          B          C          D\n",
       "0   1.082198   4.639594   1.579117   7.947086\n",
       "1  13.113252  19.887811  22.762364  28.289408\n",
       "2  -2.036341  -6.369518  -1.274717  -1.427283\n",
       "3  -3.386712  -4.909077  -7.431287  -4.893571\n",
       "4   4.328491   9.879485  15.456814  20.476806\n",
       "5   1.171336   0.677427  -3.355186   3.043402"
      ]
     },
     "execution_count": 94,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.apply(np.cumsum, axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "A    14.272224\n",
       "B     9.533497\n",
       "C     3.931385\n",
       "D    25.698741\n",
       "dtype: float64"
      ]
     },
     "execution_count": 95,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.apply(sum)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "A    14.272224\n",
       "B     9.533497\n",
       "C     3.931385\n",
       "D    25.698741\n",
       "dtype: float64"
      ]
     },
     "execution_count": 96,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "A    16.499965\n",
       "B    11.107736\n",
       "C     9.609942\n",
       "D     6.551155\n",
       "dtype: float64"
      ]
     },
     "execution_count": 97,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.apply(lambda x: x.max()-x.min())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "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>A</th>\n",
       "      <th>B</th>\n",
       "      <th>C</th>\n",
       "      <th>D</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Count</th>\n",
       "      <td>6.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>6.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>2.378704</td>\n",
       "      <td>1.588916</td>\n",
       "      <td>0.655231</td>\n",
       "      <td>4.283124</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>13.113252</td>\n",
       "      <td>6.774559</td>\n",
       "      <td>5.577329</td>\n",
       "      <td>6.398588</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>idxmin</th>\n",
       "      <td>3.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>2.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>5.914449</td>\n",
       "      <td>4.371574</td>\n",
       "      <td>4.352947</td>\n",
       "      <td>2.593603</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                A         B         C         D\n",
       "Count    6.000000  6.000000  6.000000  6.000000\n",
       "mean     2.378704  1.588916  0.655231  4.283124\n",
       "max     13.113252  6.774559  5.577329  6.398588\n",
       "idxmin   3.000000  2.000000  5.000000  2.000000\n",
       "std      5.914449  4.371574  4.352947  2.593603"
      ]
     },
     "execution_count": 98,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def my_describe(x):\n",
    "    return pd.Series([x.count(), x.mean(), x.max(), x.idxmin(), x.std()], \\\n",
    "                     index=[\"Count\", \"mean\", \"max\", \"idxmin\", \"std\"])\n",
    "df.apply(my_describe)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 12.4 缺失值处理"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**1、发现缺失值**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "scrolled": false
   },
   "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>A</th>\n",
       "      <th>B</th>\n",
       "      <th>C</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>NaN</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     A    B     C\n",
       "0    1  NaN     2\n",
       "1  NaN    3     4\n",
       "2    5    6  None"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "data = pd.DataFrame(np.array([[1, np.nan, 2],\n",
    "                              [np.nan, 3, 4],\n",
    "                              [5, 6, None]]), columns=[\"A\", \"B\", \"C\"])\n",
    "data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**注意：有None、字符串等，数据类型全部变为object，它比int和float更消耗资源**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "A    object\n",
       "B    object\n",
       "C    object\n",
       "dtype: object"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "scrolled": true
   },
   "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>A</th>\n",
       "      <th>B</th>\n",
       "      <th>C</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       A      B      C\n",
       "0  False   True  False\n",
       "1   True  False  False\n",
       "2  False  False   True"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.isnull()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "scrolled": true
   },
   "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>A</th>\n",
       "      <th>B</th>\n",
       "      <th>C</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       A      B      C\n",
       "0   True  False   True\n",
       "1  False   True   True\n",
       "2   True   True  False"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.notnull()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**2、删除缺失值**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "scrolled": true
   },
   "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>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>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>NaN</td>\n",
       "      <td>4.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>6.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>7.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>9.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>10.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>13.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      A     B     C     D\n",
       "0   1.0   NaN   2.0   3.0\n",
       "1   NaN   4.0   5.0   6.0\n",
       "2   7.0   8.0   NaN   9.0\n",
       "3  10.0  11.0  12.0  13.0"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = pd.DataFrame(np.array([[1, np.nan, 2, 3],\n",
    "                              [np.nan, 4, 5, 6],\n",
    "                              [7, 8, np.nan, 9],\n",
    "                              [10, 11 , 12, 13]]), columns=[\"A\", \"B\", \"C\", \"D\"])\n",
    "data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**注意：np.nan是一种特殊的浮点数**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "A    float64\n",
       "B    float64\n",
       "C    float64\n",
       "D    float64\n",
       "dtype: object"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.dtypes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**（1）删除整行**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "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>A</th>\n",
       "      <th>B</th>\n",
       "      <th>C</th>\n",
       "      <th>D</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>10.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>13.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      A     B     C     D\n",
       "3  10.0  11.0  12.0  13.0"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.dropna()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**（2）删除整列**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "scrolled": true
   },
   "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>D</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>6.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>9.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>13.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      D\n",
       "0   3.0\n",
       "1   6.0\n",
       "2   9.0\n",
       "3  13.0"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.dropna(axis=\"columns\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
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       "      <th>2</th>\n",
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       "      A     B     C   D\n",
       "0   1.0   NaN   2.0 NaN\n",
       "1   NaN   4.0   5.0 NaN\n",
       "2   7.0   8.0   NaN NaN\n",
       "3  10.0  11.0  12.0 NaN"
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     "execution_count": 10,
     "metadata": {},
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    "data[\"D\"] = np.nan\n",
    "data"
   ]
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   "execution_count": 13,
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    "scrolled": true
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       "      A     B     C\n",
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   "execution_count": 14,
   "metadata": {
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    {
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       "     A    B    C   D\n",
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    "data.loc[3] = np.nan\n",
    "data"
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   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
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       "     A    B    C   D\n",
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     "metadata": {},
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    "data.dropna(how=\"all\")"
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  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**3、填充缺失值**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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       "      A     B     C     D\n",
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    "data = pd.DataFrame(np.array([[1, np.nan, 2, 3],\n",
    "                              [np.nan, 4, 5, 6],\n",
    "                              [7, 8, np.nan, 9],\n",
    "                              [10, 11 , 12, 13]]), columns=[\"A\", \"B\", \"C\", \"D\"])\n",
    "data"
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  {
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   "execution_count": 19,
   "metadata": {
    "scrolled": true
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    {
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      "text/plain": [
       "      A     B     C     D\n",
       "0   1.0   5.0   2.0   3.0\n",
       "1   5.0   4.0   5.0   6.0\n",
       "2   7.0   8.0   5.0   9.0\n",
       "3  10.0  11.0  12.0  13.0"
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     "execution_count": 19,
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   "source": [
    "data.fillna(value=5)"
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  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* 用均值进行替换"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "A    6.000000\n",
       "B    7.666667\n",
       "C    6.333333\n",
       "D    7.750000\n",
       "dtype: float64"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fill = data.mean()\n",
    "fill"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "scrolled": false
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   "outputs": [
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       "      A          B          C     D\n",
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   "execution_count": 27,
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   "outputs": [
    {
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       "7.0"
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     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
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    "fill = data.stack().mean()\n",
    "fill"
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  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
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       "      <td>7.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>9.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>10.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>13.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      A     B     C     D\n",
       "0   1.0   7.0   2.0   3.0\n",
       "1   7.0   4.0   5.0   6.0\n",
       "2   7.0   8.0   7.0   9.0\n",
       "3  10.0  11.0  12.0  13.0"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.fillna(value=fill)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 12.5 合并数据"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* 构造一个生产DataFrame的函数"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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       "      <td>A2</td>\n",
       "      <td>B2</td>\n",
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      "text/plain": [
       "    A   B   C\n",
       "0  A0  B0  C0\n",
       "1  A1  B1  C1\n",
       "2  A2  B2  C2"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "def make_df(cols, ind):\n",
    "    \"一个简单的DataFrame\"\n",
    "    data = {c: [str(c)+str(i) for i in ind]  for c in cols}\n",
    "    return pd.DataFrame(data, ind)\n",
    "\n",
    "make_df(\"ABC\", range(3))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* 垂直合并"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "    A   B\n",
      "1  A1  B1\n",
      "2  A2  B2\n",
      "    A   B\n",
      "3  A3  B3\n",
      "4  A4  B4\n"
     ]
    }
   ],
   "source": [
    "df_1 = make_df(\"AB\", [1, 2])\n",
    "df_2 = make_df(\"AB\", [3, 4])\n",
    "print(df_1)\n",
    "print(df_2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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       "<div>\n",
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       "    A   B\n",
       "1  A1  B1\n",
       "2  A2  B2\n",
       "3  A3  B3\n",
       "4  A4  B4"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
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   "source": [
    "pd.concat([df_1, df_2])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* 水平合并"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "    A   B\n",
      "0  A0  B0\n",
      "1  A1  B1\n",
      "    C   D\n",
      "0  C0  D0\n",
      "1  C1  D1\n"
     ]
    }
   ],
   "source": [
    "df_3 = make_df(\"AB\", [0, 1])\n",
    "df_4 = make_df(\"CD\", [0, 1])\n",
    "print(df_3)\n",
    "print(df_4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
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       "    A   B   C   D\n",
       "0  A0  B0  C0  D0\n",
       "1  A1  B1  C1  D1"
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     "execution_count": 51,
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   ],
   "source": [
    "pd.concat([df_3, df_4], axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* 索引重叠"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "行重叠"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "    A   B\n",
      "1  A1  B1\n",
      "2  A2  B2\n",
      "    A   B\n",
      "1  A1  B1\n",
      "2  A2  B2\n"
     ]
    }
   ],
   "source": [
    "df_5 = make_df(\"AB\", [1, 2])\n",
    "df_6 = make_df(\"AB\", [1, 2])\n",
    "print(df_5)\n",
    "print(df_6)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
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       "    A   B\n",
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    "pd.concat([df_5, df_6])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
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     "metadata": {},
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   "source": [
    "pd.concat([df_5, df_6],ignore_index=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "列重叠"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "    A   B   C\n",
      "1  A1  B1  C1\n",
      "2  A2  B2  C2\n",
      "    B   C   D\n",
      "1  B1  C1  D1\n",
      "2  B2  C2  D2\n"
     ]
    }
   ],
   "source": [
    "df_7 = make_df(\"ABC\", [1, 2])\n",
    "df_8 = make_df(\"BCD\", [1, 2])\n",
    "print(df_7)\n",
    "print(df_8)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
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       "      <td>C1</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
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       "      <td>C2</td>\n",
       "      <td>B2</td>\n",
       "      <td>C2</td>\n",
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       "    A   B   C   B   C   D\n",
       "1  A1  B1  C1  B1  C1  D1\n",
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     "execution_count": 11,
     "metadata": {},
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   "source": [
    "pd.concat([df_7, df_8], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
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       "    0   1   2   3   4   5\n",
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     "execution_count": 12,
     "metadata": {},
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   "source": [
    "pd.concat([df_7, df_8],axis=1, ignore_index=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* 对齐合并merge()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "    A   B\n",
      "1  A1  B1\n",
      "2  A2  B2\n",
      "    B   C\n",
      "1  B1  C1\n",
      "2  B2  C2\n"
     ]
    }
   ],
   "source": [
    "df_9 = make_df(\"AB\", [1, 2])\n",
    "df_10 = make_df(\"BC\", [1, 2])\n",
    "print(df_9)\n",
    "print(df_10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
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       "      <td>B2</td>\n",
       "      <td>C2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    A   B   C\n",
       "0  A1  B1  C1\n",
       "1  A2  B2  C2"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.merge(df_9, df_10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "    A   B\n",
      "1  A1  B1\n",
      "2  A2  B2\n",
      "    C   B\n",
      "2  C2  B2\n",
      "1  C1  B1\n"
     ]
    }
   ],
   "source": [
    "df_9 = make_df(\"AB\", [1, 2])\n",
    "df_10 = make_df(\"CB\", [2, 1])\n",
    "print(df_9)\n",
    "print(df_10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {
    "scrolled": false
   },
   "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",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\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>A</th>\n",
       "      <th>B</th>\n",
       "      <th>C</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>A1</td>\n",
       "      <td>B1</td>\n",
       "      <td>C1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>A2</td>\n",
       "      <td>B2</td>\n",
       "      <td>C2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    A   B   C\n",
       "0  A1  B1  C1\n",
       "1  A2  B2  C2"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.merge(df_9, df_10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "【例】 合并城市信息"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "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>city</th>\n",
       "      <th>pop</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>BeiJing</td>\n",
       "      <td>2154</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>HangZhou</td>\n",
       "      <td>981</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>ShenZhen</td>\n",
       "      <td>1303</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       city   pop\n",
       "0   BeiJing  2154\n",
       "1  HangZhou   981\n",
       "2  ShenZhen  1303"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "population_dict = {\"city\": (\"BeiJing\", \"HangZhou\", \"ShenZhen\"),\n",
    "                   \"pop\": (2154, 981, 1303)}\n",
    "population = pd.DataFrame(population_dict)\n",
    "population"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "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>city</th>\n",
       "      <th>GDP</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>BeiJing</td>\n",
       "      <td>30320</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>ShangHai</td>\n",
       "      <td>32680</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>HangZhou</td>\n",
       "      <td>13468</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       city    GDP\n",
       "0   BeiJing  30320\n",
       "1  ShangHai  32680\n",
       "2  HangZhou  13468"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "GDP_dict = {\"city\": (\"BeiJing\", \"ShangHai\", \"HangZhou\"),\n",
    "            \"GDP\": (30320, 32680, 13468)}\n",
    "GDP = pd.DataFrame(GDP_dict)\n",
    "GDP"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {
    "scrolled": true
   },
   "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>city</th>\n",
       "      <th>pop</th>\n",
       "      <th>GDP</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>BeiJing</td>\n",
       "      <td>2154</td>\n",
       "      <td>30320</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>HangZhou</td>\n",
       "      <td>981</td>\n",
       "      <td>13468</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       city   pop    GDP\n",
       "0   BeiJing  2154  30320\n",
       "1  HangZhou   981  13468"
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "city_info = pd.merge(population, GDP)\n",
    "city_info"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {
    "scrolled": true
   },
   "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>city</th>\n",
       "      <th>pop</th>\n",
       "      <th>GDP</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>BeiJing</td>\n",
       "      <td>2154.0</td>\n",
       "      <td>30320.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>HangZhou</td>\n",
       "      <td>981.0</td>\n",
       "      <td>13468.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>ShenZhen</td>\n",
       "      <td>1303.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>ShangHai</td>\n",
       "      <td>NaN</td>\n",
       "      <td>32680.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       city     pop      GDP\n",
       "0   BeiJing  2154.0  30320.0\n",
       "1  HangZhou   981.0  13468.0\n",
       "2  ShenZhen  1303.0      NaN\n",
       "3  ShangHai     NaN  32680.0"
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "city_info = pd.merge(population, GDP, how=\"outer\")\n",
    "city_info"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 12.6 分组和数据透视表"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {
    "scrolled": false
   },
   "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>key</th>\n",
       "      <th>data1</th>\n",
       "      <th>data2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>A</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>B</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>C</td>\n",
       "      <td>2</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>C</td>\n",
       "      <td>3</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>B</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>A</td>\n",
       "      <td>5</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  key  data1  data2\n",
       "0   A      0      1\n",
       "1   B      1      4\n",
       "2   C      2      9\n",
       "3   C      3      9\n",
       "4   B      4      1\n",
       "5   A      5      9"
      ]
     },
     "execution_count": 103,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.DataFrame({\"key\":[\"A\", \"B\", \"C\", \"C\", \"B\", \"A\"],\n",
    "                  \"data1\": range(6),\n",
    "                  \"data2\": np.random.randint(0, 10, size=6)})\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**（1）分组**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* 延迟计算"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<pandas.core.groupby.generic.DataFrameGroupBy object at 0x000002276795A240>"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby(\"key\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "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>data1</th>\n",
       "      <th>data2</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>key</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>A</th>\n",
       "      <td>5</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>B</th>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>C</th>\n",
       "      <td>5</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     data1  data2\n",
       "key              \n",
       "A        5     10\n",
       "B        5      6\n",
       "C        5     11"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby(\"key\").sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "scrolled": false
   },
   "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>data1</th>\n",
       "      <th>data2</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>key</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>A</th>\n",
       "      <td>2.5</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>B</th>\n",
       "      <td>2.5</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>C</th>\n",
       "      <td>2.5</td>\n",
       "      <td>5.5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     data1  data2\n",
       "key              \n",
       "A      2.5    5.0\n",
       "B      2.5    3.0\n",
       "C      2.5    5.5"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby(\"key\").mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "('A',   key  data1  data2\n",
      "0   A      0      2\n",
      "5   A      5      8)\n",
      "('B',   key  data1  data2\n",
      "1   B      1      2\n",
      "4   B      4      4)\n",
      "('C',   key  data1  data2\n",
      "2   C      2      8\n",
      "3   C      3      3)\n"
     ]
    }
   ],
   "source": [
    "for i in df.groupby(\"key\"):\n",
    "    print(str(i))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* 按列取值"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "key\n",
       "A    10\n",
       "B     6\n",
       "C    11\n",
       "Name: data2, dtype: int32"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby(\"key\")[\"data2\"].sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* 按组迭代"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "A     shape=(2, 3)\n",
      "B     shape=(2, 3)\n",
      "C     shape=(2, 3)\n"
     ]
    }
   ],
   "source": [
    "for data, group in df.groupby(\"key\"):\n",
    "    print(\"{0:5} shape={1}\".format(data, group.shape))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* 调用方法"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "scrolled": true
   },
   "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>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>25%</th>\n",
       "      <th>50%</th>\n",
       "      <th>75%</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>key</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>A</th>\n",
       "      <td>2.0</td>\n",
       "      <td>2.5</td>\n",
       "      <td>3.535534</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.25</td>\n",
       "      <td>2.5</td>\n",
       "      <td>3.75</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>B</th>\n",
       "      <td>2.0</td>\n",
       "      <td>2.5</td>\n",
       "      <td>2.121320</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.75</td>\n",
       "      <td>2.5</td>\n",
       "      <td>3.25</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>C</th>\n",
       "      <td>2.0</td>\n",
       "      <td>2.5</td>\n",
       "      <td>0.707107</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.25</td>\n",
       "      <td>2.5</td>\n",
       "      <td>2.75</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     count  mean       std  min   25%  50%   75%  max\n",
       "key                                                  \n",
       "A      2.0   2.5  3.535534  0.0  1.25  2.5  3.75  5.0\n",
       "B      2.0   2.5  2.121320  1.0  1.75  2.5  3.25  4.0\n",
       "C      2.0   2.5  0.707107  2.0  2.25  2.5  2.75  3.0"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby(\"key\")[\"data1\"].describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* 支持更复杂的操作"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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       "      <th>key</th>\n",
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       "      <th></th>\n",
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       "  <tbody>\n",
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       "      <th>A</th>\n",
       "      <td>0</td>\n",
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       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>5.0</td>\n",
       "      <td>8</td>\n",
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       "    <tr>\n",
       "      <th>B</th>\n",
       "      <td>1</td>\n",
       "      <td>2.5</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>C</th>\n",
       "      <td>2</td>\n",
       "      <td>2.5</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>5.5</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    data1            data2           \n",
       "      min median max   min median max\n",
       "key                                  \n",
       "A       0    2.5   5     2    5.0   8\n",
       "B       1    2.5   4     2    3.0   4\n",
       "C       2    2.5   3     3    5.5   8"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby(\"key\").aggregate([\"min\", \"median\", \"max\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* 过滤"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "key\n",
       "A    4.242641\n",
       "B    1.414214\n",
       "C    3.535534\n",
       "Name: data2, dtype: float64"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def filter_func(x):\n",
    "    return x[\"data2\"].std() > 3\n",
    "df.groupby(\"key\")[\"data2\"].std()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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      "text/plain": [
       "  key  data1  data2\n",
       "0   A      0      2\n",
       "2   C      2      8\n",
       "3   C      3      3\n",
       "5   A      5      8"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby(\"key\").filter(filter_func)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* 转换"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "  key  data1  data2\n",
       "0   A      0      2\n",
       "1   B      1      2\n",
       "2   C      2      8\n",
       "3   C      3      3\n",
       "4   B      4      4\n",
       "5   A      5      8"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
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   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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      ],
      "text/plain": [
       "   data1  data2\n",
       "0   -2.5   -3.0\n",
       "1   -1.5   -1.0\n",
       "2   -0.5    2.5\n",
       "3    0.5   -2.5\n",
       "4    1.5    1.0\n",
       "5    2.5    3.0"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby(\"key\").transform(lambda x: x-x.mean())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>2</th>\n",
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       "      <td>9</td>\n",
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       "      <th>3</th>\n",
       "      <td>C</td>\n",
       "      <td>3</td>\n",
       "      <td>9</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>B</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>A</td>\n",
       "      <td>5</td>\n",
       "      <td>9</td>\n",
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       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  key  data1  data2\n",
       "0   A      0      1\n",
       "1   B      1      4\n",
       "2   C      2      9\n",
       "3   C      3      9\n",
       "4   B      4      1\n",
       "5   A      5      9"
      ]
     },
     "execution_count": 104,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>-0.5</td>\n",
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       "      <th>3</th>\n",
       "      <td>0.5</td>\n",
       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1.5</td>\n",
       "      <td>-1.5</td>\n",
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       "      <th>5</th>\n",
       "      <td>2.5</td>\n",
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      ],
      "text/plain": [
       "   data1  data2\n",
       "0   -2.5   -4.0\n",
       "1   -1.5    1.5\n",
       "2   -0.5    0.0\n",
       "3    0.5    0.0\n",
       "4    1.5   -1.5\n",
       "5    2.5    4.0"
      ]
     },
     "execution_count": 105,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby(\"key\").apply(lambda x: x-x.mean())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* apply（）方法"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>1</th>\n",
       "      <td>B</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>C</td>\n",
       "      <td>2</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>C</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>B</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>A</td>\n",
       "      <td>5</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  key  data1  data2\n",
       "0   A      0      2\n",
       "1   B      1      2\n",
       "2   C      2      8\n",
       "3   C      3      3\n",
       "4   B      4      4\n",
       "5   A      5      8"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "def norm_by_data2(x):\n",
    "    x[\"data1\"] /= x[\"data2\"].sum()\n",
    "    return x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th>1</th>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>C</td>\n",
       "      <td>0.181818</td>\n",
       "      <td>8</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>C</td>\n",
       "      <td>0.272727</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>B</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>A</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  key     data1  data2\n",
       "0   A  0.000000      2\n",
       "1   B  0.166667      2\n",
       "2   C  0.181818      8\n",
       "3   C  0.272727      3\n",
       "4   B  0.666667      4\n",
       "5   A  0.500000      8"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby(\"key\").apply(norm_by_data2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* 将列表、数组设为分组键"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th>data1</th>\n",
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       "      <th>2</th>\n",
       "      <td>C</td>\n",
       "      <td>2</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>C</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>B</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
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       "      <th>5</th>\n",
       "      <td>A</td>\n",
       "      <td>5</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  key  data1  data2\n",
       "0   A      0      2\n",
       "1   B      1      2\n",
       "2   C      2      8\n",
       "3   C      3      3\n",
       "4   B      4      4\n",
       "5   A      5      8"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "L = [0, 1, 0, 1, 2, 0]\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "      <th>data2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
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       "      <th>0</th>\n",
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      ],
      "text/plain": [
       "   data1  data2\n",
       "0      7     18\n",
       "1      4      5\n",
       "2      4      4"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby(L).sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* 用字典将索引映射到分组"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th>key</th>\n",
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       "      <th>C</th>\n",
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       "      <th>C</th>\n",
       "      <td>3</td>\n",
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       "      <th>B</th>\n",
       "      <td>4</td>\n",
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       "      <th>A</th>\n",
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      ],
      "text/plain": [
       "     data1  data2\n",
       "key              \n",
       "A        0      2\n",
       "B        1      2\n",
       "C        2      8\n",
       "C        3      3\n",
       "B        4      4\n",
       "A        5      8"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2 = df.set_index(\"key\")\n",
    "df2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    <tr>\n",
       "      <th>constant</th>\n",
       "      <td>10</td>\n",
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       "      <th>first</th>\n",
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       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          data1  data2\n",
       "constant     10     17\n",
       "first         5     10"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mapping = {\"A\": \"first\", \"B\": \"constant\", \"C\": \"constant\"}\n",
    "df2.groupby(mapping).sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* 任意Python函数"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "      <th>data2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>a</th>\n",
       "      <td>2.5</td>\n",
       "      <td>5.0</td>\n",
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       "    <tr>\n",
       "      <th>b</th>\n",
       "      <td>2.5</td>\n",
       "      <td>3.0</td>\n",
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       "    <tr>\n",
       "      <th>c</th>\n",
       "      <td>2.5</td>\n",
       "      <td>5.5</td>\n",
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       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   data1  data2\n",
       "a    2.5    5.0\n",
       "b    2.5    3.0\n",
       "c    2.5    5.5"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2.groupby(str.lower).mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* 多个有效值组成的列表"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>a</th>\n",
       "      <th>first</th>\n",
       "      <td>2.5</td>\n",
       "      <td>5.0</td>\n",
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       "    <tr>\n",
       "      <th>b</th>\n",
       "      <th>constant</th>\n",
       "      <td>2.5</td>\n",
       "      <td>3.0</td>\n",
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       "    <tr>\n",
       "      <th>c</th>\n",
       "      <th>constant</th>\n",
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      ],
      "text/plain": [
       "            data1  data2\n",
       "a first       2.5    5.0\n",
       "b constant    2.5    3.0\n",
       "c constant    2.5    5.5"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2.groupby([str.lower, mapping]).mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "【例1】 行星观测数据处理"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "import seaborn as sns\n",
    "\n",
    "planets = sns.load_dataset(\"planets\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1035, 6)"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "planets.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th></th>\n",
       "      <th>method</th>\n",
       "      <th>number</th>\n",
       "      <th>orbital_period</th>\n",
       "      <th>mass</th>\n",
       "      <th>distance</th>\n",
       "      <th>year</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Radial Velocity</td>\n",
       "      <td>1</td>\n",
       "      <td>269.300</td>\n",
       "      <td>7.10</td>\n",
       "      <td>77.40</td>\n",
       "      <td>2006</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Radial Velocity</td>\n",
       "      <td>1</td>\n",
       "      <td>874.774</td>\n",
       "      <td>2.21</td>\n",
       "      <td>56.95</td>\n",
       "      <td>2008</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Radial Velocity</td>\n",
       "      <td>1</td>\n",
       "      <td>763.000</td>\n",
       "      <td>2.60</td>\n",
       "      <td>19.84</td>\n",
       "      <td>2011</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Radial Velocity</td>\n",
       "      <td>1</td>\n",
       "      <td>326.030</td>\n",
       "      <td>19.40</td>\n",
       "      <td>110.62</td>\n",
       "      <td>2007</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Radial Velocity</td>\n",
       "      <td>1</td>\n",
       "      <td>516.220</td>\n",
       "      <td>10.50</td>\n",
       "      <td>119.47</td>\n",
       "      <td>2009</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            method  number  orbital_period   mass  distance  year\n",
       "0  Radial Velocity       1         269.300   7.10     77.40  2006\n",
       "1  Radial Velocity       1         874.774   2.21     56.95  2008\n",
       "2  Radial Velocity       1         763.000   2.60     19.84  2011\n",
       "3  Radial Velocity       1         326.030  19.40    110.62  2007\n",
       "4  Radial Velocity       1         516.220  10.50    119.47  2009"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "planets.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "scrolled": false
   },
   "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",
       "\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>number</th>\n",
       "      <th>orbital_period</th>\n",
       "      <th>mass</th>\n",
       "      <th>distance</th>\n",
       "      <th>year</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>1035.000000</td>\n",
       "      <td>992.000000</td>\n",
       "      <td>513.000000</td>\n",
       "      <td>808.000000</td>\n",
       "      <td>1035.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>1.785507</td>\n",
       "      <td>2002.917596</td>\n",
       "      <td>2.638161</td>\n",
       "      <td>264.069282</td>\n",
       "      <td>2009.070531</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>1.240976</td>\n",
       "      <td>26014.728304</td>\n",
       "      <td>3.818617</td>\n",
       "      <td>733.116493</td>\n",
       "      <td>3.972567</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.090706</td>\n",
       "      <td>0.003600</td>\n",
       "      <td>1.350000</td>\n",
       "      <td>1989.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>5.442540</td>\n",
       "      <td>0.229000</td>\n",
       "      <td>32.560000</td>\n",
       "      <td>2007.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>39.979500</td>\n",
       "      <td>1.260000</td>\n",
       "      <td>55.250000</td>\n",
       "      <td>2010.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>2.000000</td>\n",
       "      <td>526.005000</td>\n",
       "      <td>3.040000</td>\n",
       "      <td>178.500000</td>\n",
       "      <td>2012.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>7.000000</td>\n",
       "      <td>730000.000000</td>\n",
       "      <td>25.000000</td>\n",
       "      <td>8500.000000</td>\n",
       "      <td>2014.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            number  orbital_period        mass     distance         year\n",
       "count  1035.000000      992.000000  513.000000   808.000000  1035.000000\n",
       "mean      1.785507     2002.917596    2.638161   264.069282  2009.070531\n",
       "std       1.240976    26014.728304    3.818617   733.116493     3.972567\n",
       "min       1.000000        0.090706    0.003600     1.350000  1989.000000\n",
       "25%       1.000000        5.442540    0.229000    32.560000  2007.000000\n",
       "50%       1.000000       39.979500    1.260000    55.250000  2010.000000\n",
       "75%       2.000000      526.005000    3.040000   178.500000  2012.000000\n",
       "max       7.000000   730000.000000   25.000000  8500.000000  2014.000000"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "planets.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "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",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>method</th>\n",
       "      <th>number</th>\n",
       "      <th>orbital_period</th>\n",
       "      <th>mass</th>\n",
       "      <th>distance</th>\n",
       "      <th>year</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Radial Velocity</td>\n",
       "      <td>1</td>\n",
       "      <td>269.300</td>\n",
       "      <td>7.10</td>\n",
       "      <td>77.40</td>\n",
       "      <td>2006</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Radial Velocity</td>\n",
       "      <td>1</td>\n",
       "      <td>874.774</td>\n",
       "      <td>2.21</td>\n",
       "      <td>56.95</td>\n",
       "      <td>2008</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Radial Velocity</td>\n",
       "      <td>1</td>\n",
       "      <td>763.000</td>\n",
       "      <td>2.60</td>\n",
       "      <td>19.84</td>\n",
       "      <td>2011</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Radial Velocity</td>\n",
       "      <td>1</td>\n",
       "      <td>326.030</td>\n",
       "      <td>19.40</td>\n",
       "      <td>110.62</td>\n",
       "      <td>2007</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Radial Velocity</td>\n",
       "      <td>1</td>\n",
       "      <td>516.220</td>\n",
       "      <td>10.50</td>\n",
       "      <td>119.47</td>\n",
       "      <td>2009</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            method  number  orbital_period   mass  distance  year\n",
       "0  Radial Velocity       1         269.300   7.10     77.40  2006\n",
       "1  Radial Velocity       1         874.774   2.21     56.95  2008\n",
       "2  Radial Velocity       1         763.000   2.60     19.84  2011\n",
       "3  Radial Velocity       1         326.030  19.40    110.62  2007\n",
       "4  Radial Velocity       1         516.220  10.50    119.47  2009"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "planets.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    2000\n",
       "1    2000\n",
       "2    2010\n",
       "3    2000\n",
       "4    2000\n",
       "Name: year, dtype: int64"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "decade = 10 * (planets[\"year\"] // 10)\n",
    "decade.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    2000s\n",
       "1    2000s\n",
       "2    2010s\n",
       "3    2000s\n",
       "4    2000s\n",
       "Name: decade, dtype: object"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "decade = decade.astype(str) + \"s\"\n",
    "decade.name = \"decade\"\n",
    "decade.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "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>method</th>\n",
       "      <th>number</th>\n",
       "      <th>orbital_period</th>\n",
       "      <th>mass</th>\n",
       "      <th>distance</th>\n",
       "      <th>year</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Radial Velocity</td>\n",
       "      <td>1</td>\n",
       "      <td>269.300</td>\n",
       "      <td>7.10</td>\n",
       "      <td>77.40</td>\n",
       "      <td>2006</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Radial Velocity</td>\n",
       "      <td>1</td>\n",
       "      <td>874.774</td>\n",
       "      <td>2.21</td>\n",
       "      <td>56.95</td>\n",
       "      <td>2008</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Radial Velocity</td>\n",
       "      <td>1</td>\n",
       "      <td>763.000</td>\n",
       "      <td>2.60</td>\n",
       "      <td>19.84</td>\n",
       "      <td>2011</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Radial Velocity</td>\n",
       "      <td>1</td>\n",
       "      <td>326.030</td>\n",
       "      <td>19.40</td>\n",
       "      <td>110.62</td>\n",
       "      <td>2007</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Radial Velocity</td>\n",
       "      <td>1</td>\n",
       "      <td>516.220</td>\n",
       "      <td>10.50</td>\n",
       "      <td>119.47</td>\n",
       "      <td>2009</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            method  number  orbital_period   mass  distance  year\n",
       "0  Radial Velocity       1         269.300   7.10     77.40  2006\n",
       "1  Radial Velocity       1         874.774   2.21     56.95  2008\n",
       "2  Radial Velocity       1         763.000   2.60     19.84  2011\n",
       "3  Radial Velocity       1         326.030  19.40    110.62  2007\n",
       "4  Radial Velocity       1         516.220  10.50    119.47  2009"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "planets.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {
    "scrolled": true
   },
   "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></th>\n",
       "      <th>number</th>\n",
       "      <th>orbital_period</th>\n",
       "      <th>mass</th>\n",
       "      <th>distance</th>\n",
       "      <th>year</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>method</th>\n",
       "      <th>decade</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Astrometry</th>\n",
       "      <th>2010s</th>\n",
       "      <td>2</td>\n",
       "      <td>1.262360e+03</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>35.75</td>\n",
       "      <td>4023</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">Eclipse Timing Variations</th>\n",
       "      <th>2000s</th>\n",
       "      <td>5</td>\n",
       "      <td>1.930800e+04</td>\n",
       "      <td>6.05000</td>\n",
       "      <td>261.44</td>\n",
       "      <td>6025</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010s</th>\n",
       "      <td>10</td>\n",
       "      <td>2.345680e+04</td>\n",
       "      <td>4.20000</td>\n",
       "      <td>1000.00</td>\n",
       "      <td>12065</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">Imaging</th>\n",
       "      <th>2000s</th>\n",
       "      <td>29</td>\n",
       "      <td>1.350935e+06</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>956.83</td>\n",
       "      <td>40139</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010s</th>\n",
       "      <td>21</td>\n",
       "      <td>6.803750e+04</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>1210.08</td>\n",
       "      <td>36208</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">Microlensing</th>\n",
       "      <th>2000s</th>\n",
       "      <td>12</td>\n",
       "      <td>1.732500e+04</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.00</td>\n",
       "      <td>20070</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010s</th>\n",
       "      <td>15</td>\n",
       "      <td>4.750000e+03</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>41440.00</td>\n",
       "      <td>26155</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Orbital Brightness Modulation</th>\n",
       "      <th>2010s</th>\n",
       "      <td>5</td>\n",
       "      <td>2.127920e+00</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>2360.00</td>\n",
       "      <td>6035</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">Pulsar Timing</th>\n",
       "      <th>1990s</th>\n",
       "      <td>9</td>\n",
       "      <td>1.900153e+02</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.00</td>\n",
       "      <td>5978</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000s</th>\n",
       "      <td>1</td>\n",
       "      <td>3.652500e+04</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.00</td>\n",
       "      <td>2003</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010s</th>\n",
       "      <td>1</td>\n",
       "      <td>9.070629e-02</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>1200.00</td>\n",
       "      <td>2011</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pulsation Timing Variations</th>\n",
       "      <th>2000s</th>\n",
       "      <td>1</td>\n",
       "      <td>1.170000e+03</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.00</td>\n",
       "      <td>2007</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"4\" valign=\"top\">Radial Velocity</th>\n",
       "      <th>1980s</th>\n",
       "      <td>1</td>\n",
       "      <td>8.388800e+01</td>\n",
       "      <td>11.68000</td>\n",
       "      <td>40.57</td>\n",
       "      <td>1989</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1990s</th>\n",
       "      <td>52</td>\n",
       "      <td>1.091561e+04</td>\n",
       "      <td>68.17820</td>\n",
       "      <td>723.71</td>\n",
       "      <td>55943</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000s</th>\n",
       "      <td>475</td>\n",
       "      <td>2.633526e+05</td>\n",
       "      <td>945.31928</td>\n",
       "      <td>15201.16</td>\n",
       "      <td>619775</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010s</th>\n",
       "      <td>424</td>\n",
       "      <td>1.809630e+05</td>\n",
       "      <td>316.47890</td>\n",
       "      <td>11382.67</td>\n",
       "      <td>432451</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">Transit</th>\n",
       "      <th>2000s</th>\n",
       "      <td>64</td>\n",
       "      <td>2.897102e+02</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>31823.31</td>\n",
       "      <td>124462</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010s</th>\n",
       "      <td>712</td>\n",
       "      <td>8.087813e+03</td>\n",
       "      <td>1.47000</td>\n",
       "      <td>102419.46</td>\n",
       "      <td>673999</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Transit Timing Variations</th>\n",
       "      <th>2010s</th>\n",
       "      <td>9</td>\n",
       "      <td>2.393505e+02</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>3313.00</td>\n",
       "      <td>8050</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                      number  orbital_period       mass  \\\n",
       "method                        decade                                      \n",
       "Astrometry                    2010s        2    1.262360e+03    0.00000   \n",
       "Eclipse Timing Variations     2000s        5    1.930800e+04    6.05000   \n",
       "                              2010s       10    2.345680e+04    4.20000   \n",
       "Imaging                       2000s       29    1.350935e+06    0.00000   \n",
       "                              2010s       21    6.803750e+04    0.00000   \n",
       "Microlensing                  2000s       12    1.732500e+04    0.00000   \n",
       "                              2010s       15    4.750000e+03    0.00000   \n",
       "Orbital Brightness Modulation 2010s        5    2.127920e+00    0.00000   \n",
       "Pulsar Timing                 1990s        9    1.900153e+02    0.00000   \n",
       "                              2000s        1    3.652500e+04    0.00000   \n",
       "                              2010s        1    9.070629e-02    0.00000   \n",
       "Pulsation Timing Variations   2000s        1    1.170000e+03    0.00000   \n",
       "Radial Velocity               1980s        1    8.388800e+01   11.68000   \n",
       "                              1990s       52    1.091561e+04   68.17820   \n",
       "                              2000s      475    2.633526e+05  945.31928   \n",
       "                              2010s      424    1.809630e+05  316.47890   \n",
       "Transit                       2000s       64    2.897102e+02    0.00000   \n",
       "                              2010s      712    8.087813e+03    1.47000   \n",
       "Transit Timing Variations     2010s        9    2.393505e+02    0.00000   \n",
       "\n",
       "                                       distance    year  \n",
       "method                        decade                     \n",
       "Astrometry                    2010s       35.75    4023  \n",
       "Eclipse Timing Variations     2000s      261.44    6025  \n",
       "                              2010s     1000.00   12065  \n",
       "Imaging                       2000s      956.83   40139  \n",
       "                              2010s     1210.08   36208  \n",
       "Microlensing                  2000s        0.00   20070  \n",
       "                              2010s    41440.00   26155  \n",
       "Orbital Brightness Modulation 2010s     2360.00    6035  \n",
       "Pulsar Timing                 1990s        0.00    5978  \n",
       "                              2000s        0.00    2003  \n",
       "                              2010s     1200.00    2011  \n",
       "Pulsation Timing Variations   2000s        0.00    2007  \n",
       "Radial Velocity               1980s       40.57    1989  \n",
       "                              1990s      723.71   55943  \n",
       "                              2000s    15201.16  619775  \n",
       "                              2010s    11382.67  432451  \n",
       "Transit                       2000s    31823.31  124462  \n",
       "                              2010s   102419.46  673999  \n",
       "Transit Timing Variations     2010s     3313.00    8050  "
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "planets.groupby([\"method\", decade]).sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\n",
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       "\n",
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       "\n",
       "    .dataframe thead tr:last-of-type th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th colspan=\"4\" halign=\"left\">number</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>decade</th>\n",
       "      <th>1980s</th>\n",
       "      <th>1990s</th>\n",
       "      <th>2000s</th>\n",
       "      <th>2010s</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>method</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Astrometry</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Eclipse Timing Variations</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>10.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Imaging</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>29.0</td>\n",
       "      <td>21.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Microlensing</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>15.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Orbital Brightness Modulation</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pulsar Timing</th>\n",
       "      <td>0.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pulsation Timing Variations</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Radial Velocity</th>\n",
       "      <td>1.0</td>\n",
       "      <td>52.0</td>\n",
       "      <td>475.0</td>\n",
       "      <td>424.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Transit</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>64.0</td>\n",
       "      <td>712.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Transit Timing Variations</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>9.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                              number                    \n",
       "decade                         1980s 1990s  2000s  2010s\n",
       "method                                                  \n",
       "Astrometry                       0.0   0.0    0.0    2.0\n",
       "Eclipse Timing Variations        0.0   0.0    5.0   10.0\n",
       "Imaging                          0.0   0.0   29.0   21.0\n",
       "Microlensing                     0.0   0.0   12.0   15.0\n",
       "Orbital Brightness Modulation    0.0   0.0    0.0    5.0\n",
       "Pulsar Timing                    0.0   9.0    1.0    1.0\n",
       "Pulsation Timing Variations      0.0   0.0    1.0    0.0\n",
       "Radial Velocity                  1.0  52.0  475.0  424.0\n",
       "Transit                          0.0   0.0   64.0  712.0\n",
       "Transit Timing Variations        0.0   0.0    0.0    9.0"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "planets.groupby([\"method\", decade])[[\"number\"]].sum().unstack().fillna(0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**（2）数据透视表**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "【例2】泰坦尼克号乘客数据分析"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "import seaborn as sns\n",
    "\n",
    "titanic = sns.load_dataset(\"titanic\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "        vertical-align: middle;\n",
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       "\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>survived</th>\n",
       "      <th>pclass</th>\n",
       "      <th>sex</th>\n",
       "      <th>age</th>\n",
       "      <th>sibsp</th>\n",
       "      <th>parch</th>\n",
       "      <th>fare</th>\n",
       "      <th>embarked</th>\n",
       "      <th>class</th>\n",
       "      <th>who</th>\n",
       "      <th>adult_male</th>\n",
       "      <th>deck</th>\n",
       "      <th>embark_town</th>\n",
       "      <th>alive</th>\n",
       "      <th>alone</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>male</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>S</td>\n",
       "      <td>Third</td>\n",
       "      <td>man</td>\n",
       "      <td>True</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Southampton</td>\n",
       "      <td>no</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>female</td>\n",
       "      <td>38.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>C</td>\n",
       "      <td>First</td>\n",
       "      <td>woman</td>\n",
       "      <td>False</td>\n",
       "      <td>C</td>\n",
       "      <td>Cherbourg</td>\n",
       "      <td>yes</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>female</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>S</td>\n",
       "      <td>Third</td>\n",
       "      <td>woman</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Southampton</td>\n",
       "      <td>yes</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>female</td>\n",
       "      <td>35.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>S</td>\n",
       "      <td>First</td>\n",
       "      <td>woman</td>\n",
       "      <td>False</td>\n",
       "      <td>C</td>\n",
       "      <td>Southampton</td>\n",
       "      <td>yes</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>male</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>S</td>\n",
       "      <td>Third</td>\n",
       "      <td>man</td>\n",
       "      <td>True</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Southampton</td>\n",
       "      <td>no</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   survived  pclass     sex   age  sibsp  parch     fare embarked  class  \\\n",
       "0         0       3    male  22.0      1      0   7.2500        S  Third   \n",
       "1         1       1  female  38.0      1      0  71.2833        C  First   \n",
       "2         1       3  female  26.0      0      0   7.9250        S  Third   \n",
       "3         1       1  female  35.0      1      0  53.1000        S  First   \n",
       "4         0       3    male  35.0      0      0   8.0500        S  Third   \n",
       "\n",
       "     who  adult_male deck  embark_town alive  alone  \n",
       "0    man        True  NaN  Southampton    no  False  \n",
       "1  woman       False    C    Cherbourg   yes  False  \n",
       "2  woman       False  NaN  Southampton   yes   True  \n",
       "3  woman       False    C  Southampton   yes  False  \n",
       "4    man        True  NaN  Southampton    no   True  "
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titanic.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [],
   "source": [
    "T = titanic[titanic.age.notnull()].copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "24.00    30\n",
       "22.00    27\n",
       "60.00    26\n",
       "18.00    26\n",
       "28.00    25\n",
       "30.00    25\n",
       "19.00    25\n",
       "21.00    24\n",
       "25.00    23\n",
       "36.00    22\n",
       "29.00    20\n",
       "35.00    18\n",
       "32.00    18\n",
       "27.00    18\n",
       "26.00    18\n",
       "31.00    17\n",
       "16.00    17\n",
       "34.00    15\n",
       "20.00    15\n",
       "33.00    15\n",
       "23.00    15\n",
       "39.00    14\n",
       "40.00    13\n",
       "17.00    13\n",
       "42.00    13\n",
       "45.00    12\n",
       "38.00    11\n",
       "4.00     10\n",
       "50.00    10\n",
       "2.00     10\n",
       "         ..\n",
       "8.00      4\n",
       "5.00      4\n",
       "11.00     4\n",
       "6.00      3\n",
       "7.00      3\n",
       "46.00     3\n",
       "30.50     2\n",
       "57.00     2\n",
       "0.83      2\n",
       "55.00     2\n",
       "10.00     2\n",
       "59.00     2\n",
       "13.00     2\n",
       "28.50     2\n",
       "40.50     2\n",
       "45.50     2\n",
       "0.75      2\n",
       "32.50     2\n",
       "34.50     1\n",
       "55.50     1\n",
       "0.92      1\n",
       "36.50     1\n",
       "12.00     1\n",
       "53.00     1\n",
       "14.50     1\n",
       "0.67      1\n",
       "20.50     1\n",
       "23.50     1\n",
       "24.50     1\n",
       "0.42      1\n",
       "Name: age, Length: 77, dtype: int64"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "T.age.apply(lambda x: 60 if x>=60 else x)\n",
    "T.age.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "20    220\n",
       "30    167\n",
       "10    102\n",
       "40     89\n",
       "0      62\n",
       "50     48\n",
       "60     26\n",
       "Name: age, dtype: int64"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Age = 10*(T[\"age\"]//10)\n",
    "Age = Age.astype(int)\n",
    "Age.head()\n",
    "Age.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0      20s\n",
       "1      30s\n",
       "2      20s\n",
       "3      30s\n",
       "4      30s\n",
       "6      50s\n",
       "7       0s\n",
       "8      20s\n",
       "9      10s\n",
       "10      0s\n",
       "11     50s\n",
       "12     20s\n",
       "13     30s\n",
       "14     10s\n",
       "15     50s\n",
       "16      0s\n",
       "18     30s\n",
       "20     30s\n",
       "21     30s\n",
       "22     10s\n",
       "23     20s\n",
       "24      0s\n",
       "25     30s\n",
       "27     10s\n",
       "30     40s\n",
       "33     60s\n",
       "34     20s\n",
       "35     40s\n",
       "37     20s\n",
       "38     10s\n",
       "      ... \n",
       "856    40s\n",
       "857    50s\n",
       "858    20s\n",
       "860    40s\n",
       "861    20s\n",
       "862    40s\n",
       "864    20s\n",
       "865    40s\n",
       "866    20s\n",
       "867    30s\n",
       "869     0s\n",
       "870    20s\n",
       "871    40s\n",
       "872    30s\n",
       "873    40s\n",
       "874    20s\n",
       "875    10s\n",
       "876    20s\n",
       "877    10s\n",
       "879    50s\n",
       "880    20s\n",
       "881    30s\n",
       "882    20s\n",
       "883    20s\n",
       "884    20s\n",
       "885    30s\n",
       "886    20s\n",
       "887    10s\n",
       "889    20s\n",
       "890    30s\n",
       "Name: age, Length: 714, dtype: object"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Age.astype(str)+\"s\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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       "    .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>age</th>\n",
       "      <th>0</th>\n",
       "      <th>10</th>\n",
       "      <th>20</th>\n",
       "      <th>30</th>\n",
       "      <th>40</th>\n",
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       "      <th>60</th>\n",
       "    </tr>\n",
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       "      <th>sex</th>\n",
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       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>female</th>\n",
       "      <td>0.633333</td>\n",
       "      <td>0.755556</td>\n",
       "      <td>0.722222</td>\n",
       "      <td>0.833333</td>\n",
       "      <td>0.687500</td>\n",
       "      <td>0.888889</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>male</th>\n",
       "      <td>0.593750</td>\n",
       "      <td>0.122807</td>\n",
       "      <td>0.168919</td>\n",
       "      <td>0.214953</td>\n",
       "      <td>0.210526</td>\n",
       "      <td>0.133333</td>\n",
       "      <td>0.136364</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "age           0         10        20        30        40        50        60\n",
       "sex                                                                         \n",
       "female  0.633333  0.755556  0.722222  0.833333  0.687500  0.888889  1.000000\n",
       "male    0.593750  0.122807  0.168919  0.214953  0.210526  0.133333  0.136364"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "T.groupby([\"sex\", Age])[\"survived\"].mean().unstack()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "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",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>age</th>\n",
       "      <th>0</th>\n",
       "      <th>10</th>\n",
       "      <th>20</th>\n",
       "      <th>30</th>\n",
       "      <th>40</th>\n",
       "      <th>50</th>\n",
       "      <th>60</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sex</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>female</th>\n",
       "      <td>0.633333</td>\n",
       "      <td>0.755556</td>\n",
       "      <td>0.722222</td>\n",
       "      <td>0.833333</td>\n",
       "      <td>0.687500</td>\n",
       "      <td>0.888889</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>male</th>\n",
       "      <td>0.593750</td>\n",
       "      <td>0.122807</td>\n",
       "      <td>0.168919</td>\n",
       "      <td>0.214953</td>\n",
       "      <td>0.210526</td>\n",
       "      <td>0.133333</td>\n",
       "      <td>0.136364</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "age           0         10        20        30        40        50        60\n",
       "sex                                                                         \n",
       "female  0.633333  0.755556  0.722222  0.833333  0.687500  0.888889  1.000000\n",
       "male    0.593750  0.122807  0.168919  0.214953  0.210526  0.133333  0.136364"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "T.age = Age\n",
    "T.pivot_table(\"survived\", index=\"sex\", columns=\"age\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {
    "scrolled": false
   },
   "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>survived</th>\n",
       "      <th>pclass</th>\n",
       "      <th>age</th>\n",
       "      <th>sibsp</th>\n",
       "      <th>parch</th>\n",
       "      <th>fare</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>891.000000</td>\n",
       "      <td>891.000000</td>\n",
       "      <td>714.000000</td>\n",
       "      <td>891.000000</td>\n",
       "      <td>891.000000</td>\n",
       "      <td>891.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>0.383838</td>\n",
       "      <td>2.308642</td>\n",
       "      <td>29.699118</td>\n",
       "      <td>0.523008</td>\n",
       "      <td>0.381594</td>\n",
       "      <td>32.204208</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.486592</td>\n",
       "      <td>0.836071</td>\n",
       "      <td>14.526497</td>\n",
       "      <td>1.102743</td>\n",
       "      <td>0.806057</td>\n",
       "      <td>49.693429</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.420000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>20.125000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>7.910400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>28.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>14.454200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>38.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>31.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>80.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>512.329200</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         survived      pclass         age       sibsp       parch        fare\n",
       "count  891.000000  891.000000  714.000000  891.000000  891.000000  891.000000\n",
       "mean     0.383838    2.308642   29.699118    0.523008    0.381594   32.204208\n",
       "std      0.486592    0.836071   14.526497    1.102743    0.806057   49.693429\n",
       "min      0.000000    1.000000    0.420000    0.000000    0.000000    0.000000\n",
       "25%      0.000000    2.000000   20.125000    0.000000    0.000000    7.910400\n",
       "50%      0.000000    3.000000   28.000000    0.000000    0.000000   14.454200\n",
       "75%      1.000000    3.000000   38.000000    1.000000    0.000000   31.000000\n",
       "max      1.000000    3.000000   80.000000    8.000000    6.000000  512.329200"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titanic.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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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>survived</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sex</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>female</th>\n",
       "      <td>0.742038</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>male</th>\n",
       "      <td>0.188908</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        survived\n",
       "sex             \n",
       "female  0.742038\n",
       "male    0.188908"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titanic.groupby(\"sex\")[[\"survived\"]].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "sex\n",
       "female    0.742038\n",
       "male      0.188908\n",
       "Name: survived, dtype: float64"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titanic.groupby(\"sex\")[\"survived\"].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {
    "scrolled": true
   },
   "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>class</th>\n",
       "      <th>First</th>\n",
       "      <th>Second</th>\n",
       "      <th>Third</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sex</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>female</th>\n",
       "      <td>0.968085</td>\n",
       "      <td>0.921053</td>\n",
       "      <td>0.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>male</th>\n",
       "      <td>0.368852</td>\n",
       "      <td>0.157407</td>\n",
       "      <td>0.135447</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "class      First    Second     Third\n",
       "sex                                 \n",
       "female  0.968085  0.921053  0.500000\n",
       "male    0.368852  0.157407  0.135447"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titanic.groupby([\"sex\", \"class\"])[\"survived\"].aggregate(\"mean\").unstack()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* 数据透视表"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {
    "scrolled": false
   },
   "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",
       "    .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>class</th>\n",
       "      <th>First</th>\n",
       "      <th>Second</th>\n",
       "      <th>Third</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sex</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>female</th>\n",
       "      <td>0.968085</td>\n",
       "      <td>0.921053</td>\n",
       "      <td>0.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>male</th>\n",
       "      <td>0.368852</td>\n",
       "      <td>0.157407</td>\n",
       "      <td>0.135447</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "class      First    Second     Third\n",
       "sex                                 \n",
       "female  0.968085  0.921053  0.500000\n",
       "male    0.368852  0.157407  0.135447"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titanic.pivot_table(\"survived\", index=\"sex\", columns=\"class\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {
    "scrolled": true
   },
   "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",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>class</th>\n",
       "      <th>First</th>\n",
       "      <th>Second</th>\n",
       "      <th>Third</th>\n",
       "      <th>All</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sex</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>female</th>\n",
       "      <td>0.968085</td>\n",
       "      <td>0.921053</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.742038</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>male</th>\n",
       "      <td>0.368852</td>\n",
       "      <td>0.157407</td>\n",
       "      <td>0.135447</td>\n",
       "      <td>0.188908</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>All</th>\n",
       "      <td>0.629630</td>\n",
       "      <td>0.472826</td>\n",
       "      <td>0.242363</td>\n",
       "      <td>0.383838</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "class      First    Second     Third       All\n",
       "sex                                           \n",
       "female  0.968085  0.921053  0.500000  0.742038\n",
       "male    0.368852  0.157407  0.135447  0.188908\n",
       "All     0.629630  0.472826  0.242363  0.383838"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titanic.pivot_table(\"survived\", index=\"sex\", columns=\"class\", aggfunc=\"mean\", margins=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "\n",
       "    .dataframe thead tr th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr:last-of-type th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th colspan=\"3\" halign=\"left\">fare</th>\n",
       "      <th colspan=\"3\" halign=\"left\">survived</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>class</th>\n",
       "      <th>First</th>\n",
       "      <th>Second</th>\n",
       "      <th>Third</th>\n",
       "      <th>First</th>\n",
       "      <th>Second</th>\n",
       "      <th>Third</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sex</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>female</th>\n",
       "      <td>106.125798</td>\n",
       "      <td>21.970121</td>\n",
       "      <td>16.118810</td>\n",
       "      <td>91</td>\n",
       "      <td>70</td>\n",
       "      <td>72</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>male</th>\n",
       "      <td>67.226127</td>\n",
       "      <td>19.741782</td>\n",
       "      <td>12.661633</td>\n",
       "      <td>45</td>\n",
       "      <td>17</td>\n",
       "      <td>47</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              fare                       survived             \n",
       "class        First     Second      Third    First Second Third\n",
       "sex                                                           \n",
       "female  106.125798  21.970121  16.118810       91     70    72\n",
       "male     67.226127  19.741782  12.661633       45     17    47"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titanic.pivot_table(index=\"sex\", columns=\"class\", aggfunc={\"survived\": \"sum\", \"fare\": \"mean\"})"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 12.7 其他"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**（1）向量化字符串操作**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**（2） 处理时间序列**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**（3） 多级索引：用于多维数据**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>population</th>\n",
       "      <th>GDP</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">BeiJing</th>\n",
       "      <th>2008</th>\n",
       "      <td>1771</td>\n",
       "      <td>11115</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018</th>\n",
       "      <td>2154</td>\n",
       "      <td>30320</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">ShangHai</th>\n",
       "      <th>2008</th>\n",
       "      <td>2141</td>\n",
       "      <td>14070</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018</th>\n",
       "      <td>2424</td>\n",
       "      <td>32680</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">ShenZhen</th>\n",
       "      <th>2008</th>\n",
       "      <td>1077</td>\n",
       "      <td>7806</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018</th>\n",
       "      <td>1303</td>\n",
       "      <td>24222</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">HangZhou</th>\n",
       "      <th>2008</th>\n",
       "      <td>798</td>\n",
       "      <td>4789</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018</th>\n",
       "      <td>981</td>\n",
       "      <td>13468</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               population    GDP\n",
       "BeiJing  2008        1771  11115\n",
       "         2018        2154  30320\n",
       "ShangHai 2008        2141  14070\n",
       "         2018        2424  32680\n",
       "ShenZhen 2008        1077   7806\n",
       "         2018        1303  24222\n",
       "HangZhou 2008         798   4789\n",
       "         2018         981  13468"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "base_data = np.array([[1771, 11115 ],\n",
    "                      [2154, 30320],\n",
    "                      [2141, 14070],\n",
    "                      [2424, 32680],\n",
    "                      [1077, 7806],\n",
    "                      [1303, 24222],\n",
    "                      [798, 4789],\n",
    "                      [981, 13468]]) \n",
    "data = pd.DataFrame(base_data, index=[[\"BeiJing\",\"BeiJing\",\"ShangHai\",\"ShangHai\",\"ShenZhen\",\"ShenZhen\",\"HangZhou\",\"HangZhou\"]\\\n",
    "                                     , [2008, 2018]*4], columns=[\"population\", \"GDP\"])\n",
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>population</th>\n",
       "      <th>GDP</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>city</th>\n",
       "      <th>year</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">BeiJing</th>\n",
       "      <th>2008</th>\n",
       "      <td>1771</td>\n",
       "      <td>11115</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018</th>\n",
       "      <td>2154</td>\n",
       "      <td>30320</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">ShangHai</th>\n",
       "      <th>2008</th>\n",
       "      <td>2141</td>\n",
       "      <td>14070</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018</th>\n",
       "      <td>2424</td>\n",
       "      <td>32680</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">ShenZhen</th>\n",
       "      <th>2008</th>\n",
       "      <td>1077</td>\n",
       "      <td>7806</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018</th>\n",
       "      <td>1303</td>\n",
       "      <td>24222</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">HangZhou</th>\n",
       "      <th>2008</th>\n",
       "      <td>798</td>\n",
       "      <td>4789</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018</th>\n",
       "      <td>981</td>\n",
       "      <td>13468</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               population    GDP\n",
       "city     year                   \n",
       "BeiJing  2008        1771  11115\n",
       "         2018        2154  30320\n",
       "ShangHai 2008        2141  14070\n",
       "         2018        2424  32680\n",
       "ShenZhen 2008        1077   7806\n",
       "         2018        1303  24222\n",
       "HangZhou 2008         798   4789\n",
       "         2018         981  13468"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.index.names = [\"city\", \"year\"]\n",
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "city      year\n",
       "BeiJing   2008    11115\n",
       "          2018    30320\n",
       "ShangHai  2008    14070\n",
       "          2018    32680\n",
       "ShenZhen  2008     7806\n",
       "          2018    24222\n",
       "HangZhou  2008     4789\n",
       "          2018    13468\n",
       "Name: GDP, dtype: int32"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[\"GDP\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "year\n",
       "2008    14070\n",
       "2018    32680\n",
       "Name: GDP, dtype: int32"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.loc[\"ShangHai\", \"GDP\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "32680"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.loc[\"ShangHai\", 2018][\"GDP\"]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**（4） 高性能的Pandas：eval（）**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [],
   "source": [
    "df1, df2, df3, df4 = (pd.DataFrame(np.random.random((10000,100))) for i in range(4))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "17.6 ms ± 120 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)\n"
     ]
    }
   ],
   "source": [
    "%timeit (df1+df2)/(df3+df4)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* 减少了复合代数式计算中间过程的内存分配"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "10.5 ms ± 153 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)\n"
     ]
    }
   ],
   "source": [
    "%timeit pd.eval(\"(df1+df2)/(df3+df4)\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.allclose((df1+df2)/(df3+df4), pd.eval(\"(df1+df2)/(df3+df4)\"))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* 实现列间运算"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
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       "      <td>0.739153</td>\n",
       "      <td>0.777161</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
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       "      <td>0.016556</td>\n",
       "      <td>0.348979</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
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       "          A         B         C\n",
       "0  0.418071  0.381836  0.500556\n",
       "1  0.059432  0.749066  0.302429\n",
       "2  0.489147  0.739153  0.777161\n",
       "3  0.175441  0.016556  0.348979\n",
       "4  0.766534  0.559252  0.310635"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.DataFrame(np.random.random((1000, 3)), columns=list(\"ABC\"))\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [],
   "source": [
    "res_1 = pd.eval(\"(df.A+df.B)/(df.C-1)\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [],
   "source": [
    "res_2 = df.eval(\"(A+B)/(C-1)\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.allclose(res_1, res_2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
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      "text/plain": [
       "          A         B         C         D\n",
       "0  0.418071  0.381836  0.500556 -1.601593\n",
       "1  0.059432  0.749066  0.302429 -1.159019\n",
       "2  0.489147  0.739153  0.777161 -5.512052\n",
       "3  0.175441  0.016556  0.348979 -0.294917\n",
       "4  0.766534  0.559252  0.310635 -1.923199"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"D\"] = pd.eval(\"(df.A+df.B)/(df.C-1)\")\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
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       "    <tr>\n",
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      "text/plain": [
       "          A         B         C         D\n",
       "0  0.418071  0.381836  0.500556 -1.601593\n",
       "1  0.059432  0.749066  0.302429 -1.159019\n",
       "2  0.489147  0.739153  0.777161 -5.512052\n",
       "3  0.175441  0.016556  0.348979 -0.294917\n",
       "4  0.766534  0.559252  0.310635 -1.923199"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.eval(\"D=(A+B)/(C-1)\", inplace=True)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* 使用局部变量"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    0.342788\n",
       "1    0.047409\n",
       "2   -0.387501\n",
       "3    0.236956\n",
       "4    0.694839\n",
       "dtype: float64"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "column_mean = df.mean(axis=1)\n",
    "res = df.eval(\"A+@column_mean\")\n",
    "res.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**（4） 高性能的Pandas：query（）**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th>D</th>\n",
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       "      <th>0</th>\n",
       "      <td>0.418071</td>\n",
       "      <td>0.381836</td>\n",
       "      <td>0.500556</td>\n",
       "      <td>-1.601593</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.059432</td>\n",
       "      <td>0.749066</td>\n",
       "      <td>0.302429</td>\n",
       "      <td>-1.159019</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.489147</td>\n",
       "      <td>0.739153</td>\n",
       "      <td>0.777161</td>\n",
       "      <td>-5.512052</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.175441</td>\n",
       "      <td>0.016556</td>\n",
       "      <td>0.348979</td>\n",
       "      <td>-0.294917</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.766534</td>\n",
       "      <td>0.559252</td>\n",
       "      <td>0.310635</td>\n",
       "      <td>-1.923199</td>\n",
       "    </tr>\n",
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      ],
      "text/plain": [
       "          A         B         C         D\n",
       "0  0.418071  0.381836  0.500556 -1.601593\n",
       "1  0.059432  0.749066  0.302429 -1.159019\n",
       "2  0.489147  0.739153  0.777161 -5.512052\n",
       "3  0.175441  0.016556  0.348979 -0.294917\n",
       "4  0.766534  0.559252  0.310635 -1.923199"
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1.11 ms ± 9.38 µs per loop (mean ± std. dev. of 7 runs, 1000 loops each)\n"
     ]
    }
   ],
   "source": [
    "%timeit df[(df.A < 0.5) & (df.B > 0.5)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2.55 ms ± 199 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)\n"
     ]
    }
   ],
   "source": [
    "%timeit df.query(\"(A < 0.5)&(B > 0.5)\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
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       "      <th>B</th>\n",
       "      <th>C</th>\n",
       "      <th>D</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.059432</td>\n",
       "      <td>0.749066</td>\n",
       "      <td>0.302429</td>\n",
       "      <td>-1.159019</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.489147</td>\n",
       "      <td>0.739153</td>\n",
       "      <td>0.777161</td>\n",
       "      <td>-5.512052</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0.073950</td>\n",
       "      <td>0.730144</td>\n",
       "      <td>0.646190</td>\n",
       "      <td>-2.272672</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>0.393200</td>\n",
       "      <td>0.610467</td>\n",
       "      <td>0.697096</td>\n",
       "      <td>-3.313485</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>0.065734</td>\n",
       "      <td>0.764699</td>\n",
       "      <td>0.179380</td>\n",
       "      <td>-1.011958</td>\n",
       "    </tr>\n",
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      ],
      "text/plain": [
       "           A         B         C         D\n",
       "1   0.059432  0.749066  0.302429 -1.159019\n",
       "2   0.489147  0.739153  0.777161 -5.512052\n",
       "7   0.073950  0.730144  0.646190 -2.272672\n",
       "10  0.393200  0.610467  0.697096 -3.313485\n",
       "11  0.065734  0.764699  0.179380 -1.011958"
      ]
     },
     "execution_count": 97,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.query(\"(A < 0.5)&(B > 0.5)\").head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 98,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.allclose(df[(df.A < 0.5) & (df.B > 0.5)], df.query(\"(A < 0.5)&(B > 0.5)\"))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**（5）eval（）和query（）的使用时机**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "小数组时，普通方法反而更快"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "32000"
      ]
     },
     "execution_count": 99,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.values.nbytes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "8000000"
      ]
     },
     "execution_count": 100,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1.values.nbytes"
   ]
  }
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