10_New_Features_in_Pandas_0.25.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Pandas0.25来了，别错过这10大好用的新功能"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-08-05T07:10:11.565230Z",
     "start_time": "2019-08-05T07:10:10.673141Z"
    }
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-08-05T07:10:11.590233Z",
     "start_time": "2019-08-05T07:10:11.569231Z"
    },
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'0.25.0'"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.__version__"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-08-05T07:10:11.607234Z",
     "start_time": "2019-08-05T07:10:11.595233Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'1.17.0'"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.__version__"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 1. Groupby 的命名聚合（Named Aggregation）"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1.1 在 DataFrame 上应用命名聚合"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-08-05T07:10:11.635237Z",
     "start_time": "2019-08-05T07:10:11.615235Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>品种</th>\n",
       "      <th>身高</th>\n",
       "      <th>体重</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>猫</td>\n",
       "      <td>9.1</td>\n",
       "      <td>7.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>狗</td>\n",
       "      <td>6.0</td>\n",
       "      <td>7.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>猫</td>\n",
       "      <td>9.5</td>\n",
       "      <td>9.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>狗</td>\n",
       "      <td>34.0</td>\n",
       "      <td>198.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  品种    身高     体重\n",
       "0  猫   9.1    7.9\n",
       "1  狗   6.0    7.5\n",
       "2  猫   9.5    9.9\n",
       "3  狗  34.0  198.0"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "animals = pd.DataFrame({'品种': ['猫', '狗', '猫', '狗'],\n",
    "                        '身高': [9.1, 6.0, 9.5, 34.0],\n",
    "                        '体重': [7.9, 7.5, 9.9, 198.0]})\n",
    "animals"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-08-05T07:10:11.686242Z",
     "start_time": "2019-08-05T07:10:11.640238Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>最低</th>\n",
       "      <th>最高</th>\n",
       "      <th>平均体重</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>品种</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>狗</th>\n",
       "      <td>6.0</td>\n",
       "      <td>34.0</td>\n",
       "      <td>102.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>猫</th>\n",
       "      <td>9.1</td>\n",
       "      <td>9.5</td>\n",
       "      <td>8.90</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     最低    最高    平均体重\n",
       "品种                   \n",
       "狗   6.0  34.0  102.75\n",
       "猫   9.1   9.5    8.90"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "animals.groupby('品种').agg(\n",
    "    最低=pd.NamedAgg(column='身高', aggfunc='min'),\n",
    "    最高=pd.NamedAgg(column='身高', aggfunc='max'),\n",
    "    平均体重=pd.NamedAgg(column='体重', aggfunc=np.mean),\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-08-05T07:10:11.726246Z",
     "start_time": "2019-08-05T07:10:11.689243Z"
    },
    "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",
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       "    }\n",
       "\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>最低</th>\n",
       "      <th>最高</th>\n",
       "      <th>平均体重</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>品种</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>狗</th>\n",
       "      <td>6.0</td>\n",
       "      <td>34.0</td>\n",
       "      <td>102.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>猫</th>\n",
       "      <td>9.1</td>\n",
       "      <td>9.5</td>\n",
       "      <td>8.90</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     最低    最高    平均体重\n",
       "品种                   \n",
       "狗   6.0  34.0  102.75\n",
       "猫   9.1   9.5    8.90"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "animals.groupby('品种').agg(\n",
    "    最低=('身高', min),\n",
    "    最高=('身高', max),\n",
    "    平均体重=('体重', np.mean),\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1.2 在序列（ Series）上应用命名聚合"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-08-05T07:10:11.764250Z",
     "start_time": "2019-08-05T07:10:11.737247Z"
    },
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\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>最低</th>\n",
       "      <th>最高</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>品种</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>狗</th>\n",
       "      <td>6.0</td>\n",
       "      <td>34.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>猫</th>\n",
       "      <td>9.1</td>\n",
       "      <td>9.5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     最低    最高\n",
       "品种           \n",
       "狗   6.0  34.0\n",
       "猫   9.1   9.5"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "animals.groupby('品种').身高.agg(\n",
    "    最低=min,\n",
    "    最高=max,\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-08-05T07:10:11.816255Z",
     "start_time": "2019-08-05T07:10:11.772251Z"
    }
   },
   "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>\n",
       "      <th></th>\n",
       "      <th colspan=\"2\" halign=\"left\">身高</th>\n",
       "      <th colspan=\"2\" halign=\"left\">体重</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>&lt;lambda_0&gt;</th>\n",
       "      <th>&lt;lambda_1&gt;</th>\n",
       "      <th>&lt;lambda_0&gt;</th>\n",
       "      <th>&lt;lambda_1&gt;</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>品种</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>狗</th>\n",
       "      <td>-28.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>-190.5</td>\n",
       "      <td>205.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>猫</th>\n",
       "      <td>-0.4</td>\n",
       "      <td>18.6</td>\n",
       "      <td>-2.0</td>\n",
       "      <td>17.8</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           身高                    体重           \n",
       "   <lambda_0> <lambda_1> <lambda_0> <lambda_1>\n",
       "品种                                            \n",
       "狗       -28.0       40.0     -190.5      205.5\n",
       "猫        -0.4       18.6       -2.0       17.8"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "animals.groupby('品种').agg([\n",
    "    lambda x: x.iloc[0] - x.iloc[1],\n",
    "    lambda x: x.iloc[0] + x.iloc[1]\n",
    "])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 2. Groupby 聚合支持多个 lambda 函数"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-08-05T07:10:11.839258Z",
     "start_time": "2019-08-05T07:10:11.821256Z"
    }
   },
   "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>&lt;lambda_0&gt;</th>\n",
       "      <th>&lt;lambda_1&gt;</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>品种</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>狗</th>\n",
       "      <td>6.0</td>\n",
       "      <td>34.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>猫</th>\n",
       "      <td>9.1</td>\n",
       "      <td>9.5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    <lambda_0>  <lambda_1>\n",
       "品种                        \n",
       "狗          6.0        34.0\n",
       "猫          9.1         9.5"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "animals.groupby('品种').身高.agg([\n",
    "    lambda x: x.iloc[0], lambda x: x.iloc[-1]\n",
    "])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 3. 优化了 MultiIndex 显示输出"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-08-05T07:10:11.863260Z",
     "start_time": "2019-08-05T07:10:11.846258Z"
    },
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "MultiIndex([(  'a',   0),\n",
       "            (  'a',   1),\n",
       "            (  'a',   2),\n",
       "            (  'a',   3),\n",
       "            (  'a',   4),\n",
       "            (  'a',   5),\n",
       "            (  'a',   6),\n",
       "            (  'a',   7),\n",
       "            (  'a',   8),\n",
       "            (  'a',   9),\n",
       "            ...\n",
       "            ('abc', 490),\n",
       "            ('abc', 491),\n",
       "            ('abc', 492),\n",
       "            ('abc', 493),\n",
       "            ('abc', 494),\n",
       "            ('abc', 495),\n",
       "            ('abc', 496),\n",
       "            ('abc', 497),\n",
       "            ('abc', 498),\n",
       "            ('abc', 499)],\n",
       "           length=1000)"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.MultiIndex.from_product([['a', 'abc'], range(500)])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 4. 精简显示  Series 与 DataFrame\n",
    "增加了 `display.min_rows` 选项。\n",
    "\n",
    "但是 Jupyter Notebook 貌似不支持。 "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-08-05T07:10:11.879262Z",
     "start_time": "2019-08-05T07:10:11.868260Z"
    }
   },
   "outputs": [],
   "source": [
    "sales_date1 = pd.date_range('20190101', periods=1000, freq='D')\n",
    "amount1 = np.arange(1000)\n",
    "cols = ['销售金额']\n",
    "sales1 = pd.DataFrame(amount1,index=sales_date1,columns=cols)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-08-05T07:10:11.907264Z",
     "start_time": "2019-08-05T07:10:11.884262Z"
    },
    "scrolled": false
   },
   "outputs": [
    {
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       "      <th></th>\n",
       "      <th>销售金额</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2019-01-01</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-01-02</th>\n",
       "      <td>1</td>\n",
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       "      <th>2019-01-03</th>\n",
       "      <td>2</td>\n",
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       "      <th>2019-01-04</th>\n",
       "      <td>3</td>\n",
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       "      <th>2019-01-05</th>\n",
       "      <td>4</td>\n",
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       "      <th>2019-01-06</th>\n",
       "      <td>5</td>\n",
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       "    <tr>\n",
       "      <th>2019-01-07</th>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-01-08</th>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-01-09</th>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-01-10</th>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-01-11</th>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-01-12</th>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-01-13</th>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-01-14</th>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-01-15</th>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-01-16</th>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-01-17</th>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-01-18</th>\n",
       "      <td>17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-01-19</th>\n",
       "      <td>18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-01-20</th>\n",
       "      <td>19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-01-21</th>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-01-22</th>\n",
       "      <td>21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-01-23</th>\n",
       "      <td>22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-01-24</th>\n",
       "      <td>23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-01-25</th>\n",
       "      <td>24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-01-26</th>\n",
       "      <td>25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-01-27</th>\n",
       "      <td>26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-01-28</th>\n",
       "      <td>27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-01-29</th>\n",
       "      <td>28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-01-30</th>\n",
       "      <td>29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-08-28</th>\n",
       "      <td>970</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-08-29</th>\n",
       "      <td>971</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-08-30</th>\n",
       "      <td>972</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-08-31</th>\n",
       "      <td>973</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-09-01</th>\n",
       "      <td>974</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-09-02</th>\n",
       "      <td>975</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-09-03</th>\n",
       "      <td>976</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-09-04</th>\n",
       "      <td>977</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-09-05</th>\n",
       "      <td>978</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-09-06</th>\n",
       "      <td>979</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-09-07</th>\n",
       "      <td>980</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-09-08</th>\n",
       "      <td>981</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-09-09</th>\n",
       "      <td>982</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-09-10</th>\n",
       "      <td>983</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-09-11</th>\n",
       "      <td>984</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-09-12</th>\n",
       "      <td>985</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-09-13</th>\n",
       "      <td>986</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-09-14</th>\n",
       "      <td>987</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-09-15</th>\n",
       "      <td>988</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-09-16</th>\n",
       "      <td>989</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-09-17</th>\n",
       "      <td>990</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-09-18</th>\n",
       "      <td>991</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-09-19</th>\n",
       "      <td>992</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-09-20</th>\n",
       "      <td>993</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-09-21</th>\n",
       "      <td>994</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-09-22</th>\n",
       "      <td>995</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-09-23</th>\n",
       "      <td>996</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-09-24</th>\n",
       "      <td>997</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-09-25</th>\n",
       "      <td>998</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-09-26</th>\n",
       "      <td>999</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1000 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            销售金额\n",
       "2019-01-01     0\n",
       "2019-01-02     1\n",
       "2019-01-03     2\n",
       "2019-01-04     3\n",
       "2019-01-05     4\n",
       "...          ...\n",
       "2021-09-22   995\n",
       "2021-09-23   996\n",
       "2021-09-24   997\n",
       "2021-09-25   998\n",
       "2021-09-26   999\n",
       "\n",
       "[1000 rows x 1 columns]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sales1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-08-05T07:10:11.921266Z",
     "start_time": "2019-08-05T07:10:11.912265Z"
    }
   },
   "outputs": [],
   "source": [
    "sales_date2 = pd.date_range('20190101', periods=50, freq='D')\n",
    "amount2 = np.arange(50)\n",
    "cols = ['销售金额']\n",
    "sales2 = pd.DataFrame(amount2,index=sales_date2,columns=cols)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-08-05T07:10:11.949269Z",
     "start_time": "2019-08-05T07:10:11.926266Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
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       "      <th>销售金额</th>\n",
       "    </tr>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2019-01-01</th>\n",
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       "      <td>2</td>\n",
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       "    <tr>\n",
       "      <th>2019-01-04</th>\n",
       "      <td>3</td>\n",
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       "    <tr>\n",
       "      <th>2019-01-05</th>\n",
       "      <td>4</td>\n",
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       "      <th>2019-01-07</th>\n",
       "      <td>6</td>\n",
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       "    <tr>\n",
       "      <th>2019-01-08</th>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-01-09</th>\n",
       "      <td>8</td>\n",
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       "    <tr>\n",
       "      <th>2019-01-10</th>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-01-11</th>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-01-12</th>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-01-13</th>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-01-14</th>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-01-15</th>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-01-16</th>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-01-17</th>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-01-18</th>\n",
       "      <td>17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-01-19</th>\n",
       "      <td>18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-01-20</th>\n",
       "      <td>19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-01-21</th>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-01-22</th>\n",
       "      <td>21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-01-23</th>\n",
       "      <td>22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-01-24</th>\n",
       "      <td>23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-01-25</th>\n",
       "      <td>24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-01-26</th>\n",
       "      <td>25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-01-27</th>\n",
       "      <td>26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-01-28</th>\n",
       "      <td>27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-01-29</th>\n",
       "      <td>28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-01-30</th>\n",
       "      <td>29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-01-31</th>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-02-01</th>\n",
       "      <td>31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-02-02</th>\n",
       "      <td>32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-02-03</th>\n",
       "      <td>33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-02-04</th>\n",
       "      <td>34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-02-05</th>\n",
       "      <td>35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-02-06</th>\n",
       "      <td>36</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-02-07</th>\n",
       "      <td>37</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-02-08</th>\n",
       "      <td>38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-02-09</th>\n",
       "      <td>39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-02-10</th>\n",
       "      <td>40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-02-11</th>\n",
       "      <td>41</td>\n",
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       "    <tr>\n",
       "      <th>2019-02-12</th>\n",
       "      <td>42</td>\n",
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       "    <tr>\n",
       "      <th>2019-02-13</th>\n",
       "      <td>43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-02-14</th>\n",
       "      <td>44</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-02-15</th>\n",
       "      <td>45</td>\n",
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       "    <tr>\n",
       "      <th>2019-02-16</th>\n",
       "      <td>46</td>\n",
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       "    <tr>\n",
       "      <th>2019-02-17</th>\n",
       "      <td>47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-02-18</th>\n",
       "      <td>48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-02-19</th>\n",
       "      <td>49</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            销售金额\n",
       "2019-01-01     0\n",
       "2019-01-02     1\n",
       "2019-01-03     2\n",
       "2019-01-04     3\n",
       "2019-01-05     4\n",
       "2019-01-06     5\n",
       "2019-01-07     6\n",
       "2019-01-08     7\n",
       "2019-01-09     8\n",
       "2019-01-10     9\n",
       "2019-01-11    10\n",
       "2019-01-12    11\n",
       "2019-01-13    12\n",
       "2019-01-14    13\n",
       "2019-01-15    14\n",
       "2019-01-16    15\n",
       "2019-01-17    16\n",
       "2019-01-18    17\n",
       "2019-01-19    18\n",
       "2019-01-20    19\n",
       "2019-01-21    20\n",
       "2019-01-22    21\n",
       "2019-01-23    22\n",
       "2019-01-24    23\n",
       "2019-01-25    24\n",
       "2019-01-26    25\n",
       "2019-01-27    26\n",
       "2019-01-28    27\n",
       "2019-01-29    28\n",
       "2019-01-30    29\n",
       "2019-01-31    30\n",
       "2019-02-01    31\n",
       "2019-02-02    32\n",
       "2019-02-03    33\n",
       "2019-02-04    34\n",
       "2019-02-05    35\n",
       "2019-02-06    36\n",
       "2019-02-07    37\n",
       "2019-02-08    38\n",
       "2019-02-09    39\n",
       "2019-02-10    40\n",
       "2019-02-11    41\n",
       "2019-02-12    42\n",
       "2019-02-13    43\n",
       "2019-02-14    44\n",
       "2019-02-15    45\n",
       "2019-02-16    46\n",
       "2019-02-17    47\n",
       "2019-02-18    48\n",
       "2019-02-19    49"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sales2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-08-05T07:10:11.960270Z",
     "start_time": "2019-08-05T07:10:11.955269Z"
    }
   },
   "outputs": [],
   "source": [
    "pd.options.display.min_rows = 30"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-08-05T07:10:11.972271Z",
     "start_time": "2019-08-05T07:10:11.965270Z"
    }
   },
   "outputs": [],
   "source": [
    "pd.options.display.max_rows = 60"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-08-05T07:10:11.987272Z",
     "start_time": "2019-08-05T07:10:11.976271Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(60, 30)"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.get_option(\"display.max_rows\"),pd.get_option(\"display.min_rows\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 5. json_normalize()  支持 max_level"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-08-05T07:10:12.019276Z",
     "start_time": "2019-08-05T07:10:11.994273Z"
    }
   },
   "outputs": [
    {
     "data": {
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       "\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
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       "      <th></th>\n",
       "      <th>CreatedBy.Name</th>\n",
       "      <th>Lookup.TextField</th>\n",
       "      <th>Lookup.UserField</th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>User001</td>\n",
       "      <td>Some text</td>\n",
       "      <td>{'Id': 'ID001', 'Name': 'Name001'}</td>\n",
       "      <td>b</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  CreatedBy.Name Lookup.TextField                    Lookup.UserField Image.a\n",
       "0        User001        Some text  {'Id': 'ID001', 'Name': 'Name001'}       b"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from pandas.io.json import json_normalize\n",
    "\n",
    "data = [{\n",
    "        'CreatedBy': {'Name': 'User001'},\n",
    "        'Lookup': {'TextField': 'Some text',\n",
    "                   'UserField': {'Id': 'ID001', 'Name': 'Name001'}},\n",
    "        'Image': {'a': 'b'}\n",
    "        }]\n",
    "\n",
    "json_normalize(data, max_level=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 6. 增加 explode() 方法，把 list 炸成行"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-08-05T07:10:12.037277Z",
     "start_time": "2019-08-05T07:10:12.024276Z"
    },
    "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",
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       "\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>变量1</th>\n",
       "      <th>变量2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>a,b,c</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>d,e,f</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     变量1  变量2\n",
       "0  a,b,c    1\n",
       "1  d,e,f    2"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.DataFrame([{'变量1': 'a,b,c', '变量2': 1},\n",
    "                   {'变量1': 'd,e,f', '变量2': 2}])\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-08-05T07:10:12.076281Z",
     "start_time": "2019-08-05T07:10:12.042278Z"
    },
    "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",
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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></th>\n",
       "      <th>变量1</th>\n",
       "      <th>变量2</th>\n",
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       "      <th>0</th>\n",
       "      <td>c</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>d</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>e</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>f</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  变量1  变量2\n",
       "0   a    1\n",
       "0   b    1\n",
       "0   c    1\n",
       "1   d    2\n",
       "1   e    2\n",
       "1   f    2"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.assign(变量1=df.变量1.str.split(',')).explode('变量1')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 7. SparseDataFrame 被废弃了"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-08-05T07:10:12.096283Z",
     "start_time": "2019-08-05T07:10:12.082282Z"
    },
    "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",
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       "\n",
       "    .dataframe thead th {\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
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       "      <th></th>\n",
       "      <th>A</th>\n",
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      "text/plain": [
       "   A\n",
       "0  0\n",
       "1  1"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "pd.DataFrame({\"A\": pd.SparseArray([0, 1])})"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 8. 对 DataFrame Groupby 后，Groupby.apply 对每组只处理一次"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-08-05T07:10:12.150289Z",
     "start_time": "2019-08-05T07:10:12.109285Z"
    },
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "x\n",
      "y\n"
     ]
    },
    {
     "data": {
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       "      <th>a</th>\n",
       "      <th>b</th>\n",
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       "      <th>0</th>\n",
       "      <td>x</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>y</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   a  b\n",
       "0  x  1\n",
       "1  y  2"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.DataFrame({\"a\": [\"x\", \"y\"], \"b\": [1, 2]})\n",
    "df\n",
    "\n",
    "def func(group):\n",
    "    print(group.name)\n",
    "    return group\n",
    "\n",
    "df.groupby('a').apply(func)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 9. 用 Dict 生成的 DataFrame，终于支持列排序啦"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-08-05T07:10:12.176291Z",
     "start_time": "2019-08-05T07:10:12.159290Z"
    },
    "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>姓 名</th>\n",
       "      <th>城 市</th>\n",
       "      <th>年 龄</th>\n",
       "      <th>爱 好</th>\n",
       "      <th>财务状况</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>张三</td>\n",
       "      <td>北京</td>\n",
       "      <td>18</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>李四</td>\n",
       "      <td>上海</td>\n",
       "      <td>19</td>\n",
       "      <td>打游戏</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>王五</td>\n",
       "      <td>广州</td>\n",
       "      <td>20</td>\n",
       "      <td>NaN</td>\n",
       "      <td>优</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  姓 名 城 市  年 龄  爱 好 财务状况\n",
       "0  张三  北京   18  NaN  NaN\n",
       "1  李四  上海   19  打游戏  NaN\n",
       "2  王五  广州   20  NaN    优"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = [\n",
    "    {'姓 名': '张三', '城 市': '北京', '年 龄': 18},\n",
    "    {'姓 名': '李四', '城 市': '上海', '年 龄': 19, '爱 好': '打游戏'},\n",
    "    {'姓 名': '王五', '城 市': '广州', '年 龄': 20, '财务状况': '优'}\n",
    "]\n",
    "pd.DataFrame(data)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 10. Query() 支持列名空格了"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-08-05T07:10:12.200294Z",
     "start_time": "2019-08-05T07:10:12.181292Z"
    }
   },
   "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",
       "        vertical-align: top;\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
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       "      <th>0</th>\n",
       "      <td>张三</td>\n",
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       "      <td>18</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>李四</td>\n",
       "      <td>上海</td>\n",
       "      <td>19</td>\n",
       "      <td>打游戏</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>王五</td>\n",
       "      <td>广州</td>\n",
       "      <td>20</td>\n",
       "      <td>NaN</td>\n",
       "      <td>优</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
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      "text/plain": [
       "  姓 名 城 市  年 龄  爱 好 财务状况\n",
       "0  张三  北京   18  NaN  NaN\n",
       "1  李四  上海   19  打游戏  NaN\n",
       "2  王五  广州   20  NaN    优"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.DataFrame(data)\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-08-05T07:10:12.230297Z",
     "start_time": "2019-08-05T07:10:12.207294Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "      <td>张三</td>\n",
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       "      <td>18</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "  姓 名 城 市  年 龄  爱 好 财务状况\n",
       "0  张三  北京   18  NaN  NaN"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.query('`年 龄` <19')"
   ]
  }
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