practice1 - chipotle - 了解数据.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Chipotle快餐数据 - 了解数据"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![](images/1.jpeg)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1. 从文件中加载数据，并查看数据前3行"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# 导入pandas库并命名为pd\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "chipotle.tsv\n"
     ]
    }
   ],
   "source": [
    "!ls data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# 加载数据\n",
    "chipo = pd.read_csv('data/chipotle.tsv', sep='\\t')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "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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       "        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>order_id</th>\n",
       "      <th>quantity</th>\n",
       "      <th>item_name</th>\n",
       "      <th>choice_description</th>\n",
       "      <th>item_price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Chips and Fresh Tomato Salsa</td>\n",
       "      <td>NaN</td>\n",
       "      <td>$2.39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Izze</td>\n",
       "      <td>[Clementine]</td>\n",
       "      <td>$3.39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Nantucket Nectar</td>\n",
       "      <td>[Apple]</td>\n",
       "      <td>$3.39</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   order_id  quantity                     item_name choice_description  \\\n",
       "0         1         1  Chips and Fresh Tomato Salsa                NaN   \n",
       "1         1         1                          Izze       [Clementine]   \n",
       "2         1         1              Nantucket Nectar            [Apple]   \n",
       "\n",
       "  item_price  \n",
       "0     $2.39   \n",
       "1     $3.39   \n",
       "2     $3.39   "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "chipo.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "order_id               int64\n",
       "quantity               int64\n",
       "item_name             object\n",
       "choice_description    object\n",
       "item_price            object\n",
       "dtype: object"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 查看各列的数据类型\n",
    "chipo.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# 将item_price列转换为数字类型\n",
    "chipo.item_price = chipo.item_price.apply(lambda x: float(x[1:]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "order_id                int64\n",
       "quantity                int64\n",
       "item_name              object\n",
       "choice_description     object\n",
       "item_price            float64\n",
       "dtype: object"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "chipo.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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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>order_id</th>\n",
       "      <th>quantity</th>\n",
       "      <th>item_name</th>\n",
       "      <th>choice_description</th>\n",
       "      <th>item_price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Chips and Fresh Tomato Salsa</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Izze</td>\n",
       "      <td>[Clementine]</td>\n",
       "      <td>3.39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Nantucket Nectar</td>\n",
       "      <td>[Apple]</td>\n",
       "      <td>3.39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Chips and Tomatillo-Green Chili Salsa</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>Chicken Bowl</td>\n",
       "      <td>[Tomatillo-Red Chili Salsa (Hot), [Black Beans...</td>\n",
       "      <td>16.98</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>Chicken Bowl</td>\n",
       "      <td>[Fresh Tomato Salsa (Mild), [Rice, Cheese, Sou...</td>\n",
       "      <td>10.98</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>Side of Chips</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.69</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>Steak Burrito</td>\n",
       "      <td>[Tomatillo Red Chili Salsa, [Fajita Vegetables...</td>\n",
       "      <td>11.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>Steak Soft Tacos</td>\n",
       "      <td>[Tomatillo Green Chili Salsa, [Pinto Beans, Ch...</td>\n",
       "      <td>9.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>Steak Burrito</td>\n",
       "      <td>[Fresh Tomato Salsa, [Rice, Black Beans, Pinto...</td>\n",
       "      <td>9.25</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   order_id  quantity                              item_name  \\\n",
       "0         1         1           Chips and Fresh Tomato Salsa   \n",
       "1         1         1                                   Izze   \n",
       "2         1         1                       Nantucket Nectar   \n",
       "3         1         1  Chips and Tomatillo-Green Chili Salsa   \n",
       "4         2         2                           Chicken Bowl   \n",
       "5         3         1                           Chicken Bowl   \n",
       "6         3         1                          Side of Chips   \n",
       "7         4         1                          Steak Burrito   \n",
       "8         4         1                       Steak Soft Tacos   \n",
       "9         5         1                          Steak Burrito   \n",
       "\n",
       "                                  choice_description  item_price  \n",
       "0                                                NaN        2.39  \n",
       "1                                       [Clementine]        3.39  \n",
       "2                                            [Apple]        3.39  \n",
       "3                                                NaN        2.39  \n",
       "4  [Tomatillo-Red Chili Salsa (Hot), [Black Beans...       16.98  \n",
       "5  [Fresh Tomato Salsa (Mild), [Rice, Cheese, Sou...       10.98  \n",
       "6                                                NaN        1.69  \n",
       "7  [Tomatillo Red Chili Salsa, [Fajita Vegetables...       11.75  \n",
       "8  [Tomatillo Green Chili Salsa, [Pinto Beans, Ch...        9.25  \n",
       "9  [Fresh Tomato Salsa, [Rice, Black Beans, Pinto...        9.25  "
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "chipo.head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2. 查看数据集的基本信息"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 4622 entries, 0 to 4621\n",
      "Data columns (total 5 columns):\n",
      "order_id              4622 non-null int64\n",
      "quantity              4622 non-null int64\n",
      "item_name             4622 non-null object\n",
      "choice_description    3376 non-null object\n",
      "item_price            4622 non-null float64\n",
      "dtypes: float64(1), int64(2), object(2)\n",
      "memory usage: 180.6+ KB\n"
     ]
    }
   ],
   "source": [
    "chipo.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3. 查看数据的行列数和列名"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(row, column) =  (4622, 5)\n"
     ]
    }
   ],
   "source": [
    "print ('(row, column) = ', chipo.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "columns =  Index(['order_id', 'quantity', 'item_name', 'choice_description',\n",
      "       'item_price'],\n",
      "      dtype='object')\n"
     ]
    }
   ],
   "source": [
    "# 列名\n",
    "print('columns = ', chipo.columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RangeIndex(start=0, stop=4622, step=1)"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 查看索引信息\n",
    "chipo.index"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 4. 查询商品种类的数量，以及下单最多的几种商品"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total items:  50\n",
      "--------------------------------------------------------------------------------\n",
      "Total items:  50\n",
      "--------------------------------------------------------------------------------\n",
      "Total items =  50\n"
     ]
    }
   ],
   "source": [
    "print ('Total items: ', len(chipo.item_name.unique()))\n",
    "print ('--------------------------------------------------------------------------------')\n",
    "print ('Total items: ', chipo.item_name.value_counts().count())\n",
    "print ('--------------------------------------------------------------------------------')\n",
    "print ('Total items = ', len(chipo.groupby(['item_name']).groups))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Chicken Bowl', 'Chicken Burrito', 'Chips and Guacamole',\n",
       "       'Steak Burrito', 'Canned Soft Drink'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 获取销量最多的商品名\n",
    "chipo.item_name.value_counts().head().index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Chicken Bowl           726\n",
       "Chicken Burrito        553\n",
       "Chips and Guacamole    479\n",
       "Steak Burrito          368\n",
       "Canned Soft Drink      301\n",
       "Name: item_name, dtype: int64"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 获取销量最多的商品销售量\n",
    "chipo.item_name.value_counts().head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 5. 查询最受欢迎的食物及其销量，销售额"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "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>order_id</th>\n",
       "      <th>quantity</th>\n",
       "      <th>item_price</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>item_name</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Chicken Bowl</th>\n",
       "      <td>713926</td>\n",
       "      <td>761</td>\n",
       "      <td>7342.73</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Chicken Burrito</th>\n",
       "      <td>497303</td>\n",
       "      <td>591</td>\n",
       "      <td>5575.82</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Chips and Guacamole</th>\n",
       "      <td>449959</td>\n",
       "      <td>506</td>\n",
       "      <td>2201.04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Steak Burrito</th>\n",
       "      <td>328437</td>\n",
       "      <td>386</td>\n",
       "      <td>3851.43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Canned Soft Drink</th>\n",
       "      <td>304753</td>\n",
       "      <td>351</td>\n",
       "      <td>438.75</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     order_id  quantity  item_price\n",
       "item_name                                          \n",
       "Chicken Bowl           713926       761     7342.73\n",
       "Chicken Burrito        497303       591     5575.82\n",
       "Chips and Guacamole    449959       506     2201.04\n",
       "Steak Burrito          328437       386     3851.43\n",
       "Canned Soft Drink      304753       351      438.75"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 查询最受欢迎的食物\n",
    "favoriteItems = chipo.groupby(['item_name']).sum().sort_values('quantity', ascending=False)\n",
    "# 删除order_id列\n",
    "favoriteItems=favoriteItems.drop('order_id', axis=1)\n",
    "favoriteItems.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 6. 查询最受欢迎食物（下单数，销量，销售额）"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>order_id</th>\n",
       "      <th>quantity</th>\n",
       "      <th>item_price</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>item_name</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Chicken Bowl</th>\n",
       "      <td>726</td>\n",
       "      <td>761</td>\n",
       "      <td>7342.73</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Chicken Burrito</th>\n",
       "      <td>553</td>\n",
       "      <td>591</td>\n",
       "      <td>5575.82</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Chips and Guacamole</th>\n",
       "      <td>479</td>\n",
       "      <td>506</td>\n",
       "      <td>2201.04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Steak Burrito</th>\n",
       "      <td>368</td>\n",
       "      <td>386</td>\n",
       "      <td>3851.43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Canned Soft Drink</th>\n",
       "      <td>301</td>\n",
       "      <td>351</td>\n",
       "      <td>438.75</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     order_id  quantity  item_price\n",
       "item_name                                          \n",
       "Chicken Bowl              726       761     7342.73\n",
       "Chicken Burrito           553       591     5575.82\n",
       "Chips and Guacamole       479       506     2201.04\n",
       "Steak Burrito             368       386     3851.43\n",
       "Canned Soft Drink         301       351      438.75"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "favoriteItems = chipo.groupby(['item_name']).agg({'order_id' : 'count', 'quantity' : 'sum', 'item_price' : 'sum'}).sort_values('quantity', ascending=False)\n",
    "favoriteItems.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###  7. 查询订单的商品总数量"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4972"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "chipo.quantity.sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 8. 统计总收入"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "34500.160000000003"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "chipo.item_price.sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 9. 统计订单总数"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1834"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "chipo.order_id.value_counts().count()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 10. 订单平均消费"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "18.811428571428575"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "chipo.item_price.sum() / chipo.order_id.value_counts().count()\n",
    "# chipo.groupby(by=['order_id']).sum()['item_price'].mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 11. 按商品名称过滤查询数据"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "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>order_id</th>\n",
       "      <th>quantity</th>\n",
       "      <th>item_name</th>\n",
       "      <th>choice_description</th>\n",
       "      <th>item_price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>Chicken Bowl</td>\n",
       "      <td>[Tomatillo-Red Chili Salsa (Hot), [Black Beans...</td>\n",
       "      <td>16.98</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>154</th>\n",
       "      <td>70</td>\n",
       "      <td>2</td>\n",
       "      <td>Chicken Bowl</td>\n",
       "      <td>[Fresh Tomato Salsa, [Fajita Vegetables, Rice,...</td>\n",
       "      <td>17.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>282</th>\n",
       "      <td>124</td>\n",
       "      <td>2</td>\n",
       "      <td>Chicken Bowl</td>\n",
       "      <td>[Fresh Tomato Salsa, [Rice, Black Beans, Chees...</td>\n",
       "      <td>17.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>409</th>\n",
       "      <td>178</td>\n",
       "      <td>3</td>\n",
       "      <td>Chicken Bowl</td>\n",
       "      <td>[[Fresh Tomato Salsa (Mild), Tomatillo-Green C...</td>\n",
       "      <td>32.94</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>415</th>\n",
       "      <td>181</td>\n",
       "      <td>2</td>\n",
       "      <td>Chicken Bowl</td>\n",
       "      <td>[Tomatillo Red Chili Salsa]</td>\n",
       "      <td>17.50</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     order_id  quantity     item_name  \\\n",
       "4           2         2  Chicken Bowl   \n",
       "154        70         2  Chicken Bowl   \n",
       "282       124         2  Chicken Bowl   \n",
       "409       178         3  Chicken Bowl   \n",
       "415       181         2  Chicken Bowl   \n",
       "\n",
       "                                    choice_description  item_price  \n",
       "4    [Tomatillo-Red Chili Salsa (Hot), [Black Beans...       16.98  \n",
       "154  [Fresh Tomato Salsa, [Fajita Vegetables, Rice,...       17.50  \n",
       "282  [Fresh Tomato Salsa, [Rice, Black Beans, Chees...       17.50  \n",
       "409  [[Fresh Tomato Salsa (Mild), Tomatillo-Green C...       32.94  \n",
       "415                        [Tomatillo Red Chili Salsa]       17.50  "
      ]
     },
     "execution_count": 91,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "chipo[(chipo['item_name'] == 'Chicken Bowl') & (chipo['quantity'] > 1)].head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 12. 查询消费金额最大的订单信息 (3个)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "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>quantity</th>\n",
       "      <th>item_price</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>order_id</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>926</th>\n",
       "      <td>23</td>\n",
       "      <td>205.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1443</th>\n",
       "      <td>35</td>\n",
       "      <td>160.74</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1483</th>\n",
       "      <td>14</td>\n",
       "      <td>139.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          quantity  item_price\n",
       "order_id                      \n",
       "926             23      205.25\n",
       "1443            35      160.74\n",
       "1483            14      139.00"
      ]
     },
     "execution_count": 96,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "maxOrder = chipo.groupby(['order_id']).sum().sort_values('item_price', ascending=False)\n",
    "maxOrder.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 926, 1443, 1483], dtype=int64)"
      ]
     },
     "execution_count": 107,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arrOrderId = maxOrder.head(3).index[0:3].values\n",
    "arrOrderId"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>order_id</th>\n",
       "      <th>quantity</th>\n",
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       "      <th>item_price</th>\n",
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       "      <th>2304</th>\n",
       "      <td>926</td>\n",
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       "      <th>2305</th>\n",
       "      <td>926</td>\n",
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       "      <th>2306</th>\n",
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       "      <th>2307</th>\n",
       "      <td>926</td>\n",
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       "    <tr>\n",
       "      <th>2308</th>\n",
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       "      <th>2309</th>\n",
       "      <td>926</td>\n",
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       "      <td>8.75</td>\n",
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       "    <tr>\n",
       "      <th>2310</th>\n",
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       "    <tr>\n",
       "      <th>2311</th>\n",
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       "      <th>2312</th>\n",
       "      <td>926</td>\n",
       "      <td>1</td>\n",
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       "      <td>[Fresh Tomato Salsa, [Rice, Lettuce]]</td>\n",
       "      <td>8.75</td>\n",
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       "    <tr>\n",
       "      <th>2313</th>\n",
       "      <td>926</td>\n",
       "      <td>1</td>\n",
       "      <td>Chicken Bowl</td>\n",
       "      <td>[Fresh Tomato Salsa, [Rice, Cheese, Sour Cream...</td>\n",
       "      <td>8.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2314</th>\n",
       "      <td>926</td>\n",
       "      <td>1</td>\n",
       "      <td>Chicken Salad Bowl</td>\n",
       "      <td>[Roasted Chili Corn Salsa, [Rice, Sour Cream]]</td>\n",
       "      <td>8.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2315</th>\n",
       "      <td>926</td>\n",
       "      <td>1</td>\n",
       "      <td>Steak Bowl</td>\n",
       "      <td>[Fresh Tomato Salsa, [Rice, Black Beans, Chees...</td>\n",
       "      <td>9.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2316</th>\n",
       "      <td>926</td>\n",
       "      <td>1</td>\n",
       "      <td>Chicken Burrito</td>\n",
       "      <td>[Roasted Chili Corn Salsa, [Rice, Black Beans,...</td>\n",
       "      <td>8.75</td>\n",
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       "    <tr>\n",
       "      <th>2317</th>\n",
       "      <td>926</td>\n",
       "      <td>1</td>\n",
       "      <td>Steak Bowl</td>\n",
       "      <td>[Roasted Chili Corn Salsa, [Rice, Black Beans,...</td>\n",
       "      <td>9.25</td>\n",
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       "    <tr>\n",
       "      <th>2318</th>\n",
       "      <td>926</td>\n",
       "      <td>1</td>\n",
       "      <td>Chicken Bowl</td>\n",
       "      <td>[Roasted Chili Corn Salsa, [Fajita Vegetables,...</td>\n",
       "      <td>8.75</td>\n",
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       "    <tr>\n",
       "      <th>2319</th>\n",
       "      <td>926</td>\n",
       "      <td>1</td>\n",
       "      <td>Steak Bowl</td>\n",
       "      <td>[Fresh Tomato Salsa, [Rice, Cheese]]</td>\n",
       "      <td>9.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2320</th>\n",
       "      <td>926</td>\n",
       "      <td>1</td>\n",
       "      <td>Chicken Burrito</td>\n",
       "      <td>[Fresh Tomato Salsa, [Rice, Cheese, Lettuce]]</td>\n",
       "      <td>8.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2321</th>\n",
       "      <td>926</td>\n",
       "      <td>1</td>\n",
       "      <td>Chicken Bowl</td>\n",
       "      <td>[Roasted Chili Corn Salsa, [Rice, Pinto Beans,...</td>\n",
       "      <td>8.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2322</th>\n",
       "      <td>926</td>\n",
       "      <td>1</td>\n",
       "      <td>Chicken Bowl</td>\n",
       "      <td>[Roasted Chili Corn Salsa, [Rice, Cheese]]</td>\n",
       "      <td>8.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2323</th>\n",
       "      <td>926</td>\n",
       "      <td>1</td>\n",
       "      <td>Barbacoa Burrito</td>\n",
       "      <td>[Fresh Tomato Salsa, [Rice, Black Beans, Chees...</td>\n",
       "      <td>9.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2324</th>\n",
       "      <td>926</td>\n",
       "      <td>1</td>\n",
       "      <td>Chicken Burrito</td>\n",
       "      <td>[Tomatillo Red Chili Salsa, [Rice, Cheese, Sou...</td>\n",
       "      <td>8.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2325</th>\n",
       "      <td>926</td>\n",
       "      <td>1</td>\n",
       "      <td>Steak Bowl</td>\n",
       "      <td>[Tomatillo Red Chili Salsa, [Rice, Black Beans...</td>\n",
       "      <td>9.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2326</th>\n",
       "      <td>926</td>\n",
       "      <td>1</td>\n",
       "      <td>Veggie Bowl</td>\n",
       "      <td>[Roasted Chili Corn Salsa, [Rice, Black Beans,...</td>\n",
       "      <td>8.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3598</th>\n",
       "      <td>1443</td>\n",
       "      <td>15</td>\n",
       "      <td>Chips and Fresh Tomato Salsa</td>\n",
       "      <td>NaN</td>\n",
       "      <td>44.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3599</th>\n",
       "      <td>1443</td>\n",
       "      <td>7</td>\n",
       "      <td>Bottled Water</td>\n",
       "      <td>NaN</td>\n",
       "      <td>10.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3600</th>\n",
       "      <td>1443</td>\n",
       "      <td>1</td>\n",
       "      <td>6 Pack Soft Drink</td>\n",
       "      <td>[Coke]</td>\n",
       "      <td>6.49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3601</th>\n",
       "      <td>1443</td>\n",
       "      <td>3</td>\n",
       "      <td>Veggie Burrito</td>\n",
       "      <td>[Fresh Tomato Salsa, [Fajita Vegetables, Rice,...</td>\n",
       "      <td>33.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3602</th>\n",
       "      <td>1443</td>\n",
       "      <td>4</td>\n",
       "      <td>Chicken Burrito</td>\n",
       "      <td>[Fresh Tomato Salsa, [Rice, Black Beans, Chees...</td>\n",
       "      <td>35.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3603</th>\n",
       "      <td>1443</td>\n",
       "      <td>3</td>\n",
       "      <td>Steak Burrito</td>\n",
       "      <td>[Fresh Tomato Salsa, [Rice, Black Beans, Chees...</td>\n",
       "      <td>27.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3604</th>\n",
       "      <td>1443</td>\n",
       "      <td>2</td>\n",
       "      <td>Bottled Water</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3700</th>\n",
       "      <td>1483</td>\n",
       "      <td>1</td>\n",
       "      <td>Chicken Bowl</td>\n",
       "      <td>[Roasted Chili Corn Salsa, [Rice, Black Beans,...</td>\n",
       "      <td>8.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3701</th>\n",
       "      <td>1483</td>\n",
       "      <td>1</td>\n",
       "      <td>Steak Bowl</td>\n",
       "      <td>[Roasted Chili Corn Salsa, [Fajita Vegetables,...</td>\n",
       "      <td>11.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3702</th>\n",
       "      <td>1483</td>\n",
       "      <td>1</td>\n",
       "      <td>Chicken Burrito</td>\n",
       "      <td>[Tomatillo Red Chili Salsa, [Fajita Vegetables...</td>\n",
       "      <td>8.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3703</th>\n",
       "      <td>1483</td>\n",
       "      <td>1</td>\n",
       "      <td>Steak Bowl</td>\n",
       "      <td>[Roasted Chili Corn Salsa, [Fajita Vegetables,...</td>\n",
       "      <td>9.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3704</th>\n",
       "      <td>1483</td>\n",
       "      <td>1</td>\n",
       "      <td>Steak Burrito</td>\n",
       "      <td>[Roasted Chili Corn Salsa, [Rice, Black Beans,...</td>\n",
       "      <td>11.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3705</th>\n",
       "      <td>1483</td>\n",
       "      <td>1</td>\n",
       "      <td>Steak Soft Tacos</td>\n",
       "      <td>[Fresh Tomato Salsa, [Fajita Vegetables, Black...</td>\n",
       "      <td>9.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3706</th>\n",
       "      <td>1483</td>\n",
       "      <td>1</td>\n",
       "      <td>Chicken Salad Bowl</td>\n",
       "      <td>[Roasted Chili Corn Salsa, [Pinto Beans, Chees...</td>\n",
       "      <td>8.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3707</th>\n",
       "      <td>1483</td>\n",
       "      <td>1</td>\n",
       "      <td>Steak Bowl</td>\n",
       "      <td>[Roasted Chili Corn Salsa, [Black Beans, Chees...</td>\n",
       "      <td>9.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3708</th>\n",
       "      <td>1483</td>\n",
       "      <td>1</td>\n",
       "      <td>Chicken Bowl</td>\n",
       "      <td>[Fresh Tomato Salsa, [Rice, Black Beans, Sour ...</td>\n",
       "      <td>8.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3709</th>\n",
       "      <td>1483</td>\n",
       "      <td>1</td>\n",
       "      <td>Chicken Bowl</td>\n",
       "      <td>[Roasted Chili Corn Salsa, [Rice, Black Beans,...</td>\n",
       "      <td>11.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3710</th>\n",
       "      <td>1483</td>\n",
       "      <td>1</td>\n",
       "      <td>Steak Soft Tacos</td>\n",
       "      <td>[Fresh Tomato Salsa, Guacamole]</td>\n",
       "      <td>11.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3711</th>\n",
       "      <td>1483</td>\n",
       "      <td>1</td>\n",
       "      <td>Carnitas Bowl</td>\n",
       "      <td>[Fresh Tomato Salsa, [Fajita Vegetables, Rice,...</td>\n",
       "      <td>9.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3712</th>\n",
       "      <td>1483</td>\n",
       "      <td>1</td>\n",
       "      <td>Chicken Salad Bowl</td>\n",
       "      <td>[Roasted Chili Corn Salsa, [Black Beans, Chees...</td>\n",
       "      <td>8.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3713</th>\n",
       "      <td>1483</td>\n",
       "      <td>1</td>\n",
       "      <td>Steak Bowl</td>\n",
       "      <td>[Roasted Chili Corn Salsa, [Rice, Black Beans,...</td>\n",
       "      <td>11.75</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      order_id  quantity                     item_name  \\\n",
       "2304       926         1                 Steak Burrito   \n",
       "2305       926         1                  Chicken Bowl   \n",
       "2306       926         1                  Chicken Bowl   \n",
       "2307       926         1                  Chicken Bowl   \n",
       "2308       926         1                    Steak Bowl   \n",
       "2309       926         1                  Chicken Bowl   \n",
       "2310       926         1                 Steak Burrito   \n",
       "2311       926         1               Chicken Burrito   \n",
       "2312       926         1                  Chicken Bowl   \n",
       "2313       926         1                  Chicken Bowl   \n",
       "2314       926         1            Chicken Salad Bowl   \n",
       "2315       926         1                    Steak Bowl   \n",
       "2316       926         1               Chicken Burrito   \n",
       "2317       926         1                    Steak Bowl   \n",
       "2318       926         1                  Chicken Bowl   \n",
       "2319       926         1                    Steak Bowl   \n",
       "2320       926         1               Chicken Burrito   \n",
       "2321       926         1                  Chicken Bowl   \n",
       "2322       926         1                  Chicken Bowl   \n",
       "2323       926         1              Barbacoa Burrito   \n",
       "2324       926         1               Chicken Burrito   \n",
       "2325       926         1                    Steak Bowl   \n",
       "2326       926         1                   Veggie Bowl   \n",
       "3598      1443        15  Chips and Fresh Tomato Salsa   \n",
       "3599      1443         7                 Bottled Water   \n",
       "3600      1443         1             6 Pack Soft Drink   \n",
       "3601      1443         3                Veggie Burrito   \n",
       "3602      1443         4               Chicken Burrito   \n",
       "3603      1443         3                 Steak Burrito   \n",
       "3604      1443         2                 Bottled Water   \n",
       "3700      1483         1                  Chicken Bowl   \n",
       "3701      1483         1                    Steak Bowl   \n",
       "3702      1483         1               Chicken Burrito   \n",
       "3703      1483         1                    Steak Bowl   \n",
       "3704      1483         1                 Steak Burrito   \n",
       "3705      1483         1              Steak Soft Tacos   \n",
       "3706      1483         1            Chicken Salad Bowl   \n",
       "3707      1483         1                    Steak Bowl   \n",
       "3708      1483         1                  Chicken Bowl   \n",
       "3709      1483         1                  Chicken Bowl   \n",
       "3710      1483         1              Steak Soft Tacos   \n",
       "3711      1483         1                 Carnitas Bowl   \n",
       "3712      1483         1            Chicken Salad Bowl   \n",
       "3713      1483         1                    Steak Bowl   \n",
       "\n",
       "                                     choice_description  item_price  \n",
       "2304  [Fresh Tomato Salsa, [Rice, Sour Cream, Lettuce]]        9.25  \n",
       "2305  [Roasted Chili Corn Salsa, [Fajita Vegetables,...        8.75  \n",
       "2306  [Roasted Chili Corn Salsa, [Fajita Vegetables,...        8.75  \n",
       "2307  [Roasted Chili Corn Salsa, [Fajita Vegetables,...        8.75  \n",
       "2308  [Fresh Tomato Salsa, [Rice, Black Beans, Lettu...        9.25  \n",
       "2309  [Fresh Tomato Salsa, [Rice, Black Beans, Chees...        8.75  \n",
       "2310  [Roasted Chili Corn Salsa, [Rice, Cheese, Sour...        9.25  \n",
       "2311  [Fresh Tomato Salsa, [Rice, Black Beans, Chees...        8.75  \n",
       "2312              [Fresh Tomato Salsa, [Rice, Lettuce]]        8.75  \n",
       "2313  [Fresh Tomato Salsa, [Rice, Cheese, Sour Cream...        8.75  \n",
       "2314     [Roasted Chili Corn Salsa, [Rice, Sour Cream]]        8.75  \n",
       "2315  [Fresh Tomato Salsa, [Rice, Black Beans, Chees...        9.25  \n",
       "2316  [Roasted Chili Corn Salsa, [Rice, Black Beans,...        8.75  \n",
       "2317  [Roasted Chili Corn Salsa, [Rice, Black Beans,...        9.25  \n",
       "2318  [Roasted Chili Corn Salsa, [Fajita Vegetables,...        8.75  \n",
       "2319               [Fresh Tomato Salsa, [Rice, Cheese]]        9.25  \n",
       "2320      [Fresh Tomato Salsa, [Rice, Cheese, Lettuce]]        8.75  \n",
       "2321  [Roasted Chili Corn Salsa, [Rice, Pinto Beans,...        8.75  \n",
       "2322         [Roasted Chili Corn Salsa, [Rice, Cheese]]        8.75  \n",
       "2323  [Fresh Tomato Salsa, [Rice, Black Beans, Chees...        9.25  \n",
       "2324  [Tomatillo Red Chili Salsa, [Rice, Cheese, Sou...        8.75  \n",
       "2325  [Tomatillo Red Chili Salsa, [Rice, Black Beans...        9.25  \n",
       "2326  [Roasted Chili Corn Salsa, [Rice, Black Beans,...        8.75  \n",
       "3598                                                NaN       44.25  \n",
       "3599                                                NaN       10.50  \n",
       "3600                                             [Coke]        6.49  \n",
       "3601  [Fresh Tomato Salsa, [Fajita Vegetables, Rice,...       33.75  \n",
       "3602  [Fresh Tomato Salsa, [Rice, Black Beans, Chees...       35.00  \n",
       "3603  [Fresh Tomato Salsa, [Rice, Black Beans, Chees...       27.75  \n",
       "3604                                                NaN        3.00  \n",
       "3700  [Roasted Chili Corn Salsa, [Rice, Black Beans,...        8.75  \n",
       "3701  [Roasted Chili Corn Salsa, [Fajita Vegetables,...       11.75  \n",
       "3702  [Tomatillo Red Chili Salsa, [Fajita Vegetables...        8.75  \n",
       "3703  [Roasted Chili Corn Salsa, [Fajita Vegetables,...        9.25  \n",
       "3704  [Roasted Chili Corn Salsa, [Rice, Black Beans,...       11.75  \n",
       "3705  [Fresh Tomato Salsa, [Fajita Vegetables, Black...        9.25  \n",
       "3706  [Roasted Chili Corn Salsa, [Pinto Beans, Chees...        8.75  \n",
       "3707  [Roasted Chili Corn Salsa, [Black Beans, Chees...        9.25  \n",
       "3708  [Fresh Tomato Salsa, [Rice, Black Beans, Sour ...        8.75  \n",
       "3709  [Roasted Chili Corn Salsa, [Rice, Black Beans,...       11.25  \n",
       "3710                    [Fresh Tomato Salsa, Guacamole]       11.75  \n",
       "3711  [Fresh Tomato Salsa, [Fajita Vegetables, Rice,...        9.25  \n",
       "3712  [Roasted Chili Corn Salsa, [Black Beans, Chees...        8.75  \n",
       "3713  [Roasted Chili Corn Salsa, [Rice, Black Beans,...       11.75  "
      ]
     },
     "execution_count": 108,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "chipo[chipo['order_id'].isin(arrOrderId)]"
   ]
  }
 ],
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