4. chipotle.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "\n",
    "plt.style.use('seaborn')"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "csv: comma seperated values의 약자로 콤마로 열이 구분되어 있다\n",
    "tsv: tab~~\n",
    "\n",
    "|이름|설명|\n",
    "|-|-|\n",
    "|read_csv|csv파일을 불러옴|\n",
    "|sep|구분자 설정|\n",
    "|set_option|옵션 설정|\n",
    "|display.max_columns|표시 되는 최대 행(작으면 ... 으로 표시 됨)|"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%% md\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "outputs": [],
   "source": [
    "df = pd.read_csv(\"./chipotle.tsv\", sep=\"\\t\")  # 구분자를 tab으로\n",
    "pd.set_option('display.max_columns', 3)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "shape: dataframe의 모양 (행, 열)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%% md\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "outputs": [
    {
     "data": {
      "text/plain": "(4622, 5)"
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "## info\n",
    "전반적인 정보를 보여준다.\n",
    "\n",
    "여기서 결측값(null)을 확인해야 한다."
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%% md\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "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",
      " #   Column              Non-Null Count  Dtype \n",
      "---  ------              --------------  ----- \n",
      " 0   order_id            4622 non-null   int64 \n",
      " 1   quantity            4622 non-null   int64 \n",
      " 2   item_name           4622 non-null   object\n",
      " 3   choice_description  3376 non-null   object\n",
      " 4   item_price          4622 non-null   object\n",
      "dtypes: int64(2), object(3)\n",
      "memory usage: 180.7+ KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "이 외에도 isnull이라는 것을 사용해서 결측값을 확인할 수 있다.\n",
    "\n",
    "결측값을 채울 때는 fillna를 사용한다.\n",
    "꼭!!! 결측값을 채우고 나서 대입해야 값이 나온다."
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%% md\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "outputs": [],
   "source": [
    "df.isnull().sum()\n",
    "df['choice_description'] = df['choice_description'].fillna('[]')"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "## head\n",
    "위에서 부터 다섯줄"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%% md\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "outputs": [
    {
     "data": {
      "text/plain": "   order_id  ...  item_price\n0         1  ...      $2.39 \n1         1  ...      $3.39 \n2         1  ...      $3.39 \n3         1  ...      $2.39 \n4         2  ...     $16.98 \n\n[5 rows x 5 columns]",
      "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>...</th>\n      <th>item_price</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>1</td>\n      <td>...</td>\n      <td>$2.39</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>1</td>\n      <td>...</td>\n      <td>$3.39</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>1</td>\n      <td>...</td>\n      <td>$3.39</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>1</td>\n      <td>...</td>\n      <td>$2.39</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>2</td>\n      <td>...</td>\n      <td>$16.98</td>\n    </tr>\n  </tbody>\n</table>\n<p>5 rows × 5 columns</p>\n</div>"
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "outputs": [
    {
     "data": {
      "text/plain": "   order_id  quantity                              item_name  \\\n0         1         1           Chips and Fresh Tomato Salsa   \n1         1         1                                   Izze   \n2         1         1                       Nantucket Nectar   \n3         1         1  Chips and Tomatillo-Green Chili Salsa   \n4         2         2                           Chicken Bowl   \n\n                                  choice_description item_price  \n0                                                 []     $2.39   \n1                                       [Clementine]     $3.39   \n2                                            [Apple]     $3.39   \n3                                                 []     $2.39   \n4  [Tomatillo-Red Chili Salsa (Hot), [Black Beans...    $16.98   ",
      "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>0</th>\n      <td>1</td>\n      <td>1</td>\n      <td>Chips and Fresh Tomato Salsa</td>\n      <td>[]</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>[]</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  </tbody>\n</table>\n</div>"
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.set_option('display.max_columns', 100)\n",
    "df.head()"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "## describe\n",
    "기초 통계량.\n",
    "numeric(int, float) 칼럼에 대해서만 알려준다."
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%% md\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "outputs": [
    {
     "data": {
      "text/plain": "          order_id     quantity\ncount  4622.000000  4622.000000\nmean    927.254868     1.075725\nstd     528.890796     0.410186\nmin       1.000000     1.000000\n25%     477.250000     1.000000\n50%     926.000000     1.000000\n75%    1393.000000     1.000000\nmax    1834.000000    15.000000",
      "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    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>count</th>\n      <td>4622.000000</td>\n      <td>4622.000000</td>\n    </tr>\n    <tr>\n      <th>mean</th>\n      <td>927.254868</td>\n      <td>1.075725</td>\n    </tr>\n    <tr>\n      <th>std</th>\n      <td>528.890796</td>\n      <td>0.410186</td>\n    </tr>\n    <tr>\n      <th>min</th>\n      <td>1.000000</td>\n      <td>1.000000</td>\n    </tr>\n    <tr>\n      <th>25%</th>\n      <td>477.250000</td>\n      <td>1.000000</td>\n    </tr>\n    <tr>\n      <th>50%</th>\n      <td>926.000000</td>\n      <td>1.000000</td>\n    </tr>\n    <tr>\n      <th>75%</th>\n      <td>1393.000000</td>\n      <td>1.000000</td>\n    </tr>\n    <tr>\n      <th>max</th>\n      <td>1834.000000</td>\n      <td>15.000000</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "## 데이터 전처리\n",
    "여기서 item_price는 사실상 numeric한 변수인데, `$` 때문에 문자로 취급 됐다\n",
    "\n",
    "그리고 order_id는 numeric해봤자 쓸모가 없기 때문에 object로 다시 바꿔준다."
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%% md\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "outputs": [],
   "source": [
    "df['order_id'] = df['order_id'].astype(str)\n",
    "df['item_price'] = df['item_price'].apply(lambda x: float(x[1:]))"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "outputs": [
    {
     "data": {
      "text/plain": "          quantity   item_price\ncount  4622.000000  4622.000000\nmean      1.075725     7.464336\nstd       0.410186     4.245557\nmin       1.000000     1.090000\n25%       1.000000     3.390000\n50%       1.000000     8.750000\n75%       1.000000     9.250000\nmax      15.000000    44.250000",
      "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  </thead>\n  <tbody>\n    <tr>\n      <th>count</th>\n      <td>4622.000000</td>\n      <td>4622.000000</td>\n    </tr>\n    <tr>\n      <th>mean</th>\n      <td>1.075725</td>\n      <td>7.464336</td>\n    </tr>\n    <tr>\n      <th>std</th>\n      <td>0.410186</td>\n      <td>4.245557</td>\n    </tr>\n    <tr>\n      <th>min</th>\n      <td>1.000000</td>\n      <td>1.090000</td>\n    </tr>\n    <tr>\n      <th>25%</th>\n      <td>1.000000</td>\n      <td>3.390000</td>\n    </tr>\n    <tr>\n      <th>50%</th>\n      <td>1.000000</td>\n      <td>8.750000</td>\n    </tr>\n    <tr>\n      <th>75%</th>\n      <td>1.000000</td>\n      <td>9.250000</td>\n    </tr>\n    <tr>\n      <th>max</th>\n      <td>15.000000</td>\n      <td>44.250000</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "outputs": [
    {
     "data": {
      "text/plain": "  order_id  quantity                              item_name  \\\n0        1         1           Chips and Fresh Tomato Salsa   \n1        1         1                                   Izze   \n2        1         1                       Nantucket Nectar   \n3        1         1  Chips and Tomatillo-Green Chili Salsa   \n4        2         2                           Chicken Bowl   \n\n                                  choice_description  item_price  \n0                                                 []        2.39  \n1                                       [Clementine]        3.39  \n2                                            [Apple]        3.39  \n3                                                 []        2.39  \n4  [Tomatillo-Red Chili Salsa (Hot), [Black Beans...       16.98  ",
      "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>0</th>\n      <td>1</td>\n      <td>1</td>\n      <td>Chips and Fresh Tomato Salsa</td>\n      <td>[]</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>[]</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  </tbody>\n</table>\n</div>"
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "* 각 행을 선택했을 때 반환 타입: Series\n",
    "* 여러 행을 선택했을 때 반환 타입: DataFrame"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%% md\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "outputs": [
    {
     "data": {
      "text/plain": "pandas.core.series.Series"
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(df['order_id'])"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "outputs": [
    {
     "data": {
      "text/plain": "pandas.core.frame.DataFrame"
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(df[['order_id', 'item_price']])"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "outputs": [
    {
     "data": {
      "text/plain": "     order_id  item_price\n0           1        2.39\n1           1        3.39\n2           1        3.39\n3           1        2.39\n4           2       16.98\n...       ...         ...\n4617     1833       11.75\n4618     1833       11.75\n4619     1834       11.25\n4620     1834        8.75\n4621     1834        8.75\n\n[4622 rows x 2 columns]",
      "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>item_price</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>1</td>\n      <td>2.39</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>1</td>\n      <td>3.39</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>1</td>\n      <td>3.39</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>1</td>\n      <td>2.39</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>2</td>\n      <td>16.98</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>4617</th>\n      <td>1833</td>\n      <td>11.75</td>\n    </tr>\n    <tr>\n      <th>4618</th>\n      <td>1833</td>\n      <td>11.75</td>\n    </tr>\n    <tr>\n      <th>4619</th>\n      <td>1834</td>\n      <td>11.25</td>\n    </tr>\n    <tr>\n      <th>4620</th>\n      <td>1834</td>\n      <td>8.75</td>\n    </tr>\n    <tr>\n      <th>4621</th>\n      <td>1834</td>\n      <td>8.75</td>\n    </tr>\n  </tbody>\n</table>\n<p>4622 rows × 2 columns</p>\n</div>"
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[['order_id', 'item_price']]"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "outputs": [
    {
     "data": {
      "text/plain": "     order_id  quantity                              item_name  \\\n0           1         1           Chips and Fresh Tomato Salsa   \n1           1         1                                   Izze   \n2           1         1                       Nantucket Nectar   \n3           1         1  Chips and Tomatillo-Green Chili Salsa   \n4           2         2                           Chicken Bowl   \n...       ...       ...                                    ...   \n4617     1833         1                          Steak Burrito   \n4618     1833         1                          Steak Burrito   \n4619     1834         1                     Chicken Salad Bowl   \n4620     1834         1                     Chicken Salad Bowl   \n4621     1834         1                     Chicken Salad Bowl   \n\n                                     choice_description  item_price  \n0                                                    []        2.39  \n1                                          [Clementine]        3.39  \n2                                               [Apple]        3.39  \n3                                                    []        2.39  \n4     [Tomatillo-Red Chili Salsa (Hot), [Black Beans...       16.98  \n...                                                 ...         ...  \n4617  [Fresh Tomato Salsa, [Rice, Black Beans, Sour ...       11.75  \n4618  [Fresh Tomato Salsa, [Rice, Sour Cream, Cheese...       11.75  \n4619  [Fresh Tomato Salsa, [Fajita Vegetables, Pinto...       11.25  \n4620  [Fresh Tomato Salsa, [Fajita Vegetables, Lettu...        8.75  \n4621  [Fresh Tomato Salsa, [Fajita Vegetables, Pinto...        8.75  \n\n[4622 rows x 5 columns]",
      "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>0</th>\n      <td>1</td>\n      <td>1</td>\n      <td>Chips and Fresh Tomato Salsa</td>\n      <td>[]</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>[]</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>...</th>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>4617</th>\n      <td>1833</td>\n      <td>1</td>\n      <td>Steak Burrito</td>\n      <td>[Fresh Tomato Salsa, [Rice, Black Beans, Sour ...</td>\n      <td>11.75</td>\n    </tr>\n    <tr>\n      <th>4618</th>\n      <td>1833</td>\n      <td>1</td>\n      <td>Steak Burrito</td>\n      <td>[Fresh Tomato Salsa, [Rice, Sour Cream, Cheese...</td>\n      <td>11.75</td>\n    </tr>\n    <tr>\n      <th>4619</th>\n      <td>1834</td>\n      <td>1</td>\n      <td>Chicken Salad Bowl</td>\n      <td>[Fresh Tomato Salsa, [Fajita Vegetables, Pinto...</td>\n      <td>11.25</td>\n    </tr>\n    <tr>\n      <th>4620</th>\n      <td>1834</td>\n      <td>1</td>\n      <td>Chicken Salad Bowl</td>\n      <td>[Fresh Tomato Salsa, [Fajita Vegetables, Lettu...</td>\n      <td>8.75</td>\n    </tr>\n    <tr>\n      <th>4621</th>\n      <td>1834</td>\n      <td>1</td>\n      <td>Chicken Salad Bowl</td>\n      <td>[Fresh Tomato Salsa, [Fajita Vegetables, Pinto...</td>\n      <td>8.75</td>\n    </tr>\n  </tbody>\n</table>\n<p>4622 rows × 5 columns</p>\n</div>"
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "## Series.unique()\n",
    "반환 타입: ndarray"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%% md\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "outputs": [
    {
     "data": {
      "text/plain": "array(['1', '2', '3', ..., '1832', '1833', '1834'], dtype=object)"
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['order_id'].unique()"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "outputs": [
    {
     "data": {
      "text/plain": "1834"
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 주문 개수\n",
    "df['order_id'].unique().size"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "outputs": [
    {
     "data": {
      "text/plain": "array([1, 2, 3, 4, 5])"
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.Series([1, 2, 3, 3, 3, 4, 4, 5\n",
    "           #%%"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "outputs": [],
   "source": [],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "outputs": [],
   "source": [],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "outputs": [],
   "source": [],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "outputs": [],
   "source": [
    "f.df.df.]).unique()"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "outputs": [
    {
     "data": {
      "text/plain": "array(['Chips and Fresh Tomato Salsa', 'Izze', 'Nantucket Nectar',\n       'Chips and Tomatillo-Green Chili Salsa', 'Chicken Bowl',\n       'Side of Chips', 'Steak Burrito', 'Steak Soft Tacos',\n       'Chips and Guacamole', 'Chicken Crispy Tacos',\n       'Chicken Soft Tacos', 'Chicken Burrito', 'Canned Soda',\n       'Barbacoa Burrito', 'Carnitas Burrito', 'Carnitas Bowl',\n       'Bottled Water', 'Chips and Tomatillo Green Chili Salsa',\n       'Barbacoa Bowl', 'Chips', 'Chicken Salad Bowl', 'Steak Bowl',\n       'Barbacoa Soft Tacos', 'Veggie Burrito', 'Veggie Bowl',\n       'Steak Crispy Tacos', 'Chips and Tomatillo Red Chili Salsa',\n       'Barbacoa Crispy Tacos', 'Veggie Salad Bowl',\n       'Chips and Roasted Chili-Corn Salsa',\n       'Chips and Roasted Chili Corn Salsa', 'Carnitas Soft Tacos',\n       'Chicken Salad', 'Canned Soft Drink', 'Steak Salad Bowl',\n       '6 Pack Soft Drink', 'Chips and Tomatillo-Red Chili Salsa', 'Bowl',\n       'Burrito', 'Crispy Tacos', 'Carnitas Crispy Tacos', 'Steak Salad',\n       'Chips and Mild Fresh Tomato Salsa', 'Veggie Soft Tacos',\n       'Carnitas Salad Bowl', 'Barbacoa Salad Bowl', 'Salad',\n       'Veggie Crispy Tacos', 'Veggie Salad', 'Carnitas Salad'],\n      dtype=object)"
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['item_name'].unique()"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "## Series.value_counts()\n",
    "반환 타입: Series"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%% md\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "outputs": [
    {
     "data": {
      "text/plain": "Chicken Bowl                             726\nChicken Burrito                          553\nChips and Guacamole                      479\nSteak Burrito                            368\nCanned Soft Drink                        301\nSteak Bowl                               211\nChips                                    211\nBottled Water                            162\nChicken Soft Tacos                       115\nChicken Salad Bowl                       110\nChips and Fresh Tomato Salsa             110\nCanned Soda                              104\nSide of Chips                            101\nVeggie Burrito                            95\nBarbacoa Burrito                          91\nVeggie Bowl                               85\nCarnitas Bowl                             68\nBarbacoa Bowl                             66\nCarnitas Burrito                          59\nSteak Soft Tacos                          55\n6 Pack Soft Drink                         54\nChips and Tomatillo Red Chili Salsa       48\nChicken Crispy Tacos                      47\nChips and Tomatillo Green Chili Salsa     43\nCarnitas Soft Tacos                       40\nSteak Crispy Tacos                        35\nChips and Tomatillo-Green Chili Salsa     31\nSteak Salad Bowl                          29\nNantucket Nectar                          27\nBarbacoa Soft Tacos                       25\nChips and Roasted Chili Corn Salsa        22\nChips and Tomatillo-Red Chili Salsa       20\nIzze                                      20\nChips and Roasted Chili-Corn Salsa        18\nVeggie Salad Bowl                         18\nBarbacoa Crispy Tacos                     11\nBarbacoa Salad Bowl                       10\nChicken Salad                              9\nVeggie Soft Tacos                          7\nCarnitas Crispy Tacos                      7\nBurrito                                    6\nVeggie Salad                               6\nCarnitas Salad Bowl                        6\nSteak Salad                                4\nBowl                                       2\nSalad                                      2\nCrispy Tacos                               2\nVeggie Crispy Tacos                        1\nCarnitas Salad                             1\nChips and Mild Fresh Tomato Salsa          1\nName: item_name, dtype: int64"
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['item_name'].value_counts()"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "outputs": [
    {
     "data": {
      "text/plain": "50"
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['item_name'].unique().size\n"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "outputs": [
    {
     "data": {
      "text/plain": "50"
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['item_name'].value_counts().size"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "outputs": [
    {
     "data": {
      "text/plain": "array(['6 Pack Soft Drink', 'Barbacoa Bowl', 'Barbacoa Burrito',\n       'Barbacoa Crispy Tacos', 'Barbacoa Salad Bowl',\n       'Barbacoa Soft Tacos', 'Bottled Water', 'Bowl', 'Burrito',\n       'Canned Soda', 'Canned Soft Drink', 'Carnitas Bowl',\n       'Carnitas Burrito', 'Carnitas Crispy Tacos', 'Carnitas Salad',\n       'Carnitas Salad Bowl', 'Carnitas Soft Tacos', 'Chicken Bowl',\n       'Chicken Burrito', 'Chicken Crispy Tacos', 'Chicken Salad',\n       'Chicken Salad Bowl', 'Chicken Soft Tacos', 'Chips',\n       'Chips and Fresh Tomato Salsa', 'Chips and Guacamole',\n       'Chips and Mild Fresh Tomato Salsa',\n       'Chips and Roasted Chili Corn Salsa',\n       'Chips and Roasted Chili-Corn Salsa',\n       'Chips and Tomatillo Green Chili Salsa',\n       'Chips and Tomatillo Red Chili Salsa',\n       'Chips and Tomatillo-Green Chili Salsa',\n       'Chips and Tomatillo-Red Chili Salsa', 'Crispy Tacos', 'Izze',\n       'Nantucket Nectar', 'Salad', 'Side of Chips', 'Steak Bowl',\n       'Steak Burrito', 'Steak Crispy Tacos', 'Steak Salad',\n       'Steak Salad Bowl', 'Steak Soft Tacos', 'Veggie Bowl',\n       'Veggie Burrito', 'Veggie Crispy Tacos', 'Veggie Salad',\n       'Veggie Salad Bowl', 'Veggie Soft Tacos'], dtype=object)"
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "unique = df['item_name'].unique()\n",
    "unique.sort()\n",
    "unique"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "outputs": [
    {
     "data": {
      "text/plain": "array(['6 Pack Soft Drink', 'Barbacoa Bowl', 'Barbacoa Burrito',\n       'Barbacoa Crispy Tacos', 'Barbacoa Salad Bowl',\n       'Barbacoa Soft Tacos', 'Bottled Water', 'Bowl', 'Burrito',\n       'Canned Soda', 'Canned Soft Drink', 'Carnitas Bowl',\n       'Carnitas Burrito', 'Carnitas Crispy Tacos', 'Carnitas Salad',\n       'Carnitas Salad Bowl', 'Carnitas Soft Tacos', 'Chicken Bowl',\n       'Chicken Burrito', 'Chicken Crispy Tacos', 'Chicken Salad',\n       'Chicken Salad Bowl', 'Chicken Soft Tacos', 'Chips',\n       'Chips and Fresh Tomato Salsa', 'Chips and Guacamole',\n       'Chips and Mild Fresh Tomato Salsa',\n       'Chips and Roasted Chili Corn Salsa',\n       'Chips and Roasted Chili-Corn Salsa',\n       'Chips and Tomatillo Green Chili Salsa',\n       'Chips and Tomatillo Red Chili Salsa',\n       'Chips and Tomatillo-Green Chili Salsa',\n       'Chips and Tomatillo-Red Chili Salsa', 'Crispy Tacos', 'Izze',\n       'Nantucket Nectar', 'Salad', 'Side of Chips', 'Steak Bowl',\n       'Steak Burrito', 'Steak Crispy Tacos', 'Steak Salad',\n       'Steak Salad Bowl', 'Steak Soft Tacos', 'Veggie Bowl',\n       'Veggie Burrito', 'Veggie Crispy Tacos', 'Veggie Salad',\n       'Veggie Salad Bowl', 'Veggie Soft Tacos'], dtype=object)"
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "value_counts = df['item_name'].value_counts().index.to_numpy()\n",
    "value_counts.sort()\n",
    "value_counts"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "outputs": [
    {
     "data": {
      "text/plain": "a    1\nb    2\nc    3\ndtype: int64"
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 들어가는 데이터는 1차원, index는 무조건 숫자가 아니다.\n",
    "pd.Series([1, 2, 3], index=['a', 'b', 'c'])"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "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 ('Steak Bowl', 211),\n ('Chips', 211),\n ('Bottled Water', 162),\n ('Chicken Soft Tacos', 115),\n ('Chicken Salad Bowl', 110),\n ('Chips and Fresh Tomato Salsa', 110),\n ('Canned Soda', 104),\n ('Side of Chips', 101),\n ('Veggie Burrito', 95),\n ('Barbacoa Burrito', 91),\n ('Veggie Bowl', 85),\n ('Carnitas Bowl', 68),\n ('Barbacoa Bowl', 66),\n ('Carnitas Burrito', 59),\n ('Steak Soft Tacos', 55),\n ('6 Pack Soft Drink', 54),\n ('Chips and Tomatillo Red Chili Salsa', 48),\n ('Chicken Crispy Tacos', 47),\n ('Chips and Tomatillo Green Chili Salsa', 43),\n ('Carnitas Soft Tacos', 40),\n ('Steak Crispy Tacos', 35),\n ('Chips and Tomatillo-Green Chili Salsa', 31),\n ('Steak Salad Bowl', 29),\n ('Nantucket Nectar', 27),\n ('Barbacoa Soft Tacos', 25),\n ('Chips and Roasted Chili Corn Salsa', 22),\n ('Chips and Tomatillo-Red Chili Salsa', 20),\n ('Izze', 20),\n ('Chips and Roasted Chili-Corn Salsa', 18),\n ('Veggie Salad Bowl', 18),\n ('Barbacoa Crispy Tacos', 11),\n ('Barbacoa Salad Bowl', 10),\n ('Chicken Salad', 9),\n ('Veggie Soft Tacos', 7),\n ('Carnitas Crispy Tacos', 7),\n ('Burrito', 6),\n ('Veggie Salad', 6),\n ('Carnitas Salad Bowl', 6),\n ('Steak Salad', 4),\n ('Bowl', 2),\n ('Salad', 2),\n ('Crispy Tacos', 2),\n ('Veggie Crispy Tacos', 1),\n ('Carnitas Salad', 1),\n ('Chips and Mild Fresh Tomato Salsa', 1)]"
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "list(df['item_name'].value_counts().iteritems())"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "### enumerate: 파이썬 내장 함수\n",
    "enumerate(반복 가능한 객체[, 시작 index])"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%% md\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "outputs": [
    {
     "data": {
      "text/plain": "[(1, 'a'), (2, 'b'), (3, 'c'), (4, 'd'), (5, 'e'), (6, 'f'), (7, 'g')]"
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "list(enumerate(\"abcdefg\", 1))"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "outputs": [
    {
     "data": {
      "text/plain": "[(1, ('Chicken Bowl', 726)),\n (2, ('Chicken Burrito', 553)),\n (3, ('Chips and Guacamole', 479)),\n (4, ('Steak Burrito', 368)),\n (5, ('Canned Soft Drink', 301)),\n (6, ('Steak Bowl', 211)),\n (7, ('Chips', 211)),\n (8, ('Bottled Water', 162)),\n (9, ('Chicken Soft Tacos', 115)),\n (10, ('Chicken Salad Bowl', 110)),\n (11, ('Chips and Fresh Tomato Salsa', 110)),\n (12, ('Canned Soda', 104)),\n (13, ('Side of Chips', 101)),\n (14, ('Veggie Burrito', 95)),\n (15, ('Barbacoa Burrito', 91)),\n (16, ('Veggie Bowl', 85)),\n (17, ('Carnitas Bowl', 68)),\n (18, ('Barbacoa Bowl', 66)),\n (19, ('Carnitas Burrito', 59)),\n (20, ('Steak Soft Tacos', 55)),\n (21, ('6 Pack Soft Drink', 54)),\n (22, ('Chips and Tomatillo Red Chili Salsa', 48)),\n (23, ('Chicken Crispy Tacos', 47)),\n (24, ('Chips and Tomatillo Green Chili Salsa', 43)),\n (25, ('Carnitas Soft Tacos', 40)),\n (26, ('Steak Crispy Tacos', 35)),\n (27, ('Chips and Tomatillo-Green Chili Salsa', 31)),\n (28, ('Steak Salad Bowl', 29)),\n (29, ('Nantucket Nectar', 27)),\n (30, ('Barbacoa Soft Tacos', 25)),\n (31, ('Chips and Roasted Chili Corn Salsa', 22)),\n (32, ('Chips and Tomatillo-Red Chili Salsa', 20)),\n (33, ('Izze', 20)),\n (34, ('Chips and Roasted Chili-Corn Salsa', 18)),\n (35, ('Veggie Salad Bowl', 18)),\n (36, ('Barbacoa Crispy Tacos', 11)),\n (37, ('Barbacoa Salad Bowl', 10)),\n (38, ('Chicken Salad', 9)),\n (39, ('Veggie Soft Tacos', 7)),\n (40, ('Carnitas Crispy Tacos', 7)),\n (41, ('Burrito', 6)),\n (42, ('Veggie Salad', 6)),\n (43, ('Carnitas Salad Bowl', 6)),\n (44, ('Steak Salad', 4)),\n (45, ('Bowl', 2)),\n (46, ('Salad', 2)),\n (47, ('Crispy Tacos', 2)),\n (48, ('Veggie Crispy Tacos', 1)),\n (49, ('Carnitas Salad', 1)),\n (50, ('Chips and Mild Fresh Tomato Salsa', 1))]"
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "list(enumerate(df['item_name'].value_counts().iteritems(), 1))"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1.\t726\tChicken Bowl\n",
      "2.\t553\tChicken Burrito\n",
      "3.\t479\tChips and Guacamole\n",
      "4.\t368\tSteak Burrito\n",
      "5.\t301\tCanned Soft Drink\n",
      "6.\t211\tSteak Bowl\n",
      "7.\t211\tChips\n",
      "8.\t162\tBottled Water\n",
      "9.\t115\tChicken Soft Tacos\n",
      "10.\t110\tChicken Salad Bowl\n",
      "11.\t110\tChips and Fresh Tomato Salsa\n",
      "12.\t104\tCanned Soda\n",
      "13.\t101\tSide of Chips\n",
      "14.\t95\tVeggie Burrito\n",
      "15.\t91\tBarbacoa Burrito\n",
      "16.\t85\tVeggie Bowl\n",
      "17.\t68\tCarnitas Bowl\n",
      "18.\t66\tBarbacoa Bowl\n",
      "19.\t59\tCarnitas Burrito\n",
      "20.\t55\tSteak Soft Tacos\n",
      "21.\t54\t6 Pack Soft Drink\n",
      "22.\t48\tChips and Tomatillo Red Chili Salsa\n",
      "23.\t47\tChicken Crispy Tacos\n",
      "24.\t43\tChips and Tomatillo Green Chili Salsa\n",
      "25.\t40\tCarnitas Soft Tacos\n",
      "26.\t35\tSteak Crispy Tacos\n",
      "27.\t31\tChips and Tomatillo-Green Chili Salsa\n",
      "28.\t29\tSteak Salad Bowl\n",
      "29.\t27\tNantucket Nectar\n",
      "30.\t25\tBarbacoa Soft Tacos\n",
      "31.\t22\tChips and Roasted Chili Corn Salsa\n",
      "32.\t20\tChips and Tomatillo-Red Chili Salsa\n",
      "33.\t20\tIzze\n",
      "34.\t18\tChips and Roasted Chili-Corn Salsa\n",
      "35.\t18\tVeggie Salad Bowl\n",
      "36.\t11\tBarbacoa Crispy Tacos\n",
      "37.\t10\tBarbacoa Salad Bowl\n",
      "38.\t9\tChicken Salad\n",
      "39.\t7\tVeggie Soft Tacos\n",
      "40.\t7\tCarnitas Crispy Tacos\n",
      "41.\t6\tBurrito\n",
      "42.\t6\tVeggie Salad\n",
      "43.\t6\tCarnitas Salad Bowl\n",
      "44.\t4\tSteak Salad\n",
      "45.\t2\tBowl\n",
      "46.\t2\tSalad\n",
      "47.\t2\tCrispy Tacos\n",
      "48.\t1\tVeggie Crispy Tacos\n",
      "49.\t1\tCarnitas Salad\n",
      "50.\t1\tChips and Mild Fresh Tomato Salsa\n"
     ]
    }
   ],
   "source": [
    "for idx, (name, counts) in enumerate(df['item_name'].value_counts().iteritems(), 1):\n",
    "    print(f\"{idx}.\\t{counts}\\t{name}\")"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "## groupby"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%% md\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "outputs": [
    {
     "data": {
      "text/plain": "item_name\n6 Pack Soft Drink                         54\nBarbacoa Bowl                             66\nBarbacoa Burrito                          91\nBarbacoa Crispy Tacos                     11\nBarbacoa Salad Bowl                       10\nBarbacoa Soft Tacos                       25\nBottled Water                            162\nBowl                                       2\nBurrito                                    6\nCanned Soda                              104\nCanned Soft Drink                        301\nCarnitas Bowl                             68\nCarnitas Burrito                          59\nCarnitas Crispy Tacos                      7\nCarnitas Salad                             1\nCarnitas Salad Bowl                        6\nCarnitas Soft Tacos                       40\nChicken Bowl                             726\nChicken Burrito                          553\nChicken Crispy Tacos                      47\nChicken Salad                              9\nChicken Salad Bowl                       110\nChicken Soft Tacos                       115\nChips                                    211\nChips and Fresh Tomato Salsa             110\nChips and Guacamole                      479\nChips and Mild Fresh Tomato Salsa          1\nChips and Roasted Chili Corn Salsa        22\nChips and Roasted Chili-Corn Salsa        18\nChips and Tomatillo Green Chili Salsa     43\nChips and Tomatillo Red Chili Salsa       48\nChips and Tomatillo-Green Chili Salsa     31\nChips and Tomatillo-Red Chili Salsa       20\nCrispy Tacos                               2\nIzze                                      20\nNantucket Nectar                          27\nSalad                                      2\nSide of Chips                            101\nSteak Bowl                               211\nSteak Burrito                            368\nSteak Crispy Tacos                        35\nSteak Salad                                4\nSteak Salad Bowl                          29\nSteak Soft Tacos                          55\nVeggie Bowl                               85\nVeggie Burrito                            95\nVeggie Crispy Tacos                        1\nVeggie Salad                               6\nVeggie Salad Bowl                         18\nVeggie Soft Tacos                          7\nName: order_id, dtype: int64"
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 음식 주문 횟수\n",
    "df.groupby('item_name')['order_id'].count()"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "outputs": [
    {
     "data": {
      "text/plain": "item_name\n6 Pack Soft Drink                         55\nBarbacoa Bowl                             66\nBarbacoa Burrito                          91\nBarbacoa Crispy Tacos                     12\nBarbacoa Salad Bowl                       10\nBarbacoa Soft Tacos                       25\nBottled Water                            211\nBowl                                       4\nBurrito                                    6\nCanned Soda                              126\nCanned Soft Drink                        351\nCarnitas Bowl                             71\nCarnitas Burrito                          60\nCarnitas Crispy Tacos                      8\nCarnitas Salad                             1\nCarnitas Salad Bowl                        6\nCarnitas Soft Tacos                       40\nChicken Bowl                             761\nChicken Burrito                          591\nChicken Crispy Tacos                      50\nChicken Salad                              9\nChicken Salad Bowl                       123\nChicken Soft Tacos                       120\nChips                                    230\nChips and Fresh Tomato Salsa             130\nChips and Guacamole                      506\nChips and Mild Fresh Tomato Salsa          1\nChips and Roasted Chili Corn Salsa        23\nChips and Roasted Chili-Corn Salsa        18\nChips and Tomatillo Green Chili Salsa     45\nChips and Tomatillo Red Chili Salsa       50\nChips and Tomatillo-Green Chili Salsa     33\nChips and Tomatillo-Red Chili Salsa       25\nCrispy Tacos                               2\nIzze                                      20\nNantucket Nectar                          29\nSalad                                      2\nSide of Chips                            110\nSteak Bowl                               221\nSteak Burrito                            386\nSteak Crispy Tacos                        36\nSteak Salad                                4\nSteak Salad Bowl                          31\nSteak Soft Tacos                          56\nVeggie Bowl                               87\nVeggie Burrito                            97\nVeggie Crispy Tacos                        1\nVeggie Salad                               6\nVeggie Salad Bowl                         18\nVeggie Soft Tacos                          8\nName: quantity, dtype: int64"
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 음식 주문 량\n",
    "df.groupby('item_name')['quantity'].sum()"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "## Series.plot()"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%% md\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 576x720 with 1 Axes>",
      "image/png": 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\n"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.subplots(figsize=(8, 10))\n",
    "per = df.groupby('item_name')['quantity'].sum() / df.groupby('item_name')['order_id'].count() - 1\n",
    "per.plot(kind='barh')\n",
    "# plt.barh(per.index, per.values)\n",
    "plt.show()"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 576x720 with 1 Axes>",
      "image/png": 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\n"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.subplots(figsize=(8, 10))\n",
    "df.groupby('item_name')['quantity'].sum().plot(kind='barh')\n",
    "plt.show()"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 576x720 with 1 Axes>",
      "image/png": "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\n"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.subplots(figsize=(8, 10))\n",
    "item_quantity = df.groupby('item_name')['quantity'].sum()\n",
    "plt.bar(np.arange(item_quantity.size), item_quantity.values)\n",
    "plt.show()\n"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "outputs": [
    {
     "data": {
      "text/plain": "order_id\n1       11.56\n10      13.20\n100     10.08\n1000    20.50\n1001    10.08\n        ...  \n995     24.95\n996     43.00\n997     22.50\n998     10.88\n999     29.25\nName: item_price, Length: 1834, dtype: float64"
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 테이블 당 낸 금액\n",
    "sum_by_order_id = df.groupby('order_id')['item_price'].sum()\n",
    "sum_by_order_id"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "outputs": [
    {
     "data": {
      "text/plain": "18.811428571428568"
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 테이블 당 낸 금액의 평균\n",
    "df.groupby('order_id')['item_price'].sum().mean()"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "outputs": [
    {
     "data": {
      "text/plain": "array(['1000', '1004', '1006', '1010', '1012', '1013', '1016', '1017',\n       '1026', '103', '1030', '1031', '1033', '1035', '1038', '1041',\n       '1044', '1051', '1052', '1057', '1059', '1063', '1070', '1071',\n       '1074', '1076', '1078', '1079', '108', '1080', '1082', '1085',\n       '1087', '1088', '1091', '1092', '1095', '1097', '110', '1102',\n       '1103', '1106', '1110', '1113', '1115', '1116', '1126', '1128',\n       '1136', '1138', '1140', '1142', '1153', '1154', '1156', '1157',\n       '1158', '1166', '1167', '1168', '1169', '1170', '1171', '1175',\n       '1176', '1182', '1188', '1189', '1191', '1193', '1196', '1206',\n       '1208', '1209', '1221', '1222', '1223', '1224', '1226', '1228',\n       '123', '124', '1243', '1244', '1248', '125', '1250', '1255',\n       '1257', '126', '1261', '1269', '1270', '1271', '1276', '128',\n       '1282', '1283', '1289', '129', '1294', '1296', '1297', '1304',\n       '1317', '132', '1320', '1322', '1323', '1330', '1331', '1334',\n       '1336', '134', '1341', '1343', '1353', '1356', '1357', '1360',\n       '1361', '1364', '1367', '1368', '1381', '1382', '1386', '1388',\n       '1389', '1392', '1395', '1396', '1398', '1402', '1409', '1410',\n       '1411', '1413', '1414', '1418', '1419', '1425', '1426', '1429',\n       '1431', '1433', '1434', '1440', '1443', '1444', '1449', '1450',\n       '1453', '1454', '1455', '146', '1460', '1461', '1462', '1470',\n       '1472', '1474', '1476', '1477', '148', '1480', '1483', '149',\n       '1492', '1502', '1509', '1511', '1512', '1517', '152', '1521',\n       '1522', '1526', '1533', '1534', '1537', '1538', '1539', '1540',\n       '1541', '1544', '1547', '1554', '1556', '1559', '1561', '1572',\n       '1577', '1580', '1585', '1586', '1588', '1592', '1598', '1601',\n       '1603', '1607', '1610', '1611', '1613', '1615', '1626', '1627',\n       '1633', '1643', '1646', '1647', '1648', '1652', '1655', '1657',\n       '1659', '166', '1660', '1662', '1664', '1670', '1673', '1679',\n       '1684', '1686', '1690', '1691', '1692', '1695', '1696', '1699',\n       '1700', '1701', '1703', '1708', '1713', '1714', '1717', '1718',\n       '1719', '1724', '1726', '1730', '1731', '1735', '1737', '1738',\n       '1742', '1745', '1746', '1747', '1748', '1750', '1751', '176',\n       '1761', '1764', '1766', '1768', '1772', '1775', '178', '1780',\n       '1786', '1789', '1793', '1798', '18', '1802', '1803', '1805',\n       '1810', '1811', '1812', '182', '1822', '1824', '1825', '1827',\n       '1829', '1830', '1833', '1834', '184', '186', '192', '193', '195',\n       '196', '198', '199', '20', '205', '206', '207', '21', '210', '211',\n       '212', '215', '217', '222', '223', '225', '226', '227', '229',\n       '230', '231', '233', '234', '235', '237', '238', '240', '242',\n       '243', '247', '250', '253', '254', '258', '26', '261', '264',\n       '265', '271', '273', '276', '279', '28', '282', '284', '299', '30',\n       '300', '304', '305', '309', '311', '312', '317', '319', '326',\n       '327', '337', '338', '34', '343', '344', '346', '352', '356',\n       '357', '358', '360', '362', '363', '366', '367', '369', '372',\n       '376', '377', '379', '380', '381', '384', '387', '389', '396', '4',\n       '40', '400', '402', '404', '407', '408', '413', '419', '428', '43',\n       '432', '434', '438', '440', '443', '45', '451', '456', '457',\n       '459', '460', '462', '466', '467', '468', '471', '475', '478',\n       '479', '481', '482', '483', '485', '487', '488', '491', '497',\n       '498', '504', '506', '507', '509', '511', '513', '514', '519',\n       '527', '529', '53', '530', '534', '535', '541', '545', '546',\n       '549', '551', '552', '560', '561', '562', '571', '576', '577',\n       '578', '585', '588', '590', '596', '60', '602', '616', '620',\n       '621', '623', '627', '628', '635', '636', '637', '64', '643',\n       '646', '648', '649', '65', '651', '652', '653', '654', '658',\n       '664', '667', '670', '68', '682', '687', '688', '691', '692',\n       '694', '698', '70', '703', '705', '706', '708', '71', '712', '716',\n       '719', '73', '734', '737', '74', '741', '746', '75', '754', '759',\n       '760', '770', '777', '784', '787', '791', '792', '794', '795',\n       '798', '806', '807', '810', '812', '814', '816', '817', '818',\n       '82', '823', '83', '830', '836', '837', '838', '839', '84', '842',\n       '844', '848', '849', '850', '855', '856', '859', '86', '866',\n       '868', '869', '879', '880', '881', '885', '886', '888', '890',\n       '893', '894', '898', '899', '90', '900', '901', '907', '909',\n       '912', '913', '914', '916', '92', '926', '93', '930', '937', '94',\n       '940', '941', '943', '945', '946', '947', '948', '949', '951',\n       '952', '953', '957', '96', '960', '962', '966', '968', '97', '972',\n       '973', '975', '976', '979', '98', '981', '983', '987', '995',\n       '996', '997', '999'], dtype=object)"
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# $20 이상 낸 테이블의 주문 번호 (order_id)\n",
    "sum_by_order_id[sum_by_order_id >= 20].index.values"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "outputs": [
    {
     "data": {
      "text/plain": "Chicken Bowl                             693\nChicken Burrito                          521\nChips and Guacamole                      455\nSteak Burrito                            352\nCanned Soft Drink                        264\nSteak Bowl                               202\nChips                                    193\nBottled Water                            139\nChicken Soft Tacos                       110\nChips and Fresh Tomato Salsa             105\nSide of Chips                             99\nChicken Salad Bowl                        97\nVeggie Burrito                            94\nBarbacoa Burrito                          91\nCanned Soda                               84\nVeggie Bowl                               83\nBarbacoa Bowl                             66\nCarnitas Bowl                             66\nCarnitas Burrito                          58\nSteak Soft Tacos                          54\n6 Pack Soft Drink                         53\nChips and Tomatillo Red Chili Salsa       46\nChicken Crispy Tacos                      44\nChips and Tomatillo Green Chili Salsa     41\nCarnitas Soft Tacos                       40\nSteak Crispy Tacos                        34\nChips and Tomatillo-Green Chili Salsa     29\nSteak Salad Bowl                          27\nBarbacoa Soft Tacos                       25\nNantucket Nectar                          25\nChips and Roasted Chili Corn Salsa        21\nIzze                                      20\nChips and Roasted Chili-Corn Salsa        18\nVeggie Salad Bowl                         18\nChips and Tomatillo-Red Chili Salsa       17\nBarbacoa Crispy Tacos                     10\nBarbacoa Salad Bowl                       10\nChicken Salad                              9\nVeggie Soft Tacos                          6\nBurrito                                    6\nVeggie Salad                               6\nCarnitas Salad Bowl                        6\nCarnitas Crispy Tacos                      6\nSteak Salad                                4\nSalad                                      2\nCrispy Tacos                               2\nVeggie Crispy Tacos                        1\nBowl                                       1\nCarnitas Salad                             1\nChips and Mild Fresh Tomato Salsa          1\nName: item_name, dtype: int64"
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 음식 당 가격\n",
    "one_quantity = df[df.quantity == 1]\n",
    "one_quantity['item_name'].value_counts()"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "outputs": [
    {
     "data": {
      "text/plain": "item_name\n6 Pack Soft Drink                        6.49\nBarbacoa Bowl                            8.69\nBarbacoa Burrito                         8.69\nBarbacoa Crispy Tacos                    8.99\nBarbacoa Salad Bowl                      9.39\nBarbacoa Soft Tacos                      8.99\nBottled Water                            1.09\nBowl                                     7.40\nBurrito                                  7.40\nCanned Soda                              1.09\nCanned Soft Drink                        1.25\nCarnitas Bowl                            8.99\nCarnitas Burrito                         8.69\nCarnitas Crispy Tacos                    8.99\nCarnitas Salad                           8.99\nCarnitas Salad Bowl                      9.39\nCarnitas Soft Tacos                      8.99\nChicken Bowl                             8.19\nChicken Burrito                          8.19\nChicken Crispy Tacos                     8.49\nChicken Salad                            8.19\nChicken Salad Bowl                       8.75\nChicken Soft Tacos                       8.49\nChips                                    1.99\nChips and Fresh Tomato Salsa             2.29\nChips and Guacamole                      3.89\nChips and Mild Fresh Tomato Salsa        3.00\nChips and Roasted Chili Corn Salsa       2.95\nChips and Roasted Chili-Corn Salsa       2.39\nChips and Tomatillo Green Chili Salsa    2.95\nChips and Tomatillo Red Chili Salsa      2.95\nChips and Tomatillo-Green Chili Salsa    2.39\nChips and Tomatillo-Red Chili Salsa      2.39\nCrispy Tacos                             7.40\nIzze                                     3.39\nNantucket Nectar                         3.39\nSalad                                    7.40\nSide of Chips                            1.69\nSteak Bowl                               8.69\nSteak Burrito                            8.69\nSteak Crispy Tacos                       8.69\nSteak Salad                              8.69\nSteak Salad Bowl                         9.39\nSteak Soft Tacos                         8.99\nVeggie Bowl                              8.49\nVeggie Burrito                           8.49\nVeggie Crispy Tacos                      8.49\nVeggie Salad                             8.49\nVeggie Salad Bowl                        8.75\nVeggie Soft Tacos                        8.49\nName: item_price, dtype: float64"
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "one_quantity.groupby('item_name').item_price.min()"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "DataFrame.min()은 각각의 칼럼에서 가장 작은 값을 가져온 뒤, 합치는 것이다.\n",
    "\n",
    "같은 줄에 있다고 해서 같은 레코드가 아니다!!!"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%% md\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "outputs": [
    {
     "data": {
      "text/plain": "              order_id  quantity  \\\nitem_name                          \nBarbacoa Bowl     1000         1   \n\n                                              choice_description  item_price  \nitem_name                                                                     \nBarbacoa Bowl  [Fresh Tomato (Mild), [Lettuce, Black Beans, R...        8.69  ",
      "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>choice_description</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      <th></th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>Barbacoa Bowl</th>\n      <td>1000</td>\n      <td>1</td>\n      <td>[Fresh Tomato (Mild), [Lettuce, Black Beans, R...</td>\n      <td>8.69</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "one_quantity.groupby('item_name').min()[1:2]"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "outputs": [
    {
     "data": {
      "text/plain": "     order_id  quantity      item_name  \\\n2517     1000         1  Barbacoa Bowl   \n\n                                     choice_description  item_price  \n2517  [Roasted Chili Corn Salsa, [Rice, Black Beans,...        9.25  ",
      "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>2517</th>\n      <td>1000</td>\n      <td>1</td>\n      <td>Barbacoa Bowl</td>\n      <td>[Roasted Chili Corn Salsa, [Rice, Black Beans,...</td>\n      <td>9.25</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "one_quantity[(one_quantity.order_id == '1000') & (one_quantity.item_name == 'Barbacoa Bowl')]"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "outputs": [
    {
     "data": {
      "text/plain": "a    a\nb    1\ndtype: object"
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.DataFrame({\n",
    "    'a': ['a', 'b', 'c'],\n",
    "    'b': [3, 2, 1]\n",
    "}).min()"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "outputs": [
    {
     "data": {
      "text/plain": "23"
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 가장 비싼 주문에서 / 아이템이 총 몇 개 팔렸는지 구하기\n",
    "df.groupby('order_id').sum().sort_values(by='item_price', ascending=False).iloc[0].quantity.astype(int)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "outputs": [
    {
     "data": {
      "text/plain": "633"
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 특정 아이템을 주문 시킨 테이블 수\n",
    "df[(df.item_name == 'Chicken Bowl') | (df.item_name == 'Veggie Salad Bowl')].drop_duplicates(['item_name', 'order_id']).index.size"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "outputs": [
    {
     "data": {
      "text/plain": "114"
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 특정 아이템을 2개 이상 주문한 주문 횟수 구하기\n",
    "\n",
    "# chicken_bowl_quantity는 Index가 order_id고, value가 quantity의 합인 Series\n",
    "chicken_bowl_quantity = df[df.item_name == 'Chicken Bowl'].groupby('order_id').quantity.sum()\n",
    "chicken_bowl_quantity[chicken_bowl_quantity >= 2].size\n"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  }
 ],
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