Data Crawling.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import requests\n",
    "from bs4 import BeautifulSoup as bs\n",
    "import re"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "ua = {\"User-Agent\":\"Mozilla/5.0\"}\n",
    "top1000_url = \"https://www.whiskybase.com/whiskies/top1000\"\n",
    "top1000_page = requests.get(top1000_url, headers=ua)\n",
    "top1000_soup = bs(top1000_page.text, \"html\")            #top1000리스트 주소 가져오기"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "top1000_list = top1000_soup.select(\".clickable\")\n",
    "top1000_link_list = []\n",
    "for index, element in enumerate(top1000_list, 1):\n",
    "\ttop1000_link_list.append((element['href'],element.text))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'whisky_link = top1000_link_list[0][0]\\nwhisky_url = whisky_link\\nwhisky_page = requests.get(whisky_url, headers=ua)\\nwhisky_soup = bs(whisky_page.text, \"html\")'"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "'''whisky_link = top1000_link_list[0][0]\n",
    "whisky_url = whisky_link\n",
    "whisky_page = requests.get(whisky_url, headers=ua)\n",
    "whisky_soup = bs(whisky_page.text, \"html\")'''"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "whisky_list = []\n",
    "for index in range(len(top1000_link_list)):\n",
    "\n",
    "    whisky_link = top1000_link_list[index][0]\n",
    "    whisky_url = whisky_link\n",
    "    whisky_page = requests.get(whisky_url, headers=ua)\n",
    "    whisky_soup = bs(whisky_page.text, \"html\")          #위스키 링크 하나씩 가져옴\n",
    "\n",
    "\n",
    "    test_list = []\n",
    "\n",
    "\n",
    "    for x in range(len(whisky_soup.select(\".block-tasting\")[0].select(\".btn-tastingtag\"))):     #테이스팅 태그\n",
    "        tag_split = str(whisky_soup.select(\".block-tasting\")[0].select(\".btn-tastingtag\")[x]).split(\">\")[0]\n",
    "        re_pre=re.compile('data-num?=?\"?[0-9]?\"')       #정규표현식\n",
    "        tag_result=re_pre.findall(tag_split)\n",
    "        tag_name_str = whisky_soup.select(\".block-tasting\")[0].select(\".tag-name\")[x].text.replace(\"\\t\",\"\").replace(\"\\n\",\"\")\n",
    "        #정규 표현식 사용해서 테이스팅 태그 추출\n",
    "\n",
    "        if(str(tag_result).split(\"\\\"\")[1] == \"\"):\n",
    "            tag_cnt = \"0\"\n",
    "        else:\n",
    "            tag_cnt = str(tag_result).split(\"\\\"\")[1]\n",
    "        test_list.append((tag_name_str,tag_cnt))\n",
    "\n",
    "\n",
    "    for i in range(len(test_list)):\n",
    "        for j in range(len(test_list)):\n",
    "            if(test_list[i][0] < test_list[j][0]):\n",
    "                tmp = test_list[i]\n",
    "                test_list[i] = test_list[j]\n",
    "                test_list[j] = tmp\n",
    "    #테이스팅 태그만 있는 test_list를 sort해줌\n",
    "\n",
    "    for x in range(len(whisky_soup.select(\"dl\")[1].select(\"dt\"))):\n",
    "        test_list.insert(x,(whisky_soup.select(\"dl\")[1].select(\"dt\")[x].text, whisky_soup.select(\"dl\")[1].select(\"dd\")[x].text))\n",
    "    #위스키 정보들 가져옴\n",
    "\n",
    "\n",
    "    for i in range(3):\n",
    "        if(whisky_soup.select(\".wb--community-block\")[i].select(\"h4\")[0].text == \"\"):\n",
    "            if(i == 0):\n",
    "                test_list.insert(0,(\"Wishlist num\", 0))\n",
    "            elif(i == 1):\n",
    "                test_list.insert(0,(\"Member ratings\",0))\n",
    "            elif(i == 2):\n",
    "                test_list.insert(0,(\"Collection\",0))\n",
    "        else:\n",
    "            if(i == 0):\n",
    "                test_list.insert(0,(\"Wishlist num\", whisky_soup.select(\".wb--community-block\")[i].select(\"h4\")[0].text.replace(\"\\n\",\"\").replace(\"\\t\",\"\").split(\" \")[0]))\n",
    "            elif(i == 1):\n",
    "                test_list.insert(0,(\"Member ratings\",whisky_soup.select(\".wb--community-block\")[i].select(\"h4\")[0].text.replace(\"\\n\",\"\").replace(\"\\t\",\"\").split(\" \")[0]))\n",
    "            elif(i == 2):\n",
    "                test_list.insert(0,(\"Collection\",whisky_soup.select(\".wb--community-block\")[i].select(\"h4\")[0].text.replace(\"\\n\",\"\").replace(\"\\t\",\"\").split(\" \")[0]))\n",
    "    #wishlist, member rating, collection 리스트에 넣기. 존재 하지 않으면 0 넣어줌\n",
    "    \n",
    "    if not (whisky_soup.select(\".block-price\")):\n",
    "        test_list.insert(0,(\"Price\",\"NONE\"))\n",
    "    else:\n",
    "        test_list.insert(0,(\"Price\",whisky_soup.select(\".block-price\")[0].select(\"p\")[-1].text))\n",
    "    test_list.insert(0,(\"All Rating\",float(whisky_soup.select(\"dl\")[0].select(\"dd\")[0].text.replace(\"\\n\",\"\").split(\"/\")[0])))\n",
    "    test_list.insert(0,(\"Votes\",int(whisky_soup.select(\"dl\")[0].select(\"dd\")[-1].text)))\n",
    "    #가격, Rating, vote 들 test_list에 넣기.\n",
    "\n",
    "    whisky_list.append(test_list)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1000"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(whisky_list)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('Votes', 43),\n",
       " ('All Rating', 96.3),\n",
       " ('Price', '€ 65431.00'),\n",
       " ('Collection', '51'),\n",
       " ('Member ratings', '43'),\n",
       " ('Wishlist num', '146'),\n",
       " ('Whiskybase ID', 'WB24413'),\n",
       " ('Category', 'Single Malt'),\n",
       " ('Distillery', 'Laphroaig'),\n",
       " ('Bottler', 'R. W. Duthie & Co. (RWD)'),\n",
       " ('Vintage', '1967'),\n",
       " ('Bottled', '1982'),\n",
       " ('Stated Age', '15 years old'),\n",
       " ('Casktype', 'Sherry Cask'),\n",
       " ('Number of bottles', '720'),\n",
       " ('Strength', '57.0 % Vol.'),\n",
       " ('Size', '750 ml'),\n",
       " ('Bottled for', 'Imported by S. Samaroli'),\n",
       " ('Market', ' Italy'),\n",
       " ('Added on', '26 jul 2011 1:19 pm'),\n",
       " ('Chocolate', '3'),\n",
       " ('Citric', '2'),\n",
       " ('Coal-gas', '2'),\n",
       " ('Cooked Fruit', '1'),\n",
       " ('Cooked Mash', '0'),\n",
       " ('Cooked Vegetable', '0'),\n",
       " ('Dried Fruit', '2'),\n",
       " ('Fragant', '0'),\n",
       " ('Fresh Fruit', '1'),\n",
       " ('Green-House', '0'),\n",
       " ('Hay-like', '0'),\n",
       " ('Honey', '0'),\n",
       " ('Husky', '0'),\n",
       " ('Kippery', '0'),\n",
       " ('Leafy', '0'),\n",
       " ('Leathery', '2'),\n",
       " ('Malt Extract', '0'),\n",
       " ('Medicinal', '0'),\n",
       " ('Mossy', '0'),\n",
       " ('New Wood', '0'),\n",
       " ('Nutty', '1'),\n",
       " ('Oily', '2'),\n",
       " ('Old Wood', '2'),\n",
       " ('Plastic', '0'),\n",
       " ('Rubbery', '0'),\n",
       " ('Sandy', '1'),\n",
       " ('Sherried', '2'),\n",
       " ('Smokey', '2'),\n",
       " ('Solvent', '0'),\n",
       " ('Sweaty', '0'),\n",
       " ('Toasted', '0'),\n",
       " ('Tobacco', '2'),\n",
       " ('Vanilla', '0'),\n",
       " ('Vegetative', '0'),\n",
       " ('Yeasty', '0')]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "whisky_list[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "whisky_final_list = []\n",
    "pd_index = []\n",
    "for index in range(1000):\n",
    "    tmp_list = []\n",
    "    if index == 0:\n",
    "        for x in range(len(whisky_list[0])):\n",
    "            pd_index.append(whisky_list[0][x][0])\n",
    "    for x in range(len(whisky_list[index])):\n",
    "        tmp_list.append(whisky_list[index][x][1])\n",
    "    whisky_final_list.append(tmp_list)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "att_set=set()\n",
    "for whisky in whisky_list:\n",
    "    for k,v in whisky:\n",
    "        att_set.add(k)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "\n",
    "data_dc=dict()\n",
    "\n",
    "cnt=0\n",
    "for whisky in whisky_list:\n",
    "    data=dict.fromkeys(att_set,)\n",
    "    for k,v in whisky:\n",
    "        data[k]=v\n",
    "    tmp=pd.Series(data)\n",
    "    data_dc[cnt]=tmp\n",
    "    cnt+=1\n",
    "\n",
    "\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_df=pd.DataFrame(data_dc).T\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "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>Bottled for</th>\n",
       "      <th>Husky</th>\n",
       "      <th>Tobacco</th>\n",
       "      <th>Member ratings</th>\n",
       "      <th>Vintage</th>\n",
       "      <th>Category</th>\n",
       "      <th>Fresh Fruit</th>\n",
       "      <th>Yeasty</th>\n",
       "      <th>Honey</th>\n",
       "      <th>Price</th>\n",
       "      <th>...</th>\n",
       "      <th>Bottle code</th>\n",
       "      <th>Hay-like</th>\n",
       "      <th>Vegetative</th>\n",
       "      <th>Casknumber</th>\n",
       "      <th>Malt Extract</th>\n",
       "      <th>Cooked Mash</th>\n",
       "      <th>Plastic</th>\n",
       "      <th>Kippery</th>\n",
       "      <th>Mossy</th>\n",
       "      <th>Whiskybase ID</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Imported by S. Samaroli</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>43</td>\n",
       "      <td>1967</td>\n",
       "      <td>Single Malt</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>€ 65431.00</td>\n",
       "      <td>...</td>\n",
       "      <td>None</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>None</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>WB24413</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Osteria Apostoli, Milan - Imported by Samaroli</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>20</td>\n",
       "      <td>1970</td>\n",
       "      <td>Single Malt</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>€ 8999.00</td>\n",
       "      <td>...</td>\n",
       "      <td>None</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4367</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>WB23249</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Samaroli</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>23</td>\n",
       "      <td>1967</td>\n",
       "      <td>Single Malt</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>€ 29750.00</td>\n",
       "      <td>...</td>\n",
       "      <td>None</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>None</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>WB12078</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>None</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>53</td>\n",
       "      <td>05.11.1964</td>\n",
       "      <td>Single Malt</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>€ 25410.00</td>\n",
       "      <td>...</td>\n",
       "      <td>None</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>None</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>WB17300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>None</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>69</td>\n",
       "      <td>1966</td>\n",
       "      <td>Single Malt</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>€ 74688.00</td>\n",
       "      <td>...</td>\n",
       "      <td>None</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>None</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>WB8854</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 61 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                      Bottled for Husky Tobacco   \n",
       "0                         Imported by S. Samaroli     0       2  \\\n",
       "1  Osteria Apostoli, Milan - Imported by Samaroli     0       0   \n",
       "2                                        Samaroli     0       0   \n",
       "3                                            None     0       0   \n",
       "4                                            None     0       0   \n",
       "\n",
       "  Member ratings     Vintage     Category Fresh Fruit Yeasty Honey   \n",
       "0             43        1967  Single Malt           1      0     0  \\\n",
       "1             20        1970  Single Malt           0      0     0   \n",
       "2             23        1967  Single Malt           0      0     0   \n",
       "3             53  05.11.1964  Single Malt           0      0     0   \n",
       "4             69        1966  Single Malt           0      0     0   \n",
       "\n",
       "        Price  ... Bottle code Hay-like Vegetative Casknumber Malt Extract   \n",
       "0  € 65431.00  ...        None        0          0       None            0  \\\n",
       "1   € 8999.00  ...        None        0          0       4367            0   \n",
       "2  € 29750.00  ...        None        0          0       None            0   \n",
       "3  € 25410.00  ...        None        0          0       None            0   \n",
       "4  € 74688.00  ...        None        0          0       None            0   \n",
       "\n",
       "  Cooked Mash Plastic Kippery Mossy Whiskybase ID  \n",
       "0           0       0       0     0       WB24413  \n",
       "1           0       0       0     0       WB23249  \n",
       "2           1       0       0     0       WB12078  \n",
       "3           0       0       0     0       WB17300  \n",
       "4           0       0       0     0        WB8854  \n",
       "\n",
       "[5 rows x 61 columns]"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Bottled for          79.1\n",
       "Husky                 0.0\n",
       "Tobacco               0.0\n",
       "Member ratings        0.0\n",
       "Vintage               7.7\n",
       "Category              0.0\n",
       "Fresh Fruit           0.0\n",
       "Yeasty                0.0\n",
       "Honey                 0.0\n",
       "Price                 0.0\n",
       "Label                90.1\n",
       "Barcode              86.8\n",
       "Vanilla               0.0\n",
       "Distillery            1.8\n",
       "Collection            0.0\n",
       "Bottler               0.0\n",
       "Number of bottles    41.8\n",
       "Chocolate             0.0\n",
       "Green-House           0.0\n",
       "Added on              0.0\n",
       "Sherried              0.0\n",
       "Leathery              0.0\n",
       "New Wood              0.0\n",
       "Smokey                0.0\n",
       "Calculated age       91.3\n",
       "Toasted               0.0\n",
       "Stated Age           27.5\n",
       "Old Wood              0.0\n",
       "Cooked Fruit          0.0\n",
       "Leafy                 0.0\n",
       "Fragant               0.0\n",
       "Nutty                 0.0\n",
       "Rubbery               0.0\n",
       "Bottling serie       17.1\n",
       "Dried Fruit           0.0\n",
       "Citric                0.0\n",
       "Oily                  0.0\n",
       "Coal-gas              0.0\n",
       "Cooked Vegetable      0.0\n",
       "Market               76.2\n",
       "Casktype             36.0\n",
       "Solvent               0.0\n",
       "Wishlist num          0.0\n",
       "All Rating            0.0\n",
       "Sweaty                0.0\n",
       "Bottled              15.9\n",
       "Size                  0.0\n",
       "Strength              0.0\n",
       "Sandy                 0.0\n",
       "Votes                 0.0\n",
       "Medicinal             0.0\n",
       "Bottle code          91.8\n",
       "Hay-like              0.0\n",
       "Vegetative            0.0\n",
       "Casknumber           50.3\n",
       "Malt Extract          0.0\n",
       "Cooked Mash           0.0\n",
       "Plastic               0.0\n",
       "Kippery               0.0\n",
       "Mossy                 0.0\n",
       "Whiskybase ID         0.0\n",
       "dtype: float64"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.set_option('display.max_rows', 61)\n",
    "data_df.isnull().sum().divide(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "tastingtags=['Mash','Cooked Vegetable',\n",
    " 'Sweaty',\n",
    " 'New',\n",
    " 'Oily',\n",
    " 'Leafy',\n",
    " 'Kippery',\n",
    " 'Citric',\n",
    " 'Sherried',\n",
    " 'Mossy',\n",
    " 'Nutty',\n",
    " 'Toasted',\n",
    " 'Solvent',\n",
    " 'Malt',\n",
    " 'Vegetable',\n",
    " 'Old',\n",
    " 'Medicinal',\n",
    " 'Plastic',\n",
    " 'Coal-gas',\n",
    " 'Husky',\n",
    " 'Yeasty',\n",
    " 'Fruit',\n",
    " 'Hay-like',\n",
    " 'Leathery',\n",
    " 'Vegetative',\n",
    " 'Green-House',\n",
    " 'Honey',\n",
    " 'Extract',\n",
    " 'Fresh',\n",
    " 'Vanilla',\n",
    " 'Cooked',\n",
    " 'Tobacco',\n",
    " 'Chocolate',\n",
    " 'Smokey',\n",
    " 'Wood',\n",
    " 'Dried',\n",
    " 'Sandy',\n",
    " 'Rubbery',\n",
    " 'Fragant','Fresh Fruit','Cooked Fruit','Malt Extract', 'Old Wood','Cooked Mash','Dried Fruit','New Wood']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "46"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(tastingtags)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "tastingtags_df=data_df.filter(items=tastingtags)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Cooked Vegetable     18\n",
       "Sweaty                7\n",
       "Oily                 51\n",
       "Leafy                15\n",
       "Kippery              18\n",
       "Citric               92\n",
       "Sherried             88\n",
       "Mossy                27\n",
       "Nutty                53\n",
       "Toasted              52\n",
       "Solvent              14\n",
       "Medicinal            37\n",
       "Plastic              10\n",
       "Coal-gas             45\n",
       "Husky                10\n",
       "Yeasty                6\n",
       "Hay-like             33\n",
       "Leathery             78\n",
       "Vegetative            3\n",
       "Green-House          14\n",
       "Honey                70\n",
       "Vanilla              49\n",
       "Tobacco              65\n",
       "Chocolate           109\n",
       "Smokey               63\n",
       "Sandy                 6\n",
       "Rubbery               9\n",
       "Fragant              22\n",
       "Fresh Fruit          62\n",
       "Cooked Fruit         48\n",
       "Malt Extract         40\n",
       "Old Wood             62\n",
       "Cooked Mash           9\n",
       "Dried Fruit         107\n",
       "New Wood             49\n",
       "dtype: int64"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tastingtags_df.astype(int).sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "othertags=[x for x in list(data_df.columns) if x not in tastingtags]\n",
    "othertags_df=data_df.filter(items=othertags)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Bottled for</th>\n",
       "      <td>791</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Member ratings</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Vintage</th>\n",
       "      <td>77</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Category</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Price</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Label</th>\n",
       "      <td>901</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Barcode</th>\n",
       "      <td>868</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Distillery</th>\n",
       "      <td>18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Collection</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Bottler</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Number of bottles</th>\n",
       "      <td>418</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Added on</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Calculated age</th>\n",
       "      <td>913</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Stated Age</th>\n",
       "      <td>275</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Bottling serie</th>\n",
       "      <td>171</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Market</th>\n",
       "      <td>762</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Casktype</th>\n",
       "      <td>360</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Wishlist num</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>All Rating</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Bottled</th>\n",
       "      <td>159</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Size</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Strength</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Votes</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Bottle code</th>\n",
       "      <td>918</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Casknumber</th>\n",
       "      <td>503</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Whiskybase ID</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     0\n",
       "Bottled for        791\n",
       "Member ratings       0\n",
       "Vintage             77\n",
       "Category             0\n",
       "Price                0\n",
       "Label              901\n",
       "Barcode            868\n",
       "Distillery          18\n",
       "Collection           0\n",
       "Bottler              0\n",
       "Number of bottles  418\n",
       "Added on             0\n",
       "Calculated age     913\n",
       "Stated Age         275\n",
       "Bottling serie     171\n",
       "Market             762\n",
       "Casktype           360\n",
       "Wishlist num         0\n",
       "All Rating           0\n",
       "Bottled            159\n",
       "Size                 0\n",
       "Strength             0\n",
       "Votes                0\n",
       "Bottle code        918\n",
       "Casknumber         503\n",
       "Whiskybase ID        0"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "temp=pd.DataFrame(othertags_df.isnull().sum())\n",
    "temp"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 4000x1000 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = temp.plot.bar(rot=0, figsize=(40, 10))#결측치 그래프"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3.8.0 ('crawling')",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.16"
  },
  "orig_nbformat": 4,
  "vscode": {
   "interpreter": {
    "hash": "3074fdbde0b3c8c369af1555785d06178d56d560f43a329f6fa714fa61236c83"
   }
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}