real_seoul_review_rec.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "pd.set_option('display.max_rows', 100)\n",
    "pd.set_option('display.max_columns', 100)\n",
    "\n",
    "import matplotlib\n",
    "import matplotlib.pyplot as plt  # 파이플롯 사용\n",
    "from IPython.display import set_matplotlib_formats\n",
    "import seaborn as sns\n",
    "sns.set_style('whitegrid')\n",
    "\n",
    "\n",
    "set_matplotlib_formats('retina')  # 한글코드를 더 선명하게 해주는 조치, 레티나 설정\n",
    "matplotlib.rc('font', family='AppleGothic') # 폰트 설정\n",
    "matplotlib.rc('axes', unicode_minus=False) # 마이너스 폰트가 깨지는 경우가 있으므로 조치"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "place = pd.read_csv(\"place.csv\", engine='python')\n",
    "ask = pd.read_csv(\"ask.csv\", engine='python')\n",
    "rel = pd.read_csv(\"answer_rel.csv\", engine='python')\n",
    "user = pd.read_csv(\"user.csv\", engine='python')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>place_id</th>\n",
       "      <th>lat</th>\n",
       "      <th>lng</th>\n",
       "      <th>info_url</th>\n",
       "      <th>name</th>\n",
       "      <th>road_address</th>\n",
       "      <th>dong_address</th>\n",
       "      <th>category</th>\n",
       "      <th>date_created</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>43</td>\n",
       "      <td>1671949372</td>\n",
       "      <td>126.917069</td>\n",
       "      <td>37.565851</td>\n",
       "      <td>http://place.map.kakao.com/1671949372</td>\n",
       "      <td>소점</td>\n",
       "      <td>서울 마포구 성미산로17길 99</td>\n",
       "      <td>서울 마포구 연남동 487-278</td>\n",
       "      <td>음식점 &gt; 술집 &gt; 호프,요리주점</td>\n",
       "      <td>2020-03-29 23:27:55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>44</td>\n",
       "      <td>8503628</td>\n",
       "      <td>126.930749</td>\n",
       "      <td>37.568224</td>\n",
       "      <td>http://place.map.kakao.com/8503628</td>\n",
       "      <td>연희동칼국수 본점</td>\n",
       "      <td>서울 서대문구 연희맛로 37</td>\n",
       "      <td>서울 서대문구 연희동 132-29</td>\n",
       "      <td>음식점 &gt; 한식 &gt; 국수</td>\n",
       "      <td>2020-03-29 23:28:13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>45</td>\n",
       "      <td>1115772275</td>\n",
       "      <td>126.927583</td>\n",
       "      <td>37.564330</td>\n",
       "      <td>http://place.map.kakao.com/1115772275</td>\n",
       "      <td>연남장</td>\n",
       "      <td>서울 서대문구 연희로5길 22</td>\n",
       "      <td>서울 서대문구 연희동 218-15</td>\n",
       "      <td>서비스,산업 &gt; 전문대행 &gt; 공간대여 &gt; 모임공간</td>\n",
       "      <td>2020-03-29 23:28:20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>46</td>\n",
       "      <td>23981049</td>\n",
       "      <td>126.921764</td>\n",
       "      <td>37.548281</td>\n",
       "      <td>http://place.map.kakao.com/23981049</td>\n",
       "      <td>뭄알로이</td>\n",
       "      <td>서울 마포구 독막로15길 3-6</td>\n",
       "      <td>서울 마포구 상수동 313-6</td>\n",
       "      <td>음식점 &gt; 아시아음식 &gt; 동남아음식 &gt; 태국음식</td>\n",
       "      <td>2020-03-29 23:28:51</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>47</td>\n",
       "      <td>14822702</td>\n",
       "      <td>126.924122</td>\n",
       "      <td>37.561599</td>\n",
       "      <td>http://place.map.kakao.com/14822702</td>\n",
       "      <td>월강 부산돼지국밥</td>\n",
       "      <td>서울 마포구 동교로 241-1</td>\n",
       "      <td>서울 마포구 연남동 258-14</td>\n",
       "      <td>음식점 &gt; 한식 &gt; 국밥</td>\n",
       "      <td>2020-03-29 23:29:08</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   id    place_id         lat        lng  \\\n",
       "0  43  1671949372  126.917069  37.565851   \n",
       "1  44     8503628  126.930749  37.568224   \n",
       "2  45  1115772275  126.927583  37.564330   \n",
       "3  46    23981049  126.921764  37.548281   \n",
       "4  47    14822702  126.924122  37.561599   \n",
       "\n",
       "                                info_url       name       road_address  \\\n",
       "0  http://place.map.kakao.com/1671949372         소점  서울 마포구 성미산로17길 99   \n",
       "1     http://place.map.kakao.com/8503628  연희동칼국수 본점    서울 서대문구 연희맛로 37   \n",
       "2  http://place.map.kakao.com/1115772275        연남장   서울 서대문구 연희로5길 22   \n",
       "3    http://place.map.kakao.com/23981049       뭄알로이  서울 마포구 독막로15길 3-6   \n",
       "4    http://place.map.kakao.com/14822702  월강 부산돼지국밥   서울 마포구 동교로 241-1   \n",
       "\n",
       "         dong_address                     category         date_created  \n",
       "0  서울 마포구 연남동 487-278           음식점 > 술집 > 호프,요리주점  2020-03-29 23:27:55  \n",
       "1  서울 서대문구 연희동 132-29                음식점 > 한식 > 국수  2020-03-29 23:28:13  \n",
       "2  서울 서대문구 연희동 218-15  서비스,산업 > 전문대행 > 공간대여 > 모임공간  2020-03-29 23:28:20  \n",
       "3    서울 마포구 상수동 313-6   음식점 > 아시아음식 > 동남아음식 > 태국음식  2020-03-29 23:28:51  \n",
       "4   서울 마포구 연남동 258-14                음식점 > 한식 > 국밥  2020-03-29 23:29:08  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "place.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>sentence</th>\n",
       "      <th>category</th>\n",
       "      <th>icon</th>\n",
       "      <th>date_created</th>\n",
       "      <th>user_id</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>소개팅 하는 날, 저녁 식사하기 좋은 곳</td>\n",
       "      <td>food</td>\n",
       "      <td>💑</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>소개팅 하는 날, 점심 식사하기 좋은 곳</td>\n",
       "      <td>food</td>\n",
       "      <td>💑</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>상견례 하는 날, 부모님과 함께 가기 좋은 식당</td>\n",
       "      <td>food</td>\n",
       "      <td>🍲</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>혼자 노트북들고 작업하러 가기 좋은 곳</td>\n",
       "      <td>work</td>\n",
       "      <td>💻</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>간단하게 혼밥하기 좋은 곳</td>\n",
       "      <td>food</td>\n",
       "      <td>🍛</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   id                    sentence category icon date_created  user_id\n",
       "0   1      소개팅 하는 날, 저녁 식사하기 좋은 곳     food    💑          NaN      NaN\n",
       "1   2      소개팅 하는 날, 점심 식사하기 좋은 곳     food    💑          NaN      NaN\n",
       "2   3  상견례 하는 날, 부모님과 함께 가기 좋은 식당     food    🍲          NaN      NaN\n",
       "3   4       혼자 노트북들고 작업하러 가기 좋은 곳     work    💻          NaN      NaN\n",
       "4   5              간단하게 혼밥하기 좋은 곳     food    🍛          NaN      NaN"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ask.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>place_id</th>\n",
       "      <th>user_id</th>\n",
       "      <th>ask_id</th>\n",
       "      <th>date_created</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>20</td>\n",
       "      <td>48</td>\n",
       "      <td>56</td>\n",
       "      <td>5</td>\n",
       "      <td>2020-03-29 23:36:38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>21</td>\n",
       "      <td>49</td>\n",
       "      <td>56</td>\n",
       "      <td>6</td>\n",
       "      <td>2020-03-29 23:36:50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>22</td>\n",
       "      <td>50</td>\n",
       "      <td>56</td>\n",
       "      <td>8</td>\n",
       "      <td>2020-03-29 23:37:20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>24</td>\n",
       "      <td>52</td>\n",
       "      <td>56</td>\n",
       "      <td>1</td>\n",
       "      <td>2020-03-29 23:38:23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>25</td>\n",
       "      <td>53</td>\n",
       "      <td>56</td>\n",
       "      <td>10</td>\n",
       "      <td>2020-03-29 23:39:23</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   id  place_id  user_id  ask_id         date_created\n",
       "0  20        48       56       5  2020-03-29 23:36:38\n",
       "1  21        49       56       6  2020-03-29 23:36:50\n",
       "2  22        50       56       8  2020-03-29 23:37:20\n",
       "3  24        52       56       1  2020-03-29 23:38:23\n",
       "4  25        53       56      10  2020-03-29 23:39:23"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rel.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x11d295150>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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      "text/plain": [
       "<Figure size 1296x432 with 2 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 441,
       "width": 1099
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import missingno as msno\n",
    "msno.matrix(place, figsize = (18,6))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 코드로직\n",
    "\n",
    "1. 나와 비슷한 큐레이터 추천받기 버튼 누름\n",
    "2. 10개 이상 지도를 찍어야 추천 받기가 가능함\n",
    "3. 로그인 한 사람의 데이터베이스에서 랜덤하게 10개의 place 추출\n",
    "4. 10개 공간을 빠르게 크롤링해서 각 공간의 TFIDF 를 만듦\n",
    "5. 10개 공간에 대한 스코어링\n",
    "6. 10개 이상 추천한 사람들로 전체 db를 추리고 그 안에서 코사인 유사도 따져야함\n",
    "\n",
    "\n",
    "\n",
    "# 작업이 필요한 내역\n",
    "\n",
    "- 크롤러 출동시켜서 기존 db들에 대해서 추가적인 TFIDF 만들어서 데이터베이스에 저장하는 쿼리 짜기\n",
    "- 셀레늄을 쓸수밖에 없는가? <- db에 따로 쌓는 작업에는 써도 상관이 없지만\n",
    "- 추천받기 버튼을 통해 웹에서 진행될 파이썬 코드 안에서 어떻게 작동시킬지 알아보기 <- 비슷한 사람 찾기는 카테고리만으로 충분한가?\n",
    "- "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "place_df = place[['category','info_url','name','id']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "temp_df = place_df['category']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1329, 285)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.feature_extraction.text import CountVectorizer\n",
    "count_vect = CountVectorizer(min_df=2, ngram_range=(1,2))\n",
    "\n",
    "place_cate = count_vect.fit_transform(place_df['category'])\n",
    "place_cate.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<1329x285 sparse matrix of type '<class 'numpy.int64'>'\n",
       "\twith 6729 stored elements in Compressed Sparse Row format>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "place_cate"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1329, 1329)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([[1.        , 0.16903085, 0.        , ..., 0.14285714, 0.16903085,\n",
       "        0.        ],\n",
       "       [0.16903085, 1.        , 0.        , ..., 0.16903085, 0.2       ,\n",
       "        0.        ],\n",
       "       [0.        , 0.        , 1.        , ..., 0.        , 0.        ,\n",
       "        0.        ]])"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.metrics.pairwise import cosine_similarity\n",
    "\n",
    "place_simi = cosine_similarity(place_cate, place_cate)\n",
    "print(place_simi.shape)\n",
    "place_simi[:3]  # 2차원 ndarray\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[   0  681  770 ...  931  179 1328]]\n"
     ]
    }
   ],
   "source": [
    "# 솔직히 0.xxx값은 크게 관심 없고 순서가 중요하니까 ndarray에서 인덱스를 뽑아내는 argsort를 써줘야함\n",
    "\n",
    "place_simi_sorted_ind = place_simi.argsort()[:, ::-1]\n",
    "print(place_simi_sorted_ind[:1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "def find_simi_place(df, sorted_ind, place_name, top_n=10):\n",
    "    \n",
    "    place_title = df[df['name'] == place_name]\n",
    "    place_index = place_title.index.values\n",
    "    similar_indexes = sorted_ind[place_index, :(top_n)]  # 매개변수로 설정한 top n개 뽑아준다\n",
    "    \n",
    "    print(similar_indexes)\n",
    "    similar_indexes = similar_indexes.reshape(-1)     #2차원 array를 데이터프레임에서 쓰게 1차원 array로 변경\n",
    "    return df.iloc[similar_indexes]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[1244    2  854   10  886]]\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "    }\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>category</th>\n",
       "      <th>info_url</th>\n",
       "      <th>name</th>\n",
       "      <th>id</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1244</th>\n",
       "      <td>서비스,산업 &gt; 전문대행 &gt; 공간대여 &gt; 공유오피스</td>\n",
       "      <td>http://place.map.kakao.com/1265687654</td>\n",
       "      <td>힙덱</td>\n",
       "      <td>1292</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>서비스,산업 &gt; 전문대행 &gt; 공간대여 &gt; 모임공간</td>\n",
       "      <td>http://place.map.kakao.com/1115772275</td>\n",
       "      <td>연남장</td>\n",
       "      <td>45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>854</th>\n",
       "      <td>서비스,산업 &gt; 전문대행 &gt; 컨설팅</td>\n",
       "      <td>http://place.map.kakao.com/1118419870</td>\n",
       "      <td>솔티드</td>\n",
       "      <td>902</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>서비스,산업 &gt; 전문대행</td>\n",
       "      <td>http://place.map.kakao.com/26417413</td>\n",
       "      <td>카우앤독</td>\n",
       "      <td>53</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>886</th>\n",
       "      <td>서비스,산업 &gt; 마케팅 &gt; 광고제작</td>\n",
       "      <td>http://place.map.kakao.com/20992998</td>\n",
       "      <td>모 픽쳐스</td>\n",
       "      <td>934</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                          category                               info_url  \\\n",
       "1244  서비스,산업 > 전문대행 > 공간대여 > 공유오피스  http://place.map.kakao.com/1265687654   \n",
       "2      서비스,산업 > 전문대행 > 공간대여 > 모임공간  http://place.map.kakao.com/1115772275   \n",
       "854            서비스,산업 > 전문대행 > 컨설팅  http://place.map.kakao.com/1118419870   \n",
       "10                   서비스,산업 > 전문대행    http://place.map.kakao.com/26417413   \n",
       "886            서비스,산업 > 마케팅 > 광고제작    http://place.map.kakao.com/20992998   \n",
       "\n",
       "       name    id  \n",
       "1244     힙덱  1292  \n",
       "2       연남장    45  \n",
       "854     솔티드   902  \n",
       "10     카우앤독    53  \n",
       "886   모 픽쳐스   934  "
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "find_simi_place(place_df, place_simi_sorted_ind, '연남장',5)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[ 396  688 1310  342  395]]\n"
     ]
    },
    {
     "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>category</th>\n",
       "      <th>info_url</th>\n",
       "      <th>name</th>\n",
       "      <th>id</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>396</th>\n",
       "      <td>음식점 &gt; 한식 &gt; 해물,생선</td>\n",
       "      <td>http://place.map.kakao.com/1402648947</td>\n",
       "      <td>개성집 남대문시장점</td>\n",
       "      <td>444</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>688</th>\n",
       "      <td>음식점 &gt; 한식 &gt; 해물,생선</td>\n",
       "      <td>http://place.map.kakao.com/27244461</td>\n",
       "      <td>메너머쭈꾸미</td>\n",
       "      <td>736</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1310</th>\n",
       "      <td>음식점 &gt; 한식 &gt; 해물,생선 &gt; 회</td>\n",
       "      <td>http://place.map.kakao.com/8128185</td>\n",
       "      <td>동해별관</td>\n",
       "      <td>1358</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>342</th>\n",
       "      <td>음식점 &gt; 한식 &gt; 해물,생선</td>\n",
       "      <td>http://place.map.kakao.com/11064200</td>\n",
       "      <td>무교동북어국집</td>\n",
       "      <td>390</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>395</th>\n",
       "      <td>음식점 &gt; 한식 &gt; 해물,생선</td>\n",
       "      <td>http://place.map.kakao.com/12997934</td>\n",
       "      <td>우정식당</td>\n",
       "      <td>443</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  category                               info_url        name  \\\n",
       "396       음식점 > 한식 > 해물,생선  http://place.map.kakao.com/1402648947  개성집 남대문시장점   \n",
       "688       음식점 > 한식 > 해물,생선    http://place.map.kakao.com/27244461      메너머쭈꾸미   \n",
       "1310  음식점 > 한식 > 해물,생선 > 회     http://place.map.kakao.com/8128185        동해별관   \n",
       "342       음식점 > 한식 > 해물,생선    http://place.map.kakao.com/11064200     무교동북어국집   \n",
       "395       음식점 > 한식 > 해물,생선    http://place.map.kakao.com/12997934        우정식당   \n",
       "\n",
       "        id  \n",
       "396    444  \n",
       "688    736  \n",
       "1310  1358  \n",
       "342    390  \n",
       "395    443  "
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "find_simi_place(place_df, place_simi_sorted_ind, '서촌계단집',5)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'http://place.map.kakao.com/1671949372'"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "place.iloc[0,4]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'http://place.map.kakao.com/8503628'"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "place.iloc[1,4]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 크롤링"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 셀레늄 import\n",
    "\n",
    "from selenium import webdriver\n",
    "from selenium.webdriver.common.keys import Keys\n",
    "from selenium.webdriver.support.ui import WebDriverWait\n",
    "from selenium.webdriver.support import expected_conditions as EC\n",
    "from selenium.common.exceptions import TimeoutException\n",
    "from selenium.common.exceptions import NoSuchElementException\n",
    "from selenium.webdriver.common.by import By\n",
    "from selenium.webdriver.support.ui import Select\n",
    "import time\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "def review_crwaling(place_url):\n",
    "\n",
    "    chromedriver = '/Users/taewoongkong/workspace/selenium_learning/chromedriver'  # 크롬 드라이브 디렉토리는 어떻게 앱에 적용시키지?\n",
    "    # headless 옵션임\n",
    "    headless_options = webdriver.ChromeOptions()\n",
    "    headless_options.add_argument('headless')\n",
    "    driver = webdriver.Chrome(chromedriver, options=headless_options)  # 대소문자 주의\n",
    "\n",
    "    # 추가 옵션들\n",
    "    headless_options.add_argument('window-size=1920x1080')\n",
    "    headless_options.add_argument('disable-gpu')\n",
    "    headless_options.add_argument('lang=ko_KR')\n",
    "    headless_options.add_argument('User-Agent:  Mozilla/5.0 (Macintosh; Intel Mac OS X 10_15_1) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/79.0.3945.79 Safari/537.36')\n",
    "\n",
    "\n",
    "    review_text = \"\"\n",
    "\n",
    "    # 크롤링 시작\n",
    "    driver.get(place_url)\n",
    "    time.sleep(0.3)\n",
    "\n",
    "    review_quantity = driver.find_element_by_css_selector(\"#mArticle > div.cont_review > div.particular_head > span\").text\n",
    "    if review_quantity != '이 장소의 리뷰를 첫 번째로 등록해주세요.':\n",
    "        review_quantity = int(review_quantity)\n",
    "        \n",
    "        if review_quantity > 3:\n",
    "            for num in range(1,4):\n",
    "                review_text = review_text + driver.find_element_by_css_selector(f\"#mArticle > div.cont_review > div.wrap_list > ul > li:nth-child({num}) > a > div.review_story > p\").text\n",
    "        else:\n",
    "            for num in range(1, review_quantity + 1):\n",
    "                review_text = review_text + driver.find_element_by_css_selector(f\"#mArticle > div.cont_review > div.wrap_list > ul > li:nth-child({num}) > a > div.review_story > p\").text\n",
    "\n",
    "        \n",
    "\n",
    "        return review_text\n",
    "    else:\n",
    "        review_text = np.nan\n",
    "\n",
    "#    driver.quit()\n",
    "#    print(\"리뷰 개수 : \", review_quantity)\n",
    "#    print(review_text)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0       http://place.map.kakao.com/1671949372\n",
       "1          http://place.map.kakao.com/8503628\n",
       "2       http://place.map.kakao.com/1115772275\n",
       "3         http://place.map.kakao.com/23981049\n",
       "4         http://place.map.kakao.com/14822702\n",
       "                        ...                  \n",
       "1324      http://place.map.kakao.com/19156993\n",
       "1325      http://place.map.kakao.com/16037797\n",
       "1326     http://place.map.kakao.com/768666442\n",
       "1327       http://place.map.kakao.com/3800303\n",
       "1328     http://place.map.kakao.com/144415612\n",
       "Name: info_url, Length: 1329, dtype: object"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "place['info_url']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "12e05d580f1f4d519dcde901165b207d",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(IntProgress(value=0, max=1329), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "name 'e' is not defined\n",
      "문제가 발생했던 데이터 행 :  53\n",
      "name 'e' is not defined\n",
      "문제가 발생했던 데이터 행 :  54\n",
      "name 'e' is not defined\n",
      "문제가 발생했던 데이터 행 :  56\n",
      "name 'e' is not defined\n",
      "문제가 발생했던 데이터 행 :  58\n",
      "name 'e' is not defined\n",
      "문제가 발생했던 데이터 행 :  67\n",
      "name 'e' is not defined\n",
      "문제가 발생했던 데이터 행 :  104\n",
      "name 'e' is not defined\n",
      "문제가 발생했던 데이터 행 :  106\n",
      "name 'e' is not defined\n",
      "문제가 발생했던 데이터 행 :  108\n",
      "name 'e' is not defined\n",
      "문제가 발생했던 데이터 행 :  109\n",
      "name 'e' is not defined\n",
      "문제가 발생했던 데이터 행 :  116\n"
     ]
    },
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-137-4a6649977e76>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m     32\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mi\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0murl\u001b[0m \u001b[0;32min\u001b[0m \u001b[0menumerate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtqdm_notebook\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mplace\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'info_url'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     33\u001b[0m     \u001b[0mdriver\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0murl\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 34\u001b[0;31m     \u001b[0mtime\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msleep\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     35\u001b[0m     \u001b[0mreview_text\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m\"\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     36\u001b[0m     \u001b[0mnum\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
     ]
    }
   ],
   "source": [
    "from tqdm import tqdm_notebook\n",
    "\n",
    "\n",
    "# 셀레늄 import\n",
    "\n",
    "from selenium import webdriver\n",
    "from selenium.webdriver.common.keys import Keys\n",
    "from selenium.webdriver.support.ui import WebDriverWait\n",
    "from selenium.webdriver.support import expected_conditions as EC\n",
    "from selenium.common.exceptions import TimeoutException\n",
    "from selenium.common.exceptions import NoSuchElementException\n",
    "from selenium.webdriver.common.by import By\n",
    "from selenium.webdriver.support.ui import Select\n",
    "import time\n",
    "\n",
    "\n",
    "\n",
    "chromedriver = '/Users/taewoongkong/workspace/selenium_learning/chromedriver'  # 크롬 드라이브 디렉토리는 어떻게 앱에 적용시키지?\n",
    "# headless 옵션임\n",
    "headless_options = webdriver.ChromeOptions()\n",
    "headless_options.add_argument('headless')\n",
    "driver = webdriver.Chrome(chromedriver, options=headless_options)  # 대소문자 주의\n",
    "\n",
    "# 추가 옵션들\n",
    "headless_options.add_argument('window-size=1920x1080')\n",
    "headless_options.add_argument('disable-gpu')\n",
    "headless_options.add_argument('lang=ko_KR')\n",
    "headless_options.add_argument('User-Agent:  Mozilla/5.0 (Macintosh; Intel Mac OS X 10_15_1) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/79.0.3945.79 Safari/537.36')\n",
    "\n",
    "\n",
    "review_list = []\n",
    "for i, url in enumerate(tqdm_notebook(place['info_url'])):\n",
    "    driver.get(url)\n",
    "    time.sleep(1)\n",
    "    review_text = \"\"\n",
    "    num = 0\n",
    "    try:\n",
    "        while num < 3:\n",
    "            try:\n",
    "                num += 1\n",
    "                review_text = review_text + driver.find_element_by_css_selector(f\"div.wrap_list > ul > li:nth-child({num}) > a > div.review_story > p\").text\n",
    "                if num == 3:\n",
    "                    review_list.append(review_text)\n",
    "            except Exception as e1:\n",
    "                if str(e).contains(\"li:nth-child(1)\"):\n",
    "                    print(\"리뷰가 하나도 없네\")\n",
    "                    review_text = \"empty\"\n",
    "                    review_list.append(review_text)\n",
    "                else:\n",
    "                    print(e1)\n",
    "                    print(\"문제가 발생했던 데이터 행 : \", i)\n",
    "                    review_list.append(review_text)\n",
    "                    break\n",
    "                \n",
    "    except Exception as e2:\n",
    "        print(e2)\n",
    "        print(\"문제가 발생했던 데이터 행 : \", i)\n",
    "        review_text = \"empty\"\n",
    "        review_list.append(review_text)\n",
    "driver.quit()\n",
    "#mArticle > div:nth-child(6) > div.wrap_list > ul > li:nth-child(2) > a > div.review_story > p\n",
    "#mArticle > div:nth-child(6) > div.wrap_list > ul > li:nth-child(1) > a > div.review_story > p\n",
    "#mArticle > div:nth-child(7) > div.wrap_list > ul > li:nth-child(1) > a > div.review_story > p\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "id                                               154\n",
       "place_id                                   753213515\n",
       "lat                                           126.98\n",
       "lng                                          37.5671\n",
       "info_url        http://place.map.kakao.com/753213515\n",
       "name                                          센터플레이스\n",
       "road_address                         서울 중구 남대문로9길 40\n",
       "dong_address                            서울 중구 다동 155\n",
       "category                                    부동산 > 빌딩\n",
       "date_created                     2020-05-20 16:02:49\n",
       "Name: 108, dtype: object"
      ]
     },
     "execution_count": 136,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "place.iloc[108,:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [],
   "source": [
    "mini_place = place.iloc[:200, :]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/taewoongkong/miniconda3/lib/python3.7/site-packages/ipykernel_launcher.py:1: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  \"\"\"Entry point for launching an IPython kernel.\n"
     ]
    }
   ],
   "source": [
    "mini_place['review'] = review_list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(200, 200)\n",
      "[[  0   2   4 120  11  45  17  92 150  74  40 198 123   9  52 156 173 155\n",
      "  199 142  86  69 186  47  34 140 161  12  37 163 183  91  18 143 124  33\n",
      "   75 157  31  28 101 158   6  96  84 149 171  30 177  21  49 154  98  62\n",
      "  100  46 196 141 115 102  61 111 139 114 145 166  79  41  65  87 194  20\n",
      "  138 159  72 107 184  35 135  23  57 180 197  39 181 167 195 191  16  13\n",
      "  176   8 126 162  22  95  15 137 127 192  24 134  70 185 118   5  38  63\n",
      "  128  80  93  50 133  27  19  73 153  43   3 112  32  29 152 189 129  44\n",
      "  190  81  48  56  54 130  36 105 174  83  51  55 165  26   1   7  97 168\n",
      "   94  99 144 175  25 188 119 106 117  59  88 132 108  14  77  66 169 151\n",
      "   58 113  10  68 136 182 121  64  89  85  82 104  53 125  60  90 193 103\n",
      "  160 131  71 187 179 146  78  67  76 164 178 172 170 122  42 110 148 147\n",
      "  109 116]]\n",
      "[[  2 154 150  52 158  17 105 156   6 155]]\n"
     ]
    },
    {
     "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>id</th>\n",
       "      <th>place_id</th>\n",
       "      <th>lat</th>\n",
       "      <th>lng</th>\n",
       "      <th>info_url</th>\n",
       "      <th>name</th>\n",
       "      <th>road_address</th>\n",
       "      <th>dong_address</th>\n",
       "      <th>category</th>\n",
       "      <th>date_created</th>\n",
       "      <th>review</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>45</td>\n",
       "      <td>1115772275</td>\n",
       "      <td>126.927583</td>\n",
       "      <td>37.564330</td>\n",
       "      <td>http://place.map.kakao.com/1115772275</td>\n",
       "      <td>연남장</td>\n",
       "      <td>서울 서대문구 연희로5길 22</td>\n",
       "      <td>서울 서대문구 연희동 218-15</td>\n",
       "      <td>서비스,산업 &gt; 전문대행 &gt; 공간대여 &gt; 모임공간</td>\n",
       "      <td>2020-03-29 23:28:20</td>\n",
       "      <td>연희동/연남동 감성 카페 연남장 (by 연남방앗간) 연남장 서울특별시 서대문구 연희...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>154</th>\n",
       "      <td>202</td>\n",
       "      <td>1538248248</td>\n",
       "      <td>126.926050</td>\n",
       "      <td>37.562844</td>\n",
       "      <td>http://place.map.kakao.com/1538248248</td>\n",
       "      <td>련남면옥</td>\n",
       "      <td>서울 마포구 성미산로 190-4</td>\n",
       "      <td>서울 마포구 연남동 228-2</td>\n",
       "      <td>음식점 &gt; 한식 &gt; 냉면</td>\n",
       "      <td>2020-05-31 00:20:59</td>\n",
       "      <td>평양냉면 먹은지는 꽤 됐는데 다시 생각나서 이제야 올리게되는 냉면 포스팅 먹은건 너...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>150</th>\n",
       "      <td>198</td>\n",
       "      <td>25048467</td>\n",
       "      <td>126.945529</td>\n",
       "      <td>37.547487</td>\n",
       "      <td>http://place.map.kakao.com/25048467</td>\n",
       "      <td>을밀대 본점</td>\n",
       "      <td>서울 마포구 숭문길 24</td>\n",
       "      <td>서울 마포구 염리동 147-6</td>\n",
       "      <td>음식점 &gt; 한식 &gt; 냉면</td>\n",
       "      <td>2020-05-31 00:20:20</td>\n",
       "      <td>여러분우웅 아녕아하세요요~~ 오랜만에.. 올립니다ㅠㅠ 요즘 바빠가지고 한번에!! 올...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>52</th>\n",
       "      <td>95</td>\n",
       "      <td>19925752</td>\n",
       "      <td>126.924055</td>\n",
       "      <td>37.565189</td>\n",
       "      <td>http://place.map.kakao.com/19925752</td>\n",
       "      <td>툭툭누들타이</td>\n",
       "      <td>서울 마포구 성미산로 161-8</td>\n",
       "      <td>서울 마포구 연남동 223-15</td>\n",
       "      <td>음식점 &gt; 아시아음식 &gt; 동남아음식 &gt; 태국음식</td>\n",
       "      <td>2020-03-31 22:57:15</td>\n",
       "      <td>동남아 여행을 가고 싶은 요즘. 태국 맛집을 맛보고 싶어 힙한 홍대로 가봤습니다. ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>158</th>\n",
       "      <td>206</td>\n",
       "      <td>17002378</td>\n",
       "      <td>126.930305</td>\n",
       "      <td>37.568397</td>\n",
       "      <td>http://place.map.kakao.com/17002378</td>\n",
       "      <td>유어마인드</td>\n",
       "      <td>서울 서대문구 연희로11라길 10-6</td>\n",
       "      <td>서울 서대문구 연희동 132-32</td>\n",
       "      <td>문화,예술 &gt; 도서 &gt; 서점</td>\n",
       "      <td>2020-05-31 00:24:11</td>\n",
       "      <td>동생과 함께 한 날씨가 매우 좋았던 어느 날의 기록. 낯설기도 익숙하기도 한 이 동...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>60</td>\n",
       "      <td>473697180</td>\n",
       "      <td>126.927748</td>\n",
       "      <td>37.566867</td>\n",
       "      <td>http://place.map.kakao.com/473697180</td>\n",
       "      <td>연희대공원</td>\n",
       "      <td>서울 서대문구 연희로11가길 42</td>\n",
       "      <td>서울 서대문구 연희동 273-9</td>\n",
       "      <td>음식점 &gt; 카페 &gt; 테마카페</td>\n",
       "      <td>2020-03-30 00:19:06</td>\n",
       "      <td>안녕하세요 동그리 입니다 :-) 이번 포스팅은 연희동에 위치한 카페겸 복합문화공간,...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>105</th>\n",
       "      <td>150</td>\n",
       "      <td>1915903142</td>\n",
       "      <td>126.206070</td>\n",
       "      <td>33.369460</td>\n",
       "      <td>http://place.map.kakao.com/1915903142</td>\n",
       "      <td>울트라마린</td>\n",
       "      <td>제주특별자치도 제주시 한경면 일주서로 4611</td>\n",
       "      <td>제주특별자치도 제주시 한경면 판포리 1611</td>\n",
       "      <td>음식점 &gt; 카페 &gt; 커피전문점</td>\n",
       "      <td>2020-05-19 18:29:33</td>\n",
       "      <td>제주도 서쪽 카페 울트라 마린 (ULTRA MARINE) 울트라마린 제주특별자치도 ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>156</th>\n",
       "      <td>204</td>\n",
       "      <td>27238568</td>\n",
       "      <td>126.924942</td>\n",
       "      <td>37.560912</td>\n",
       "      <td>http://place.map.kakao.com/27238568</td>\n",
       "      <td>숨은골목</td>\n",
       "      <td>서울 마포구 동교로34길 12</td>\n",
       "      <td>서울 마포구 연남동 260-41</td>\n",
       "      <td>음식점 &gt; 술집</td>\n",
       "      <td>2020-05-31 00:22:35</td>\n",
       "      <td>비오는날 꼭 찾아가는 곳 연남동맛집 숨은골목 비오는 금요일 저녁 눈치작전 성공한듯 ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>49</td>\n",
       "      <td>1789136969</td>\n",
       "      <td>126.914394</td>\n",
       "      <td>37.556994</td>\n",
       "      <td>http://place.map.kakao.com/1789136969</td>\n",
       "      <td>고기상</td>\n",
       "      <td>서울 마포구 잔다리로 112</td>\n",
       "      <td>서울 마포구 서교동 458-1</td>\n",
       "      <td>음식점 &gt; 한식 &gt; 육류,고기</td>\n",
       "      <td>2020-03-29 23:36:50</td>\n",
       "      <td>어느날 갑자기 꼬기가 먹고 싶다!!!!!!!!소꼬기!!!!! 를 외친 묘미동생을 위...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>155</th>\n",
       "      <td>203</td>\n",
       "      <td>27327500</td>\n",
       "      <td>126.912584</td>\n",
       "      <td>37.548300</td>\n",
       "      <td>http://place.map.kakao.com/27327500</td>\n",
       "      <td>동무밥상</td>\n",
       "      <td>서울 마포구 양화진길 10</td>\n",
       "      <td>서울 마포구 합정동 381-64</td>\n",
       "      <td>음식점 &gt; 한식</td>\n",
       "      <td>2020-05-31 00:21:43</td>\n",
       "      <td>합정역 맛집: 옥류관 출신 주방장이 만드는 평양냉면 [동무밥상] 이 집은 수요미식회...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      id    place_id         lat        lng  \\\n",
       "2     45  1115772275  126.927583  37.564330   \n",
       "154  202  1538248248  126.926050  37.562844   \n",
       "150  198    25048467  126.945529  37.547487   \n",
       "52    95    19925752  126.924055  37.565189   \n",
       "158  206    17002378  126.930305  37.568397   \n",
       "17    60   473697180  126.927748  37.566867   \n",
       "105  150  1915903142  126.206070  33.369460   \n",
       "156  204    27238568  126.924942  37.560912   \n",
       "6     49  1789136969  126.914394  37.556994   \n",
       "155  203    27327500  126.912584  37.548300   \n",
       "\n",
       "                                  info_url    name               road_address  \\\n",
       "2    http://place.map.kakao.com/1115772275     연남장           서울 서대문구 연희로5길 22   \n",
       "154  http://place.map.kakao.com/1538248248    련남면옥          서울 마포구 성미산로 190-4   \n",
       "150    http://place.map.kakao.com/25048467  을밀대 본점              서울 마포구 숭문길 24   \n",
       "52     http://place.map.kakao.com/19925752  툭툭누들타이          서울 마포구 성미산로 161-8   \n",
       "158    http://place.map.kakao.com/17002378   유어마인드       서울 서대문구 연희로11라길 10-6   \n",
       "17    http://place.map.kakao.com/473697180   연희대공원         서울 서대문구 연희로11가길 42   \n",
       "105  http://place.map.kakao.com/1915903142   울트라마린  제주특별자치도 제주시 한경면 일주서로 4611   \n",
       "156    http://place.map.kakao.com/27238568    숨은골목           서울 마포구 동교로34길 12   \n",
       "6    http://place.map.kakao.com/1789136969     고기상            서울 마포구 잔다리로 112   \n",
       "155    http://place.map.kakao.com/27327500    동무밥상             서울 마포구 양화진길 10   \n",
       "\n",
       "                 dong_address                     category  \\\n",
       "2          서울 서대문구 연희동 218-15  서비스,산업 > 전문대행 > 공간대여 > 모임공간   \n",
       "154          서울 마포구 연남동 228-2                음식점 > 한식 > 냉면   \n",
       "150          서울 마포구 염리동 147-6                음식점 > 한식 > 냉면   \n",
       "52          서울 마포구 연남동 223-15   음식점 > 아시아음식 > 동남아음식 > 태국음식   \n",
       "158        서울 서대문구 연희동 132-32              문화,예술 > 도서 > 서점   \n",
       "17          서울 서대문구 연희동 273-9              음식점 > 카페 > 테마카페   \n",
       "105  제주특별자치도 제주시 한경면 판포리 1611             음식점 > 카페 > 커피전문점   \n",
       "156         서울 마포구 연남동 260-41                     음식점 > 술집   \n",
       "6            서울 마포구 서교동 458-1             음식점 > 한식 > 육류,고기   \n",
       "155         서울 마포구 합정동 381-64                     음식점 > 한식   \n",
       "\n",
       "            date_created                                             review  \n",
       "2    2020-03-29 23:28:20  연희동/연남동 감성 카페 연남장 (by 연남방앗간) 연남장 서울특별시 서대문구 연희...  \n",
       "154  2020-05-31 00:20:59  평양냉면 먹은지는 꽤 됐는데 다시 생각나서 이제야 올리게되는 냉면 포스팅 먹은건 너...  \n",
       "150  2020-05-31 00:20:20  여러분우웅 아녕아하세요요~~ 오랜만에.. 올립니다ㅠㅠ 요즘 바빠가지고 한번에!! 올...  \n",
       "52   2020-03-31 22:57:15  동남아 여행을 가고 싶은 요즘. 태국 맛집을 맛보고 싶어 힙한 홍대로 가봤습니다. ...  \n",
       "158  2020-05-31 00:24:11  동생과 함께 한 날씨가 매우 좋았던 어느 날의 기록. 낯설기도 익숙하기도 한 이 동...  \n",
       "17   2020-03-30 00:19:06  안녕하세요 동그리 입니다 :-) 이번 포스팅은 연희동에 위치한 카페겸 복합문화공간,...  \n",
       "105  2020-05-19 18:29:33  제주도 서쪽 카페 울트라 마린 (ULTRA MARINE) 울트라마린 제주특별자치도 ...  \n",
       "156  2020-05-31 00:22:35  비오는날 꼭 찾아가는 곳 연남동맛집 숨은골목 비오는 금요일 저녁 눈치작전 성공한듯 ...  \n",
       "6    2020-03-29 23:36:50  어느날 갑자기 꼬기가 먹고 싶다!!!!!!!!소꼬기!!!!! 를 외친 묘미동생을 위...  \n",
       "155  2020-05-31 00:21:43  합정역 맛집: 옥류관 출신 주방장이 만드는 평양냉면 [동무밥상] 이 집은 수요미식회...  "
      ]
     },
     "execution_count": 105,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.feature_extraction.text import CountVectorizer\n",
    "from sklearn.metrics.pairwise import cosine_similarity\n",
    "\n",
    "\n",
    "\n",
    "count_vect = CountVectorizer(min_df=2, ngram_range=(1,2))  # min_df : 적어도 n번은 나와야 카운팅 해주겠다는 뜻\n",
    "mini_place_review = count_vect.fit_transform(mini_place['review'])  # 분석에 활용하고자 하는 칼럼 넣음\n",
    "\n",
    "\n",
    "mini_place_simi = cosine_similarity(mini_place_review, mini_place_review)  # 분석용 데이터 간의 유사도. 2차원 nd로 되어있음\n",
    "\n",
    "# 솔직히 0.xxx값은 크게 관심 없고 순서가 중요하니까 ndarray에서 인덱스를 뽑아내는 argsort를 써줘야함\n",
    "\n",
    "mini_place_simi_sorted_ind = mini_place_simi.argsort()[:, ::-1]  # 인덱스 뽑아내주는 작업\n",
    "print(mini_place_simi_sorted_ind[:1])\n",
    "\n",
    "def find_simi_place(df, sorted_ind, place_name, top_n=10):\n",
    "    \n",
    "    place_title = df[df['name'] == place_name]\n",
    "    place_index = place_title.index.values\n",
    "    similar_indexes = sorted_ind[place_index, :(top_n)]  # 매개변수로 설정한 top n개 뽑아준다\n",
    "    \n",
    "    print(similar_indexes)\n",
    "    similar_indexes = similar_indexes.reshape(-1)     #2차원 array를 데이터프레임에서 쓰게 1차원 array로 변경\n",
    "    return df.iloc[similar_indexes]\n",
    "\n",
    "\n",
    "\n",
    "find_simi_place(mini_place, mini_place_simi_sorted_ind, '연남장',10)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[  2 103  52 158   6 154  17  24 156  36]]\n"
     ]
    }
   ],
   "source": [
    "find_simi_place(mini_place, mini_place_simi_sorted_ind, '연남장',10).to_csv(\"test_recom.csv\",  mode='a', header=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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