Pandas_101.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Use import pandas to pull .csv file"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "data1 = pd.read_csv('w_restaurant.csv')\n",
    "กรณีไฟล์อยู่ในโฟลเดอร์เดียวกัน ใช้ได้เลย"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Defind path to table files"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "wrest_PATH = 'C:\\\\Users\\\\User\\\\Downloads\\\\w_restaurant.csv'\n",
    "sclick_PATH = 'C:\\\\Users\\\\User\\\\Downloads\\\\search_click.csv'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "เพิ่ม \\ อีกตัวเพื่อป้องกันเรื่องโปรกแกรมสับสนในการค้นหาไฟล์"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Load files into memo"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "w_rest_df = pd.read_csv(wrest_PATH)\n",
    "s_click_df = pd.read_csv(sclick_PATH)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Look table"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
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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>name</th>\n",
       "      <th>domain_id</th>\n",
       "      <th>closed</th>\n",
       "      <th>city_name</th>\n",
       "      <th>zipcode</th>\n",
       "      <th>geohash</th>\n",
       "      <th>new_open</th>\n",
       "      <th>weighted_average_rating</th>\n",
       "      <th>number_of_chains</th>\n",
       "      <th>...</th>\n",
       "      <th>good_for_groups</th>\n",
       "      <th>good_for_kids</th>\n",
       "      <th>bookable</th>\n",
       "      <th>best_of_wongnai</th>\n",
       "      <th>wifi</th>\n",
       "      <th>number_of_seats</th>\n",
       "      <th>music_venue</th>\n",
       "      <th>hotel_restaurant</th>\n",
       "      <th>pet_friendly</th>\n",
       "      <th>delivery</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2</td>\n",
       "      <td>นครินทร์ทันตกรรม</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>Samut Songkhram</td>\n",
       "      <td>75000</td>\n",
       "      <td>w4rh7g3</td>\n",
       "      <td>0</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>13.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4</td>\n",
       "      <td>Corner House</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>Bangkok Metropolitan Region</td>\n",
       "      <td>12150</td>\n",
       "      <td>w4rx73h</td>\n",
       "      <td>0</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>5</td>\n",
       "      <td>วัดโลกยสุธาราม</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>Phra Nakhon Si Ayutthaya</td>\n",
       "      <td>13000</td>\n",
       "      <td>w4x98jk</td>\n",
       "      <td>0</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>6</td>\n",
       "      <td>นันท์คาราโอเกะ</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>Bangkok Metropolitan Region</td>\n",
       "      <td>10700</td>\n",
       "      <td>w4rqw9q</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>7</td>\n",
       "      <td>Buono Caffe</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>Bangkok Metropolitan Region</td>\n",
       "      <td>10220</td>\n",
       "      <td>w4rx4gd</td>\n",
       "      <td>0</td>\n",
       "      <td>3.738462</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>246842</th>\n",
       "      <td>340068</td>\n",
       "      <td>Sushi Tama</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>Bangkok Metropolitan Region</td>\n",
       "      <td>11130</td>\n",
       "      <td>w4rqtcb</td>\n",
       "      <td>0</td>\n",
       "      <td>3.663522</td>\n",
       "      <td>3.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>246843</th>\n",
       "      <td>340070</td>\n",
       "      <td>สะบายดี คาราโอเกะ</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>Bangkok Metropolitan Region</td>\n",
       "      <td>NaN</td>\n",
       "      <td>w4rwjcg</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>246844</th>\n",
       "      <td>340074</td>\n",
       "      <td>สเต็กแม่&amp;กาแฟลูก</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>Bangkok Metropolitan Region</td>\n",
       "      <td>10160</td>\n",
       "      <td>w4rmvxf</td>\n",
       "      <td>0</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>246845</th>\n",
       "      <td>340079</td>\n",
       "      <td>Mom Made</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>Nakhon Si Thammarat</td>\n",
       "      <td>80000</td>\n",
       "      <td>w1x04g6</td>\n",
       "      <td>0</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>246846</th>\n",
       "      <td>340091</td>\n",
       "      <td>ก๋วยเตี๋ยวหมูน้ำตก ก๋วยเตี๋ยวเรือทอง</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>Suphan Buri</td>\n",
       "      <td>72000</td>\n",
       "      <td>w4x4q7s</td>\n",
       "      <td>0</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>246847 rows × 32 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            id                                  name  domain_id  closed  \\\n",
       "0            2                      นครินทร์ทันตกรรม          2       0   \n",
       "1            4                          Corner House          1       0   \n",
       "2            5                        วัดโลกยสุธาราม          4       0   \n",
       "3            6                        นันท์คาราโอเกะ          1       0   \n",
       "4            7                           Buono Caffe          1       0   \n",
       "...        ...                                   ...        ...     ...   \n",
       "246842  340068                            Sushi Tama          1       0   \n",
       "246843  340070                     สะบายดี คาราโอเกะ          1       0   \n",
       "246844  340074                      สเต็กแม่&กาแฟลูก          1       0   \n",
       "246845  340079                              Mom Made          1       0   \n",
       "246846  340091  ก๋วยเตี๋ยวหมูน้ำตก ก๋วยเตี๋ยวเรือทอง          1       0   \n",
       "\n",
       "                          city_name zipcode  geohash  new_open  \\\n",
       "0                   Samut Songkhram   75000  w4rh7g3         0   \n",
       "1       Bangkok Metropolitan Region   12150  w4rx73h         0   \n",
       "2          Phra Nakhon Si Ayutthaya   13000  w4x98jk         0   \n",
       "3       Bangkok Metropolitan Region   10700  w4rqw9q         0   \n",
       "4       Bangkok Metropolitan Region   10220  w4rx4gd         0   \n",
       "...                             ...     ...      ...       ...   \n",
       "246842  Bangkok Metropolitan Region   11130  w4rqtcb         0   \n",
       "246843  Bangkok Metropolitan Region     NaN  w4rwjcg         0   \n",
       "246844  Bangkok Metropolitan Region   10160  w4rmvxf         0   \n",
       "246845          Nakhon Si Thammarat   80000  w1x04g6         0   \n",
       "246846                  Suphan Buri   72000  w4x4q7s         0   \n",
       "\n",
       "        weighted_average_rating  number_of_chains  ...  good_for_groups  \\\n",
       "0                      5.000000               NaN  ...              NaN   \n",
       "1                      2.000000               NaN  ...              NaN   \n",
       "2                      4.000000               NaN  ...              NaN   \n",
       "3                      0.000000               NaN  ...              NaN   \n",
       "4                      3.738462               NaN  ...              NaN   \n",
       "...                         ...               ...  ...              ...   \n",
       "246842                 3.663522               3.0  ...              0.0   \n",
       "246843                 0.000000               NaN  ...              1.0   \n",
       "246844                 4.000000               NaN  ...              1.0   \n",
       "246845                 5.000000               NaN  ...              1.0   \n",
       "246846                 3.000000               NaN  ...              NaN   \n",
       "\n",
       "        good_for_kids  bookable  best_of_wongnai  wifi  number_of_seats  \\\n",
       "0                 NaN       NaN              NaN   NaN             13.0   \n",
       "1                 NaN       NaN              NaN   NaN              NaN   \n",
       "2                 NaN       NaN              NaN   NaN              NaN   \n",
       "3                 NaN       NaN              NaN   NaN              NaN   \n",
       "4                 NaN       NaN              NaN   1.0              2.0   \n",
       "...               ...       ...              ...   ...              ...   \n",
       "246842            1.0       0.0              NaN   0.0              NaN   \n",
       "246843            NaN       1.0              NaN   NaN              2.0   \n",
       "246844            1.0       1.0              NaN   NaN              NaN   \n",
       "246845            1.0       1.0              NaN   NaN              NaN   \n",
       "246846            NaN       NaN              NaN   NaN              NaN   \n",
       "\n",
       "        music_venue hotel_restaurant  pet_friendly  delivery  \n",
       "0               NaN              NaN           NaN       NaN  \n",
       "1               NaN              NaN           NaN       NaN  \n",
       "2               NaN              NaN           NaN       NaN  \n",
       "3               NaN              NaN           NaN       NaN  \n",
       "4               NaN              NaN           NaN       NaN  \n",
       "...             ...              ...           ...       ...  \n",
       "246842          NaN              NaN           NaN       NaN  \n",
       "246843          NaN              NaN           NaN       NaN  \n",
       "246844          NaN              NaN           NaN       NaN  \n",
       "246845          NaN              NaN           NaN       NaN  \n",
       "246846          NaN              NaN           NaN       NaN  \n",
       "\n",
       "[246847 rows x 32 columns]"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "w_rest_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
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       "      <th>search_id</th>\n",
       "      <th>business_id</th>\n",
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       "      <td>11:38.9</td>\n",
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      "text/plain": [
       "  click_timestamp                             search_id  business_id  \\\n",
       "0         11:38.9  5c004472-5ecc-4159-9698-9f329b8e26e1     298839.0   \n",
       "1         30:51.3  5f34eca0-c04e-4c10-b600-54aa447dbd8e     171372.0   \n",
       "2         32:09.6  8dc6d1f0-11f1-4239-9dd2-5c1674c81224      16157.0   \n",
       "3         00:59.8  1a5810e9-3d1f-44b9-9a2e-26ede21c524b      89741.0   \n",
       "4         23:36.8  6bc75e0d-fc65-471e-9926-ff6be6708639     189564.0   \n",
       "\n",
       "   display_position  time_diff_nanos   ads  \n",
       "0                 0     2.222400e+15  True  \n",
       "1                 0     4.608380e+14  True  \n",
       "2                 0     2.581490e+15  True  \n",
       "3                 1     1.404580e+15  True  \n",
       "4                 1     9.333590e+14  True  "
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s_click_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>search_id</th>\n",
       "      <th>business_id</th>\n",
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       "      <th>ads</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1048570</th>\n",
       "      <td>05:28.8</td>\n",
       "      <td>99aab616-6fc5-4fbf-994b-4aebee7bb360</td>\n",
       "      <td>278479.0</td>\n",
       "      <td>8</td>\n",
       "      <td>2.072630e+11</td>\n",
       "      <td>False</td>\n",
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       "    <tr>\n",
       "      <th>1048571</th>\n",
       "      <td>15:28.8</td>\n",
       "      <td>d70a2c5d-ee94-4e4c-9f7c-c5faac6db12a</td>\n",
       "      <td>321885.0</td>\n",
       "      <td>8</td>\n",
       "      <td>6.931413e+10</td>\n",
       "      <td>False</td>\n",
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       "    <tr>\n",
       "      <th>1048572</th>\n",
       "      <td>11:49.4</td>\n",
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       "      <td>221035.0</td>\n",
       "      <td>8</td>\n",
       "      <td>3.105162e+10</td>\n",
       "      <td>False</td>\n",
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       "    <tr>\n",
       "      <th>1048573</th>\n",
       "      <td>10:33.7</td>\n",
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       "      <td>220572.0</td>\n",
       "      <td>8</td>\n",
       "      <td>7.848904e+10</td>\n",
       "      <td>False</td>\n",
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       "    <tr>\n",
       "      <th>1048574</th>\n",
       "      <td>13:51.4</td>\n",
       "      <td>5741d9cf-b9f1-441d-aa83-d2b0179bf874</td>\n",
       "      <td>310448.0</td>\n",
       "      <td>8</td>\n",
       "      <td>3.157757e+10</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        click_timestamp                             search_id  business_id  \\\n",
       "1048570         05:28.8  99aab616-6fc5-4fbf-994b-4aebee7bb360     278479.0   \n",
       "1048571         15:28.8  d70a2c5d-ee94-4e4c-9f7c-c5faac6db12a     321885.0   \n",
       "1048572         11:49.4  4c1de7fe-300e-41aa-9d01-914344fc3753     221035.0   \n",
       "1048573         10:33.7  2ec0ddec-e450-4b98-9779-42b77aa7eda5     220572.0   \n",
       "1048574         13:51.4  5741d9cf-b9f1-441d-aa83-d2b0179bf874     310448.0   \n",
       "\n",
       "         display_position  time_diff_nanos    ads  \n",
       "1048570                 8     2.072630e+11  False  \n",
       "1048571                 8     6.931413e+10  False  \n",
       "1048572                 8     3.105162e+10  False  \n",
       "1048573                 8     7.848904e+10  False  \n",
       "1048574                 8     3.157757e+10  False  "
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s_click_df.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1048575, 6)"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s_click_df.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data preprocessing\n",
    "### missing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "id                         False\n",
       "name                       False\n",
       "domain_id                  False\n",
       "closed                     False\n",
       "city_name                  False\n",
       "zipcode                     True\n",
       "geohash                    False\n",
       "new_open                   False\n",
       "weighted_average_rating    False\n",
       "number_of_chains            True\n",
       "number_of_bookmarks         True\n",
       "number_of_reviews          False\n",
       "number_of_product_tags     False\n",
       "total_number_of_views      False\n",
       "number_of_checkins         False\n",
       "number_of_menu_groups      False\n",
       "number_of_menu_photos      False\n",
       "category_name              False\n",
       "price_range                 True\n",
       "avg_price                   True\n",
       "parking                     True\n",
       "credit_card_accepted        True\n",
       "good_for_groups             True\n",
       "good_for_kids               True\n",
       "bookable                    True\n",
       "best_of_wongnai             True\n",
       "wifi                        True\n",
       "number_of_seats             True\n",
       "music_venue                 True\n",
       "hotel_restaurant            True\n",
       "pet_friendly                True\n",
       "delivery                    True\n",
       "dtype: bool"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "w_rest_df.isnull().any()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "True is have null in field"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Clean data\n",
    "#### path 1  delete with missing value"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
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       "      <th></th>\n",
       "      <th>id</th>\n",
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       "      <th>61</th>\n",
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       "      <th>114</th>\n",
       "      <td>224</td>\n",
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       "      <td>1</td>\n",
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       "      <th>115</th>\n",
       "      <td>225</td>\n",
       "      <td>Mr.shake</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>Bangkok Metropolitan Region</td>\n",
       "      <td>10150</td>\n",
       "      <td>w4rmky3</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>19.0</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>131</th>\n",
       "      <td>267</td>\n",
       "      <td>KFC</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>Bangkok Metropolitan Region</td>\n",
       "      <td>10170</td>\n",
       "      <td>w4rqq7v</td>\n",
       "      <td>0</td>\n",
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       "      <td>629.0</td>\n",
       "      <td>...</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>140</th>\n",
       "      <td>297</td>\n",
       "      <td>Miss Mamon</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Bangkok Metropolitan Region</td>\n",
       "      <td>10400</td>\n",
       "      <td>w4rw22t</td>\n",
       "      <td>0</td>\n",
       "      <td>3.147959</td>\n",
       "      <td>11.0</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
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       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>246722</th>\n",
       "      <td>339787</td>\n",
       "      <td>Chang coffee</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>Chiang Mai</td>\n",
       "      <td>50100</td>\n",
       "      <td>w5q6szv</td>\n",
       "      <td>0</td>\n",
       "      <td>3.537037</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>246752</th>\n",
       "      <td>339864</td>\n",
       "      <td>Hokkaido</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>Bangkok Metropolitan Region</td>\n",
       "      <td>10170</td>\n",
       "      <td>w4rqkf3</td>\n",
       "      <td>0</td>\n",
       "      <td>3.191781</td>\n",
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       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>246815</th>\n",
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       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>Nakhon Sawan</td>\n",
       "      <td>17000</td>\n",
       "      <td>w4xqf9k</td>\n",
       "      <td>0</td>\n",
       "      <td>3.166667</td>\n",
       "      <td>98.0</td>\n",
       "      <td>...</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>246834</th>\n",
       "      <td>340048</td>\n",
       "      <td>Kuppadeli</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Bangkok Metropolitan Region</td>\n",
       "      <td>10110</td>\n",
       "      <td>w4rw0e8</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>5.0</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>246842</th>\n",
       "      <td>340068</td>\n",
       "      <td>Sushi Tama</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>Bangkok Metropolitan Region</td>\n",
       "      <td>11130</td>\n",
       "      <td>w4rqtcb</td>\n",
       "      <td>0</td>\n",
       "      <td>3.663522</td>\n",
       "      <td>3.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>13669 rows × 32 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            id          name  domain_id  closed                    city_name  \\\n",
       "61         114      มานีมีนม          1       0  Bangkok Metropolitan Region   \n",
       "114        224  สเต็กลุงหนวด          1       0  Bangkok Metropolitan Region   \n",
       "115        225      Mr.shake          1       0  Bangkok Metropolitan Region   \n",
       "131        267           KFC          1       0  Bangkok Metropolitan Region   \n",
       "140        297    Miss Mamon          1       1  Bangkok Metropolitan Region   \n",
       "...        ...           ...        ...     ...                          ...   \n",
       "246722  339787  Chang coffee          1       0                   Chiang Mai   \n",
       "246752  339864      Hokkaido          1       0  Bangkok Metropolitan Region   \n",
       "246815  340000  สเต็กลุงหนวด          1       0                 Nakhon Sawan   \n",
       "246834  340048     Kuppadeli          1       1  Bangkok Metropolitan Region   \n",
       "246842  340068    Sushi Tama          1       0  Bangkok Metropolitan Region   \n",
       "\n",
       "       zipcode  geohash  new_open  weighted_average_rating  number_of_chains  \\\n",
       "61       10312  w4rw3e0         0                 3.793103              18.0   \n",
       "114      10120  w4rmwyw         0                 3.744000              98.0   \n",
       "115      10150  w4rmky3         0                 0.000000              19.0   \n",
       "131      10170  w4rqq7v         0                 2.846154             629.0   \n",
       "140      10400  w4rw22t         0                 3.147959              11.0   \n",
       "...        ...      ...       ...                      ...               ...   \n",
       "246722   50100  w5q6szv         0                 3.537037             102.0   \n",
       "246752   10170  w4rqkf3         0                 3.191781              22.0   \n",
       "246815   17000  w4xqf9k         0                 3.166667              98.0   \n",
       "246834   10110  w4rw0e8         0                 0.000000               5.0   \n",
       "246842   11130  w4rqtcb         0                 3.663522               3.0   \n",
       "\n",
       "        ...  good_for_groups  good_for_kids  bookable  best_of_wongnai  wifi  \\\n",
       "61      ...              NaN            NaN       NaN              NaN   1.0   \n",
       "114     ...              NaN            NaN       NaN              NaN   NaN   \n",
       "115     ...              NaN            NaN       NaN              NaN   NaN   \n",
       "131     ...              NaN            NaN       NaN              NaN   0.0   \n",
       "140     ...              NaN            NaN       NaN              NaN   NaN   \n",
       "...     ...              ...            ...       ...              ...   ...   \n",
       "246722  ...              1.0            0.0       NaN              NaN   1.0   \n",
       "246752  ...              NaN            NaN       NaN              NaN   NaN   \n",
       "246815  ...              1.0            1.0       NaN              NaN   NaN   \n",
       "246834  ...              NaN            NaN       NaN              NaN   NaN   \n",
       "246842  ...              0.0            1.0       0.0              NaN   0.0   \n",
       "\n",
       "        number_of_seats  music_venue hotel_restaurant  pet_friendly  delivery  \n",
       "61                  2.0          NaN              NaN           NaN       1.0  \n",
       "114                 NaN          NaN              NaN           NaN       NaN  \n",
       "115                 NaN          NaN              NaN           NaN       NaN  \n",
       "131                 3.0          NaN              NaN           NaN       NaN  \n",
       "140                 NaN          NaN              NaN           NaN       1.0  \n",
       "...                 ...          ...              ...           ...       ...  \n",
       "246722              2.0          NaN              NaN           NaN       NaN  \n",
       "246752              NaN          NaN              NaN           NaN       NaN  \n",
       "246815              NaN          NaN              NaN           NaN       NaN  \n",
       "246834              NaN          NaN              NaN           NaN       NaN  \n",
       "246842              NaN          NaN              NaN           NaN       NaN  \n",
       "\n",
       "[13669 rows x 32 columns]"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "w_rest_df.dropna(subset = ['zipcode','number_of_chains'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "w_delete= w_rest_df.dropna(subset = ['zipcode','number_of_chains'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### path 2  fill with default value"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "61        3.0\n",
       "114       3.0\n",
       "115       0.0\n",
       "131       3.0\n",
       "140       3.0\n",
       "         ... \n",
       "246722    1.0\n",
       "246752    3.0\n",
       "246815    3.0\n",
       "246834    0.0\n",
       "246842    3.0\n",
       "Name: parking, Length: 13669, dtype: float64"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "w_delete['parking'].fillna(0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "update เป็นแต่ละ field"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\User\\Miniconda3\\lib\\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": [
    "w_delete['parking'] = w_delete['parking'].fillna(0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "update field ในตารางนั้น(แทนค่าใหม่ทับไปเลย)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### select part of table by value ตัดตาราง\n",
    "เลือกตารางโดย = ชื่อตาราง[เงื่อนไข==จริง]\n",
    "\n",
    "ตัวอย่างเงื่อนไข"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "61        False\n",
       "114       False\n",
       "115       False\n",
       "131       False\n",
       "140       False\n",
       "          ...  \n",
       "246722    False\n",
       "246752    False\n",
       "246815    False\n",
       "246834    False\n",
       "246842    False\n",
       "Name: parking, Length: 13669, dtype: bool"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "w_delete['parking']>= 5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "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>id</th>\n",
       "      <th>name</th>\n",
       "      <th>domain_id</th>\n",
       "      <th>closed</th>\n",
       "      <th>city_name</th>\n",
       "      <th>zipcode</th>\n",
       "      <th>geohash</th>\n",
       "      <th>new_open</th>\n",
       "      <th>weighted_average_rating</th>\n",
       "      <th>number_of_chains</th>\n",
       "      <th>...</th>\n",
       "      <th>good_for_groups</th>\n",
       "      <th>good_for_kids</th>\n",
       "      <th>bookable</th>\n",
       "      <th>best_of_wongnai</th>\n",
       "      <th>wifi</th>\n",
       "      <th>number_of_seats</th>\n",
       "      <th>music_venue</th>\n",
       "      <th>hotel_restaurant</th>\n",
       "      <th>pet_friendly</th>\n",
       "      <th>delivery</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>61</th>\n",
       "      <td>114</td>\n",
       "      <td>มานีมีนม</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>Bangkok Metropolitan Region</td>\n",
       "      <td>10312</td>\n",
       "      <td>w4rw3e0</td>\n",
       "      <td>0</td>\n",
       "      <td>3.793103</td>\n",
       "      <td>18.0</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>114</th>\n",
       "      <td>224</td>\n",
       "      <td>สเต็กลุงหนวด</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>Bangkok Metropolitan Region</td>\n",
       "      <td>10120</td>\n",
       "      <td>w4rmwyw</td>\n",
       "      <td>0</td>\n",
       "      <td>3.744000</td>\n",
       "      <td>98.0</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>131</th>\n",
       "      <td>267</td>\n",
       "      <td>KFC</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>Bangkok Metropolitan Region</td>\n",
       "      <td>10170</td>\n",
       "      <td>w4rqq7v</td>\n",
       "      <td>0</td>\n",
       "      <td>2.846154</td>\n",
       "      <td>629.0</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>140</th>\n",
       "      <td>297</td>\n",
       "      <td>Miss Mamon</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Bangkok Metropolitan Region</td>\n",
       "      <td>10400</td>\n",
       "      <td>w4rw22t</td>\n",
       "      <td>0</td>\n",
       "      <td>3.147959</td>\n",
       "      <td>11.0</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>149</th>\n",
       "      <td>344</td>\n",
       "      <td>Inthanin Coffee</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>Bangkok Metropolitan Region</td>\n",
       "      <td>10700</td>\n",
       "      <td>w4rqx1q</td>\n",
       "      <td>0</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>213.0</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>246646</th>\n",
       "      <td>339670</td>\n",
       "      <td>Four Seasons Chinese Restaurant</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>Bangkok Metropolitan Region</td>\n",
       "      <td>10230</td>\n",
       "      <td>w4rw98b</td>\n",
       "      <td>0</td>\n",
       "      <td>3.781711</td>\n",
       "      <td>6.0</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>246684</th>\n",
       "      <td>339718</td>\n",
       "      <td>Jeffer Steak</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>Bangkok Metropolitan Region</td>\n",
       "      <td>10312</td>\n",
       "      <td>w4rw37q</td>\n",
       "      <td>0</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>87.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>246752</th>\n",
       "      <td>339864</td>\n",
       "      <td>Hokkaido</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>Bangkok Metropolitan Region</td>\n",
       "      <td>10170</td>\n",
       "      <td>w4rqkf3</td>\n",
       "      <td>0</td>\n",
       "      <td>3.191781</td>\n",
       "      <td>22.0</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>246815</th>\n",
       "      <td>340000</td>\n",
       "      <td>สเต็กลุงหนวด</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>Nakhon Sawan</td>\n",
       "      <td>17000</td>\n",
       "      <td>w4xqf9k</td>\n",
       "      <td>0</td>\n",
       "      <td>3.166667</td>\n",
       "      <td>98.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>246842</th>\n",
       "      <td>340068</td>\n",
       "      <td>Sushi Tama</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>Bangkok Metropolitan Region</td>\n",
       "      <td>11130</td>\n",
       "      <td>w4rqtcb</td>\n",
       "      <td>0</td>\n",
       "      <td>3.663522</td>\n",
       "      <td>3.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8634 rows × 32 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            id                             name  domain_id  closed  \\\n",
       "61         114                         มานีมีนม          1       0   \n",
       "114        224                     สเต็กลุงหนวด          1       0   \n",
       "131        267                              KFC          1       0   \n",
       "140        297                       Miss Mamon          1       1   \n",
       "149        344                  Inthanin Coffee          1       0   \n",
       "...        ...                              ...        ...     ...   \n",
       "246646  339670  Four Seasons Chinese Restaurant          1       0   \n",
       "246684  339718                     Jeffer Steak          1       0   \n",
       "246752  339864                         Hokkaido          1       0   \n",
       "246815  340000                     สเต็กลุงหนวด          1       0   \n",
       "246842  340068                       Sushi Tama          1       0   \n",
       "\n",
       "                          city_name zipcode  geohash  new_open  \\\n",
       "61      Bangkok Metropolitan Region   10312  w4rw3e0         0   \n",
       "114     Bangkok Metropolitan Region   10120  w4rmwyw         0   \n",
       "131     Bangkok Metropolitan Region   10170  w4rqq7v         0   \n",
       "140     Bangkok Metropolitan Region   10400  w4rw22t         0   \n",
       "149     Bangkok Metropolitan Region   10700  w4rqx1q         0   \n",
       "...                             ...     ...      ...       ...   \n",
       "246646  Bangkok Metropolitan Region   10230  w4rw98b         0   \n",
       "246684  Bangkok Metropolitan Region   10312  w4rw37q         0   \n",
       "246752  Bangkok Metropolitan Region   10170  w4rqkf3         0   \n",
       "246815                 Nakhon Sawan   17000  w4xqf9k         0   \n",
       "246842  Bangkok Metropolitan Region   11130  w4rqtcb         0   \n",
       "\n",
       "        weighted_average_rating  number_of_chains  ...  good_for_groups  \\\n",
       "61                     3.793103              18.0  ...              NaN   \n",
       "114                    3.744000              98.0  ...              NaN   \n",
       "131                    2.846154             629.0  ...              NaN   \n",
       "140                    3.147959              11.0  ...              NaN   \n",
       "149                    3.000000             213.0  ...              NaN   \n",
       "...                         ...               ...  ...              ...   \n",
       "246646                 3.781711               6.0  ...              NaN   \n",
       "246684                 4.000000              87.0  ...              1.0   \n",
       "246752                 3.191781              22.0  ...              NaN   \n",
       "246815                 3.166667              98.0  ...              1.0   \n",
       "246842                 3.663522               3.0  ...              0.0   \n",
       "\n",
       "        good_for_kids  bookable  best_of_wongnai  wifi  number_of_seats  \\\n",
       "61                NaN       NaN              NaN   1.0              2.0   \n",
       "114               NaN       NaN              NaN   NaN              NaN   \n",
       "131               NaN       NaN              NaN   0.0              3.0   \n",
       "140               NaN       NaN              NaN   NaN              NaN   \n",
       "149               NaN       NaN              NaN   0.0              2.0   \n",
       "...               ...       ...              ...   ...              ...   \n",
       "246646            NaN       NaN              NaN   0.0              3.0   \n",
       "246684            1.0       1.0              NaN   NaN              NaN   \n",
       "246752            NaN       NaN              NaN   NaN              NaN   \n",
       "246815            1.0       NaN              NaN   NaN              NaN   \n",
       "246842            1.0       0.0              NaN   0.0              NaN   \n",
       "\n",
       "        music_venue hotel_restaurant  pet_friendly  delivery  \n",
       "61              NaN              NaN           NaN       1.0  \n",
       "114             NaN              NaN           NaN       NaN  \n",
       "131             NaN              NaN           NaN       NaN  \n",
       "140             NaN              NaN           NaN       1.0  \n",
       "149             NaN              NaN           NaN       NaN  \n",
       "...             ...              ...           ...       ...  \n",
       "246646          NaN              NaN           NaN       NaN  \n",
       "246684          NaN              NaN           NaN       NaN  \n",
       "246752          NaN              NaN           NaN       NaN  \n",
       "246815          NaN              NaN           NaN       NaN  \n",
       "246842          NaN              NaN           NaN       NaN  \n",
       "\n",
       "[8634 rows x 32 columns]"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "w_delete[w_delete['parking']>= 3]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## HW select all returants from ur hometown"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "restaurant_df = pd.read_csv(wrest_PATH)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'Abu Dhabi',\n",
       " 'Aichi-ken',\n",
       " 'Akershus',\n",
       " 'Akita-ken',\n",
       " 'Al Farwaniyah Governorate',\n",
       " 'Alabama',\n",
       " 'Alaska',\n",
       " 'Alberta',\n",
       " 'Amnat Charoen',\n",
       " 'Andalucía',\n",
       " 'Ang Thong',\n",
       " 'Aomori-ken',\n",
       " 'Appenzell Ausserrhoden',\n",
       " 'Appenzell Innerrhoden',\n",
       " 'Aragón',\n",
       " 'Argyll and Bute',\n",
       " 'Arizona',\n",
       " 'Attica',\n",
       " 'Auckland',\n",
       " 'Australia',\n",
       " 'Auvergne-Rhône-Alpes',\n",
       " 'Aydın',\n",
       " 'Ba Ria - Vung Tau',\n",
       " 'Baden-Wurttemberg',\n",
       " 'Bago Region',\n",
       " 'Bahia',\n",
       " 'Bali',\n",
       " 'Balochistan',\n",
       " 'Bangkok Metropolitan Region',\n",
       " 'Banteay Meanchey Province',\n",
       " 'Banten',\n",
       " 'Basel-Stadt',\n",
       " 'Basilicata',\n",
       " 'Basrah',\n",
       " 'Battambang',\n",
       " 'Bay Of Plenty',\n",
       " 'Bayern',\n",
       " 'Berlin',\n",
       " 'Bern',\n",
       " 'Bihar',\n",
       " 'Binh Thuan',\n",
       " 'Binh Thuan province',\n",
       " 'Borno',\n",
       " 'Bratislava',\n",
       " 'Bremen',\n",
       " 'Bretagne',\n",
       " 'British Columbia',\n",
       " 'Brussels Hoofdstedelijk Gewest',\n",
       " 'Buckinghamshire',\n",
       " 'Budapest',\n",
       " 'Bueng Kan',\n",
       " 'Buri Ram',\n",
       " 'Busan',\n",
       " 'Buskerud',\n",
       " 'C2 MALL',\n",
       " 'CALABARZON',\n",
       " 'Cairo Governorate',\n",
       " 'California',\n",
       " 'Cambodia',\n",
       " 'Canterbury',\n",
       " 'Capital Governorate',\n",
       " 'Capital Region',\n",
       " 'Capital Region of Denmark',\n",
       " 'Castilla-La Mancha',\n",
       " 'Catalonia',\n",
       " 'Central Development Region',\n",
       " 'Central Visayas',\n",
       " 'Centre-Val de Loire',\n",
       " 'Chachoengsao',\n",
       " 'Chai Nat',\n",
       " 'Chaiyaphum',\n",
       " 'Champasak Province',\n",
       " 'Changhua County',\n",
       " 'Chanthaburi',\n",
       " 'Chiang Mai',\n",
       " 'Chiang Rai',\n",
       " 'Chiayi City',\n",
       " 'Chiayi County',\n",
       " 'Chiba-ken',\n",
       " 'China',\n",
       " 'Chon Buri',\n",
       " 'Chongqing Shi',\n",
       " 'Chumphon',\n",
       " 'Chungcheongnam-do',\n",
       " 'City of Zagreb',\n",
       " 'Colorado',\n",
       " 'Comunidad de Madrid',\n",
       " 'County Cork',\n",
       " 'County Dublin',\n",
       " 'County Galway',\n",
       " 'County Kerry',\n",
       " 'Cusco',\n",
       " 'Daegu',\n",
       " 'Daejeon',\n",
       " 'Daerah Brunei-Muara',\n",
       " 'Daerah Istimewa Yogyakarta',\n",
       " 'Dar es Salaam',\n",
       " 'Delhi',\n",
       " 'Denizli',\n",
       " 'Departamento de La Paz',\n",
       " 'Departamento de Potosí',\n",
       " 'District of Columbia',\n",
       " 'Dubai',\n",
       " 'Dubrovačko-neretvanska županija',\n",
       " 'Ehime-ken',\n",
       " 'England',\n",
       " 'Espoo',\n",
       " 'Federal',\n",
       " 'Finland',\n",
       " 'Finnmark',\n",
       " 'Florida',\n",
       " 'Fribourg',\n",
       " 'Fujian Sheng',\n",
       " 'Fukui-ken',\n",
       " 'Fukuoka-ken',\n",
       " 'Fukushima-ken',\n",
       " 'Gangwon-do',\n",
       " 'Gelderland',\n",
       " 'Geneva',\n",
       " 'George Town',\n",
       " 'Georgia',\n",
       " 'Gifu-ken',\n",
       " 'Gloucestershire',\n",
       " 'Gombe',\n",
       " 'Grand Est',\n",
       " 'Graubünden',\n",
       " 'Greater London',\n",
       " 'Greater Manchester',\n",
       " 'Guangdong Sheng',\n",
       " 'Guangxi Zhuangzuzizhiqu',\n",
       " 'Guinea',\n",
       " 'Gunma-ken',\n",
       " 'Gyeonggi-do',\n",
       " 'Gyeongsangbuk-do',\n",
       " 'Hainan Sheng',\n",
       " 'Hampshire',\n",
       " 'Harju maakond',\n",
       " 'Hauts-de-France',\n",
       " 'Hawaii',\n",
       " 'Hessen',\n",
       " 'Hiiraan',\n",
       " 'Hiroshima-ken',\n",
       " 'Hokkaido',\n",
       " 'Hordaland',\n",
       " 'Hsinchu City',\n",
       " 'Hsinchu County',\n",
       " 'Hua Hin',\n",
       " 'Hunan',\n",
       " 'Hyogo-ken',\n",
       " 'Hà Nội',\n",
       " 'Höfn í Hornafirði',\n",
       " 'Hồ Chí Minh',\n",
       " 'Ibaraki-ken',\n",
       " 'Iceland',\n",
       " 'Illinois',\n",
       " 'Incheon',\n",
       " 'India',\n",
       " 'Inner Mongolia',\n",
       " 'Ireland',\n",
       " 'Ishikawa-ken',\n",
       " 'Istanbul',\n",
       " 'Iwate-ken',\n",
       " 'Jakarta Capital Region',\n",
       " 'Jawa Barat',\n",
       " 'Jawa Timur',\n",
       " 'Jeju-do',\n",
       " 'Jeollabuk-do',\n",
       " 'Jharkhand',\n",
       " 'Jiangsu Sheng',\n",
       " 'Jiangxi Sheng',\n",
       " 'Jihočeský kraj',\n",
       " 'Johor',\n",
       " 'Județul Constanța',\n",
       " 'Kagoshima',\n",
       " 'Kalasin',\n",
       " 'Kamphaeng Phet',\n",
       " 'Kanagawa-ken',\n",
       " 'Kanchanaburi',\n",
       " 'Kaohsiung City',\n",
       " 'Karlovy Vary Region',\n",
       " 'Karnataka',\n",
       " 'Kayes',\n",
       " 'Kedah',\n",
       " 'Keelung City',\n",
       " 'Kelantan',\n",
       " 'Kepulauan Riau',\n",
       " 'Khammouane Province',\n",
       " 'Khartoum',\n",
       " 'Khon Kaen',\n",
       " 'Khánh Hoà',\n",
       " 'Kinshasa',\n",
       " 'Koh Kong Province',\n",
       " 'Kowloon',\n",
       " 'Krabi',\n",
       " 'Kumamoto-ken',\n",
       " 'Kyoto-fu',\n",
       " 'København',\n",
       " 'Lac',\n",
       " 'Lampang',\n",
       " 'Lamphun',\n",
       " 'Lancashire',\n",
       " 'Lao Cai',\n",
       " 'Lazio',\n",
       " \"Leningradskaya oblast'\",\n",
       " 'Liaoning',\n",
       " 'Liguria',\n",
       " 'Lima',\n",
       " 'Limerick',\n",
       " 'Lisboa',\n",
       " 'Lithuania',\n",
       " 'Ličko-senjska županija',\n",
       " 'Loei',\n",
       " 'Lombardia',\n",
       " 'Lopburi',\n",
       " 'Louisiana',\n",
       " 'Luang Prabang Province',\n",
       " 'Luxembourg',\n",
       " 'Luxor Governorate',\n",
       " 'Luzern',\n",
       " 'Lâm Đồng',\n",
       " 'Macau',\n",
       " 'Madrid',\n",
       " 'Magway Region',\n",
       " 'Maha Sarakham',\n",
       " 'Maharashtra',\n",
       " 'Malacca',\n",
       " 'Maldives',\n",
       " 'Mandalay Region',\n",
       " 'Marlborough',\n",
       " 'Maryland',\n",
       " 'Masovian Voivodeship',\n",
       " 'Massachusetts',\n",
       " 'Mayo',\n",
       " 'Metro Manila',\n",
       " 'Michigan',\n",
       " 'Mid-Western Development Region',\n",
       " 'Mie-ken',\n",
       " 'Minnesota',\n",
       " 'Miyagi-ken',\n",
       " 'Mon State',\n",
       " 'Moscow',\n",
       " \"Moskovskaya oblast'\",\n",
       " 'Mukdahan',\n",
       " 'Munxar',\n",
       " \"Murmanskaya oblast'\",\n",
       " 'Nagano-ken',\n",
       " 'Nagasaki-ken',\n",
       " 'Nakhon Nayok',\n",
       " 'Nakhon Phanom',\n",
       " 'Nakhon Ratchasima',\n",
       " 'Nakhon Sawan',\n",
       " 'Nakhon Si Thammarat',\n",
       " 'Nan',\n",
       " 'Nantou County',\n",
       " 'Nara-ken',\n",
       " 'Narathiwat',\n",
       " 'Naxos',\n",
       " 'Negeri Sembilan',\n",
       " 'Nevada',\n",
       " 'Nevsehir',\n",
       " 'New Hampshire',\n",
       " 'New Jersey',\n",
       " 'New South Wales',\n",
       " 'New Taipei City',\n",
       " 'New Territories',\n",
       " 'New York',\n",
       " 'New Zealand',\n",
       " 'Newfoundland and Labrador',\n",
       " 'Nghệ An',\n",
       " 'Niedersachsen',\n",
       " 'Niederösterreich',\n",
       " 'Niigata-ken',\n",
       " 'Nong Bua Lam Phu',\n",
       " 'Nong Khai',\n",
       " 'Noord-Holland',\n",
       " 'Nordrhein-Westfalen',\n",
       " 'Normandie',\n",
       " 'North Central Province',\n",
       " 'Northern Ireland',\n",
       " 'Northern Territory',\n",
       " 'Nouvelle-Aquitaine',\n",
       " \"Novosibirskaya oblast'\",\n",
       " 'Nusa Tenggara Barat',\n",
       " 'Oaxaca',\n",
       " 'Oberösterreich',\n",
       " 'Obwalden',\n",
       " 'Ohio',\n",
       " 'Okayama-ken',\n",
       " 'Okinawa-ken',\n",
       " 'Oklahoma',\n",
       " 'Ontario',\n",
       " 'Oregon',\n",
       " 'Oslo',\n",
       " 'Otago',\n",
       " 'Pahang',\n",
       " 'Pakistan',\n",
       " 'Palau',\n",
       " 'Pardubice Region',\n",
       " 'Pattani',\n",
       " 'Penang',\n",
       " 'Pennsylvania',\n",
       " 'Perak',\n",
       " 'Phang-nga',\n",
       " 'Phatthalung',\n",
       " 'Phayao',\n",
       " 'Phetchabun',\n",
       " 'Phetchaburi',\n",
       " 'Phichit',\n",
       " 'Phitsanulok',\n",
       " 'Phnom Penh',\n",
       " 'Phra Nakhon Si Ayutthaya',\n",
       " 'Phrae',\n",
       " 'Phuket',\n",
       " 'Piemonte',\n",
       " 'Plzeň Region',\n",
       " 'Porto',\n",
       " 'Postojna',\n",
       " 'Prachin Buri',\n",
       " 'Prachuap Khiri Khan',\n",
       " 'Prague',\n",
       " 'Preah Sihanouk',\n",
       " 'Primorskiy kray',\n",
       " 'Primorsko-goranska županija',\n",
       " \"Provence-Alpes-Côte d'Azur\",\n",
       " 'Pulau Pinang',\n",
       " 'Punjab',\n",
       " 'Qatar',\n",
       " 'Qinghai Sheng',\n",
       " 'Quebec',\n",
       " 'Queensland',\n",
       " 'Quảng Nam',\n",
       " 'Quảng Ninh',\n",
       " 'Ranong',\n",
       " 'Ratchaburi',\n",
       " 'Rayong',\n",
       " 'Region Zealand',\n",
       " 'Rheinland-Pfalz',\n",
       " 'Riau',\n",
       " 'Roi Et',\n",
       " 'Région de Kidal',\n",
       " 'Rīgas pilsēta',\n",
       " 'Sa Kaeo',\n",
       " 'Sabah',\n",
       " 'Sachsen-Anhalt',\n",
       " 'Saga-ken',\n",
       " 'Saint Petersburg',\n",
       " 'Sainyabuli Province',\n",
       " 'Saitama-ken',\n",
       " 'Sakon Nakhon',\n",
       " 'Salzburg',\n",
       " 'Samut Songkhram',\n",
       " 'Sankt Gallen',\n",
       " 'Saraburi',\n",
       " 'Satun',\n",
       " 'Savannakhet Province',\n",
       " 'Saxony',\n",
       " 'Schaffhausen',\n",
       " 'Scotland',\n",
       " 'Selangor',\n",
       " 'Seoul',\n",
       " 'Setúbal',\n",
       " 'Shaanxi Sheng',\n",
       " 'Shan',\n",
       " 'Shandong Sheng',\n",
       " 'Shanghai',\n",
       " 'Shanxi Sheng',\n",
       " 'Shiga-ken',\n",
       " 'Shizuoka-ken',\n",
       " 'Si Sa Ket',\n",
       " 'Sichuan Sheng',\n",
       " 'Siem Reap',\n",
       " 'Sing Buri',\n",
       " 'Singapore',\n",
       " 'Slough',\n",
       " 'South Carolina',\n",
       " 'Southern Islands',\n",
       " 'Southland',\n",
       " 'Splitsko-dalmatinska županija',\n",
       " 'Stavropolskiy kray',\n",
       " 'Stockholms län',\n",
       " 'Sukhothai',\n",
       " 'Sumatera Utara',\n",
       " 'Suphan Buri',\n",
       " 'Surat Thani',\n",
       " 'Surin',\n",
       " 'Svay Rieng',\n",
       " 'Taichung City',\n",
       " 'Tainan City',\n",
       " 'Taipei City',\n",
       " 'Tak',\n",
       " 'Tamil Nadu',\n",
       " 'Tanintharyi Region',\n",
       " 'Taoyuan City',\n",
       " 'Tasmania',\n",
       " 'Tehran',\n",
       " 'Telangana',\n",
       " 'Texas',\n",
       " 'Thailand',\n",
       " 'Thimphu',\n",
       " 'Thua Thien Hue',\n",
       " 'Tianjin',\n",
       " 'Ticino',\n",
       " 'Tirol',\n",
       " 'Tochigi-ken',\n",
       " 'Tokyo-to',\n",
       " 'Tottori-ken',\n",
       " 'Toyama-ken',\n",
       " 'Trabzon',\n",
       " 'Trang',\n",
       " 'Trat',\n",
       " 'Troms',\n",
       " 'Tuscany',\n",
       " 'Ubon Ratchathani',\n",
       " 'Udon Thani',\n",
       " 'Uri',\n",
       " 'Utah',\n",
       " 'Uthai Thani',\n",
       " 'Utrecht',\n",
       " 'Uttar Pradesh',\n",
       " 'Uttaradit',\n",
       " 'Vaduz',\n",
       " 'Valais',\n",
       " 'Valletta',\n",
       " 'Vantaa',\n",
       " 'Vaud',\n",
       " 'Veneto',\n",
       " 'Victoria',\n",
       " 'Vientiane Prefecture',\n",
       " 'Vientiane Province',\n",
       " 'Virginia',\n",
       " 'Vlaanderen',\n",
       " \"Vladimirskaya oblast'\",\n",
       " 'Vorarlberg',\n",
       " 'Waikato',\n",
       " 'Wakayama-ken',\n",
       " 'Wales',\n",
       " 'Wallonie',\n",
       " 'Washington',\n",
       " 'Waterford',\n",
       " 'West Bengal',\n",
       " 'West Midlands',\n",
       " 'West Sussex',\n",
       " 'Western Australia',\n",
       " 'Western Cape',\n",
       " 'Wien',\n",
       " 'Wilayah Persekutuan Putrajaya',\n",
       " 'Wisconsin',\n",
       " 'Województwo podkarpackie',\n",
       " 'Xinjiang Weiwuerzizhiqu',\n",
       " 'Xizang (Tibet)',\n",
       " 'Yala',\n",
       " 'Yamagata-ken',\n",
       " 'Yamaguchi-ken',\n",
       " 'Yamanashi-ken',\n",
       " 'Yangon',\n",
       " 'Yasothon',\n",
       " 'Yilan County',\n",
       " 'Yunlin County',\n",
       " 'Yunnan Sheng',\n",
       " 'Zhejiang',\n",
       " 'Zuid-Holland',\n",
       " 'Zurich',\n",
       " 'kujawsko-pomorskie',\n",
       " 'lubelskie',\n",
       " 'małopolskie',\n",
       " 'pomorskie',\n",
       " 'tỉnh Kiên Giang',\n",
       " 'wielkopolskie',\n",
       " 'Çanakkale',\n",
       " 'Île-de-France',\n",
       " 'Điện Biên',\n",
       " 'Đà Nẵng',\n",
       " 'İstanbul',\n",
       " 'İzmir',\n",
       " 'Ōita-ken',\n",
       " 'Ōsaka-fu',\n",
       " 'محافظة المحرق',\n",
       " 'สงขลา',\n",
       " 'เขาใหญ่'}"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "set(restaurant_df['city_name'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [],
   "source": [
    "restinnakhon = restaurant_df[restaurant_df['city_name']=='Nakhon Si Thammarat']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## save table to file "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [],
   "source": [
    "restinnakhon.to_csv('restaurantinnakhon.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [],
   "source": [
    "restinnakhon.to_csv('test.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "                                                                END"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# week2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "wrest_PATH = 'C:\\\\Users\\\\User\\\\Downloads\\\\w_restaurant.csv'\n",
    "sclick_PATH = 'C:\\\\Users\\\\User\\\\Downloads\\\\search_click.csv'\n",
    "w_rest_df = pd.read_csv(wrest_PATH)\n",
    "s_click_df = pd.read_csv(sclick_PATH)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Select some columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "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>id</th>\n",
       "      <th>name</th>\n",
       "      <th>domain_id</th>\n",
       "      <th>closed</th>\n",
       "      <th>city_name</th>\n",
       "      <th>zipcode</th>\n",
       "      <th>geohash</th>\n",
       "      <th>new_open</th>\n",
       "      <th>weighted_average_rating</th>\n",
       "      <th>number_of_chains</th>\n",
       "      <th>...</th>\n",
       "      <th>good_for_groups</th>\n",
       "      <th>good_for_kids</th>\n",
       "      <th>bookable</th>\n",
       "      <th>best_of_wongnai</th>\n",
       "      <th>wifi</th>\n",
       "      <th>number_of_seats</th>\n",
       "      <th>music_venue</th>\n",
       "      <th>hotel_restaurant</th>\n",
       "      <th>pet_friendly</th>\n",
       "      <th>delivery</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2</td>\n",
       "      <td>นครินทร์ทันตกรรม</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>Samut Songkhram</td>\n",
       "      <td>75000</td>\n",
       "      <td>w4rh7g3</td>\n",
       "      <td>0</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>13.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4</td>\n",
       "      <td>Corner House</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>Bangkok Metropolitan Region</td>\n",
       "      <td>12150</td>\n",
       "      <td>w4rx73h</td>\n",
       "      <td>0</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>5</td>\n",
       "      <td>วัดโลกยสุธาราม</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>Phra Nakhon Si Ayutthaya</td>\n",
       "      <td>13000</td>\n",
       "      <td>w4x98jk</td>\n",
       "      <td>0</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>6</td>\n",
       "      <td>นันท์คาราโอเกะ</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>Bangkok Metropolitan Region</td>\n",
       "      <td>10700</td>\n",
       "      <td>w4rqw9q</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>7</td>\n",
       "      <td>Buono Caffe</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>Bangkok Metropolitan Region</td>\n",
       "      <td>10220</td>\n",
       "      <td>w4rx4gd</td>\n",
       "      <td>0</td>\n",
       "      <td>3.738462</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 32 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   id              name  domain_id  closed                    city_name  \\\n",
       "0   2  นครินทร์ทันตกรรม          2       0              Samut Songkhram   \n",
       "1   4      Corner House          1       0  Bangkok Metropolitan Region   \n",
       "2   5    วัดโลกยสุธาราม          4       0     Phra Nakhon Si Ayutthaya   \n",
       "3   6    นันท์คาราโอเกะ          1       0  Bangkok Metropolitan Region   \n",
       "4   7       Buono Caffe          1       0  Bangkok Metropolitan Region   \n",
       "\n",
       "  zipcode  geohash  new_open  weighted_average_rating  number_of_chains  ...  \\\n",
       "0   75000  w4rh7g3         0                 5.000000               NaN  ...   \n",
       "1   12150  w4rx73h         0                 2.000000               NaN  ...   \n",
       "2   13000  w4x98jk         0                 4.000000               NaN  ...   \n",
       "3   10700  w4rqw9q         0                 0.000000               NaN  ...   \n",
       "4   10220  w4rx4gd         0                 3.738462               NaN  ...   \n",
       "\n",
       "   good_for_groups  good_for_kids  bookable  best_of_wongnai  wifi  \\\n",
       "0              NaN            NaN       NaN              NaN   NaN   \n",
       "1              NaN            NaN       NaN              NaN   NaN   \n",
       "2              NaN            NaN       NaN              NaN   NaN   \n",
       "3              NaN            NaN       NaN              NaN   NaN   \n",
       "4              NaN            NaN       NaN              NaN   1.0   \n",
       "\n",
       "   number_of_seats  music_venue hotel_restaurant  pet_friendly  delivery  \n",
       "0             13.0          NaN              NaN           NaN       NaN  \n",
       "1              NaN          NaN              NaN           NaN       NaN  \n",
       "2              NaN          NaN              NaN           NaN       NaN  \n",
       "3              NaN          NaN              NaN           NaN       NaN  \n",
       "4              2.0          NaN              NaN           NaN       NaN  \n",
       "\n",
       "[5 rows x 32 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "w_rest_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>name</th>\n",
       "      <th>city_name</th>\n",
       "      <th>zipcode</th>\n",
       "      <th>weighted_average_rating</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>นครินทร์ทันตกรรม</td>\n",
       "      <td>Samut Songkhram</td>\n",
       "      <td>75000</td>\n",
       "      <td>5.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Corner House</td>\n",
       "      <td>Bangkok Metropolitan Region</td>\n",
       "      <td>12150</td>\n",
       "      <td>2.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>วัดโลกยสุธาราม</td>\n",
       "      <td>Phra Nakhon Si Ayutthaya</td>\n",
       "      <td>13000</td>\n",
       "      <td>4.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>นันท์คาราโอเกะ</td>\n",
       "      <td>Bangkok Metropolitan Region</td>\n",
       "      <td>10700</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Buono Caffe</td>\n",
       "      <td>Bangkok Metropolitan Region</td>\n",
       "      <td>10220</td>\n",
       "      <td>3.738462</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               name                    city_name zipcode  \\\n",
       "0  นครินทร์ทันตกรรม              Samut Songkhram   75000   \n",
       "1      Corner House  Bangkok Metropolitan Region   12150   \n",
       "2    วัดโลกยสุธาราม     Phra Nakhon Si Ayutthaya   13000   \n",
       "3    นันท์คาราโอเกะ  Bangkok Metropolitan Region   10700   \n",
       "4       Buono Caffe  Bangkok Metropolitan Region   10220   \n",
       "\n",
       "   weighted_average_rating  \n",
       "0                 5.000000  \n",
       "1                 2.000000  \n",
       "2                 4.000000  \n",
       "3                 0.000000  \n",
       "4                 3.738462  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "smt = w_rest_df[['name','city_name','zipcode','weighted_average_rating']]\n",
    "smt.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "[ใส่[list]ค่า]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>click_timestamp</th>\n",
       "      <th>search_id</th>\n",
       "      <th>business_id</th>\n",
       "      <th>display_position</th>\n",
       "      <th>time_diff_nanos</th>\n",
       "      <th>ads</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>11:38.9</td>\n",
       "      <td>5c004472-5ecc-4159-9698-9f329b8e26e1</td>\n",
       "      <td>298839.0</td>\n",
       "      <td>0</td>\n",
       "      <td>2.222400e+15</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>30:51.3</td>\n",
       "      <td>5f34eca0-c04e-4c10-b600-54aa447dbd8e</td>\n",
       "      <td>171372.0</td>\n",
       "      <td>0</td>\n",
       "      <td>4.608380e+14</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>32:09.6</td>\n",
       "      <td>8dc6d1f0-11f1-4239-9dd2-5c1674c81224</td>\n",
       "      <td>16157.0</td>\n",
       "      <td>0</td>\n",
       "      <td>2.581490e+15</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>00:59.8</td>\n",
       "      <td>1a5810e9-3d1f-44b9-9a2e-26ede21c524b</td>\n",
       "      <td>89741.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1.404580e+15</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>23:36.8</td>\n",
       "      <td>6bc75e0d-fc65-471e-9926-ff6be6708639</td>\n",
       "      <td>189564.0</td>\n",
       "      <td>1</td>\n",
       "      <td>9.333590e+14</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  click_timestamp                             search_id  business_id  \\\n",
       "0         11:38.9  5c004472-5ecc-4159-9698-9f329b8e26e1     298839.0   \n",
       "1         30:51.3  5f34eca0-c04e-4c10-b600-54aa447dbd8e     171372.0   \n",
       "2         32:09.6  8dc6d1f0-11f1-4239-9dd2-5c1674c81224      16157.0   \n",
       "3         00:59.8  1a5810e9-3d1f-44b9-9a2e-26ede21c524b      89741.0   \n",
       "4         23:36.8  6bc75e0d-fc65-471e-9926-ff6be6708639     189564.0   \n",
       "\n",
       "   display_position  time_diff_nanos   ads  \n",
       "0                 0     2.222400e+15  True  \n",
       "1                 0     4.608380e+14  True  \n",
       "2                 0     2.581490e+15  True  \n",
       "3                 1     1.404580e+15  True  \n",
       "4                 1     9.333590e+14  True  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s_click_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Combine table\n",
    "#### Add column from another table"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "add key"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\User\\Miniconda3\\lib\\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": [
    "smt['id'] = w_rest_df['id']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>name</th>\n",
       "      <th>city_name</th>\n",
       "      <th>zipcode</th>\n",
       "      <th>weighted_average_rating</th>\n",
       "      <th>id</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>นครินทร์ทันตกรรม</td>\n",
       "      <td>Samut Songkhram</td>\n",
       "      <td>75000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Corner House</td>\n",
       "      <td>Bangkok Metropolitan Region</td>\n",
       "      <td>12150</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>วัดโลกยสุธาราม</td>\n",
       "      <td>Phra Nakhon Si Ayutthaya</td>\n",
       "      <td>13000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>นันท์คาราโอเกะ</td>\n",
       "      <td>Bangkok Metropolitan Region</td>\n",
       "      <td>10700</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Buono Caffe</td>\n",
       "      <td>Bangkok Metropolitan Region</td>\n",
       "      <td>10220</td>\n",
       "      <td>3.738462</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               name                    city_name zipcode  \\\n",
       "0  นครินทร์ทันตกรรม              Samut Songkhram   75000   \n",
       "1      Corner House  Bangkok Metropolitan Region   12150   \n",
       "2    วัดโลกยสุธาราม     Phra Nakhon Si Ayutthaya   13000   \n",
       "3    นันท์คาราโอเกะ  Bangkok Metropolitan Region   10700   \n",
       "4       Buono Caffe  Bangkok Metropolitan Region   10220   \n",
       "\n",
       "   weighted_average_rating  id  \n",
       "0                 5.000000   2  \n",
       "1                 2.000000   4  \n",
       "2                 4.000000   5  \n",
       "3                 0.000000   6  \n",
       "4                 3.738462   7  "
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "smt.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### use .map()\n",
    "##### add buname in searchclick"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "s_click_df['Business_name'] = s_click_df['business_id'].map(smt.set_index('id')['name'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "s_click_df['Business_name'] create new column\n",
    "\n",
    "s_click_df['business_id'] = key ตารางหลัก\n",
    "\n",
    ".set_index('id')['name'] = key ตารางรอง ตามด้วย[ค่าที่จะใส่มาในคอลัมน์ใหม่]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <td>True</td>\n",
       "      <td>ปูดองอันยอง Eight</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>30:51.3</td>\n",
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       "      <td>Yoshinoya</td>\n",
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       "      <th>2</th>\n",
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       "      <td>0</td>\n",
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       "      <td>True</td>\n",
       "      <td>The Hub Cafe and Eatery</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>00:59.8</td>\n",
       "      <td>1a5810e9-3d1f-44b9-9a2e-26ede21c524b</td>\n",
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       "      <td>1</td>\n",
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       "      <td>True</td>\n",
       "      <td>ซั่งไห่ เสี่ยวหลงเปา</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>23:36.8</td>\n",
       "      <td>6bc75e0d-fc65-471e-9926-ff6be6708639</td>\n",
       "      <td>189564.0</td>\n",
       "      <td>1</td>\n",
       "      <td>9.333590e+14</td>\n",
       "      <td>True</td>\n",
       "      <td>Yoshinoya</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  click_timestamp                             search_id  business_id  \\\n",
       "0         11:38.9  5c004472-5ecc-4159-9698-9f329b8e26e1     298839.0   \n",
       "1         30:51.3  5f34eca0-c04e-4c10-b600-54aa447dbd8e     171372.0   \n",
       "2         32:09.6  8dc6d1f0-11f1-4239-9dd2-5c1674c81224      16157.0   \n",
       "3         00:59.8  1a5810e9-3d1f-44b9-9a2e-26ede21c524b      89741.0   \n",
       "4         23:36.8  6bc75e0d-fc65-471e-9926-ff6be6708639     189564.0   \n",
       "\n",
       "   display_position  time_diff_nanos   ads            Business_name  \n",
       "0                 0     2.222400e+15  True        ปูดองอันยอง Eight  \n",
       "1                 0     4.608380e+14  True                Yoshinoya  \n",
       "2                 0     2.581490e+15  True  The Hub Cafe and Eatery  \n",
       "3                 1     1.404580e+15  True     ซั่งไห่ เสี่ยวหลงเปา  \n",
       "4                 1     9.333590e+14  True                Yoshinoya  "
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s_click_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### use .merge()\n",
    "##### combine two table then cut uselesscolumn "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "merget = s_click_df.merge(smt,how = 'left',left_on = 'business_id',right_on = 'id')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "how = 'left' ตารางซ้ายเป็นตารางหลัก left_on = 'business_id',right_on = 'id' ใช้เป็นคีย์"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "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>click_timestamp</th>\n",
       "      <th>search_id</th>\n",
       "      <th>business_id</th>\n",
       "      <th>display_position</th>\n",
       "      <th>time_diff_nanos</th>\n",
       "      <th>ads</th>\n",
       "      <th>Business_name</th>\n",
       "      <th>name</th>\n",
       "      <th>city_name</th>\n",
       "      <th>zipcode</th>\n",
       "      <th>weighted_average_rating</th>\n",
       "      <th>id</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>11:38.9</td>\n",
       "      <td>5c004472-5ecc-4159-9698-9f329b8e26e1</td>\n",
       "      <td>298839.0</td>\n",
       "      <td>0</td>\n",
       "      <td>2.222400e+15</td>\n",
       "      <td>True</td>\n",
       "      <td>ปูดองอันยอง Eight</td>\n",
       "      <td>ปูดองอันยอง Eight</td>\n",
       "      <td>Bangkok Metropolitan Region</td>\n",
       "      <td>10110</td>\n",
       "      <td>3.836735</td>\n",
       "      <td>298839.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>30:51.3</td>\n",
       "      <td>5f34eca0-c04e-4c10-b600-54aa447dbd8e</td>\n",
       "      <td>171372.0</td>\n",
       "      <td>0</td>\n",
       "      <td>4.608380e+14</td>\n",
       "      <td>True</td>\n",
       "      <td>Yoshinoya</td>\n",
       "      <td>Yoshinoya</td>\n",
       "      <td>Bangkok Metropolitan Region</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.118367</td>\n",
       "      <td>171372.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>32:09.6</td>\n",
       "      <td>8dc6d1f0-11f1-4239-9dd2-5c1674c81224</td>\n",
       "      <td>16157.0</td>\n",
       "      <td>0</td>\n",
       "      <td>2.581490e+15</td>\n",
       "      <td>True</td>\n",
       "      <td>The Hub Cafe and Eatery</td>\n",
       "      <td>The Hub Cafe and Eatery</td>\n",
       "      <td>Bangkok Metropolitan Region</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4.015184</td>\n",
       "      <td>16157.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>00:59.8</td>\n",
       "      <td>1a5810e9-3d1f-44b9-9a2e-26ede21c524b</td>\n",
       "      <td>89741.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1.404580e+15</td>\n",
       "      <td>True</td>\n",
       "      <td>ซั่งไห่ เสี่ยวหลงเปา</td>\n",
       "      <td>ซั่งไห่ เสี่ยวหลงเปา</td>\n",
       "      <td>Bangkok Metropolitan Region</td>\n",
       "      <td>10900</td>\n",
       "      <td>3.444134</td>\n",
       "      <td>89741.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>23:36.8</td>\n",
       "      <td>6bc75e0d-fc65-471e-9926-ff6be6708639</td>\n",
       "      <td>189564.0</td>\n",
       "      <td>1</td>\n",
       "      <td>9.333590e+14</td>\n",
       "      <td>True</td>\n",
       "      <td>Yoshinoya</td>\n",
       "      <td>Yoshinoya</td>\n",
       "      <td>Bangkok Metropolitan Region</td>\n",
       "      <td>10110</td>\n",
       "      <td>2.972672</td>\n",
       "      <td>189564.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  click_timestamp                             search_id  business_id  \\\n",
       "0         11:38.9  5c004472-5ecc-4159-9698-9f329b8e26e1     298839.0   \n",
       "1         30:51.3  5f34eca0-c04e-4c10-b600-54aa447dbd8e     171372.0   \n",
       "2         32:09.6  8dc6d1f0-11f1-4239-9dd2-5c1674c81224      16157.0   \n",
       "3         00:59.8  1a5810e9-3d1f-44b9-9a2e-26ede21c524b      89741.0   \n",
       "4         23:36.8  6bc75e0d-fc65-471e-9926-ff6be6708639     189564.0   \n",
       "\n",
       "   display_position  time_diff_nanos   ads            Business_name  \\\n",
       "0                 0     2.222400e+15  True        ปูดองอันยอง Eight   \n",
       "1                 0     4.608380e+14  True                Yoshinoya   \n",
       "2                 0     2.581490e+15  True  The Hub Cafe and Eatery   \n",
       "3                 1     1.404580e+15  True     ซั่งไห่ เสี่ยวหลงเปา   \n",
       "4                 1     9.333590e+14  True                Yoshinoya   \n",
       "\n",
       "                      name                    city_name zipcode  \\\n",
       "0        ปูดองอันยอง Eight  Bangkok Metropolitan Region   10110   \n",
       "1                Yoshinoya  Bangkok Metropolitan Region     NaN   \n",
       "2  The Hub Cafe and Eatery  Bangkok Metropolitan Region     NaN   \n",
       "3     ซั่งไห่ เสี่ยวหลงเปา  Bangkok Metropolitan Region   10900   \n",
       "4                Yoshinoya  Bangkok Metropolitan Region   10110   \n",
       "\n",
       "   weighted_average_rating        id  \n",
       "0                 3.836735  298839.0  \n",
       "1                 3.118367  171372.0  \n",
       "2                 4.015184   16157.0  \n",
       "3                 3.444134   89741.0  \n",
       "4                 2.972672  189564.0  "
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "merget.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## การใช้ how = ในการ join ตาราง"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![join table](merge.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## วิธีสร้างตาราง โดยใช้ PANDAS"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![create dataframe](pandas-dataframe-shadow.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# H.W. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "สร้างตารางทั้ง 4 แบบ"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "sales1 = [{'account':'Jones LLC','Jan':'150','Feb':'200','Mar':'140'},\n",
    "         {'account':'Alpha Co','Jan':'200','Feb':'210','Mar':'215'},\n",
    "         {'account':'Blue Inc','Jan':'50','Feb':'90','Mar':'95'}]\n",
    "df1 = pd.DataFrame(sales1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "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>account</th>\n",
       "      <th>Jan</th>\n",
       "      <th>Feb</th>\n",
       "      <th>Mar</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Jones LLC</td>\n",
       "      <td>150</td>\n",
       "      <td>200</td>\n",
       "      <td>140</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Alpha Co</td>\n",
       "      <td>200</td>\n",
       "      <td>210</td>\n",
       "      <td>215</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Blue Inc</td>\n",
       "      <td>50</td>\n",
       "      <td>90</td>\n",
       "      <td>95</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     account  Jan  Feb  Mar\n",
       "0  Jones LLC  150  200  140\n",
       "1   Alpha Co  200  210  215\n",
       "2   Blue Inc   50   90   95"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "sales2 = [('Jones LLC','150','200','140'),\n",
    "         ('Alpha Co','200','210','215'),\n",
    "         ('Blue Inc','50','90','95')]\n",
    "labels = ['account','Jan','Feb','Mar']\n",
    "df2 = pd.DataFrame.from_records(sales2,columns = labels)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "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>account</th>\n",
       "      <th>Jan</th>\n",
       "      <th>Feb</th>\n",
       "      <th>Mar</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Jones LLC</td>\n",
       "      <td>150</td>\n",
       "      <td>200</td>\n",
       "      <td>140</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Alpha Co</td>\n",
       "      <td>200</td>\n",
       "      <td>210</td>\n",
       "      <td>215</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Blue Inc</td>\n",
       "      <td>50</td>\n",
       "      <td>90</td>\n",
       "      <td>95</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     account  Jan  Feb  Mar\n",
       "0  Jones LLC  150  200  140\n",
       "1   Alpha Co  200  210  215\n",
       "2   Blue Inc   50   90   95"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "sales3 = {'account':['Jones LLC','Alpha Co','Blue Inc'],\n",
    "         'Jan':['150','200','140'],\n",
    "         'Feb':['200','210','215'],\n",
    "         'Mar':['50','90','95']}\n",
    "df3 = pd.DataFrame.from_dict(sales3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "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>account</th>\n",
       "      <th>Jan</th>\n",
       "      <th>Feb</th>\n",
       "      <th>Mar</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Jones LLC</td>\n",
       "      <td>150</td>\n",
       "      <td>200</td>\n",
       "      <td>50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Alpha Co</td>\n",
       "      <td>200</td>\n",
       "      <td>210</td>\n",
       "      <td>90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Blue Inc</td>\n",
       "      <td>140</td>\n",
       "      <td>215</td>\n",
       "      <td>95</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     account  Jan  Feb Mar\n",
       "0  Jones LLC  150  200  50\n",
       "1   Alpha Co  200  210  90\n",
       "2   Blue Inc  140  215  95"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\User\\Miniconda3\\lib\\site-packages\\ipykernel_launcher.py:5: FutureWarning: from_items is deprecated. Please use DataFrame.from_dict(dict(items), ...) instead. DataFrame.from_dict(OrderedDict(items)) may be used to preserve the key order.\n",
      "  \"\"\"\n"
     ]
    }
   ],
   "source": [
    "sales4 = [('account',['Jones LLC','Alpha Co','Blue Inc']),\n",
    "         ('Jan',['150','200','140']),\n",
    "         ('Feb',['200','210','215']),\n",
    "         ('Mar',['50','90','95'])]\n",
    "df4 = pd.DataFrame.from_items(sales4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "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>account</th>\n",
       "      <th>Jan</th>\n",
       "      <th>Feb</th>\n",
       "      <th>Mar</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Jones LLC</td>\n",
       "      <td>150</td>\n",
       "      <td>200</td>\n",
       "      <td>50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Alpha Co</td>\n",
       "      <td>200</td>\n",
       "      <td>210</td>\n",
       "      <td>90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Blue Inc</td>\n",
       "      <td>140</td>\n",
       "      <td>215</td>\n",
       "      <td>95</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     account  Jan  Feb Mar\n",
       "0  Jones LLC  150  200  50\n",
       "1   Alpha Co  200  210  90\n",
       "2   Blue Inc  140  215  95"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df4"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "                                                                END"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## .iloc access data in the table as array เข้าถึงข้อมูลแบบ อาร์เรย์(เพื่อใช้วนลูปในตาราง)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "wrest_PATH = 'C:\\\\Users\\\\User\\\\Downloads\\\\w_restaurant.csv'\n",
    "sclick_PATH = 'C:\\\\Users\\\\User\\\\Downloads\\\\search_click.csv'\n",
    "w_rest_df = pd.read_csv(wrest_PATH)\n",
    "s_click_df = pd.read_csv(sclick_PATH)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>name</th>\n",
       "      <th>domain_id</th>\n",
       "      <th>closed</th>\n",
       "      <th>city_name</th>\n",
       "      <th>zipcode</th>\n",
       "      <th>geohash</th>\n",
       "      <th>new_open</th>\n",
       "      <th>weighted_average_rating</th>\n",
       "      <th>number_of_chains</th>\n",
       "      <th>...</th>\n",
       "      <th>good_for_groups</th>\n",
       "      <th>good_for_kids</th>\n",
       "      <th>bookable</th>\n",
       "      <th>best_of_wongnai</th>\n",
       "      <th>wifi</th>\n",
       "      <th>number_of_seats</th>\n",
       "      <th>music_venue</th>\n",
       "      <th>hotel_restaurant</th>\n",
       "      <th>pet_friendly</th>\n",
       "      <th>delivery</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2</td>\n",
       "      <td>นครินทร์ทันตกรรม</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>Samut Songkhram</td>\n",
       "      <td>75000</td>\n",
       "      <td>w4rh7g3</td>\n",
       "      <td>0</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>13.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4</td>\n",
       "      <td>Corner House</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>Bangkok Metropolitan Region</td>\n",
       "      <td>12150</td>\n",
       "      <td>w4rx73h</td>\n",
       "      <td>0</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>5</td>\n",
       "      <td>วัดโลกยสุธาราม</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>Phra Nakhon Si Ayutthaya</td>\n",
       "      <td>13000</td>\n",
       "      <td>w4x98jk</td>\n",
       "      <td>0</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>6</td>\n",
       "      <td>นันท์คาราโอเกะ</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>Bangkok Metropolitan Region</td>\n",
       "      <td>10700</td>\n",
       "      <td>w4rqw9q</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>7</td>\n",
       "      <td>Buono Caffe</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>Bangkok Metropolitan Region</td>\n",
       "      <td>10220</td>\n",
       "      <td>w4rx4gd</td>\n",
       "      <td>0</td>\n",
       "      <td>3.738462</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 32 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   id              name  domain_id  closed                    city_name  \\\n",
       "0   2  นครินทร์ทันตกรรม          2       0              Samut Songkhram   \n",
       "1   4      Corner House          1       0  Bangkok Metropolitan Region   \n",
       "2   5    วัดโลกยสุธาราม          4       0     Phra Nakhon Si Ayutthaya   \n",
       "3   6    นันท์คาราโอเกะ          1       0  Bangkok Metropolitan Region   \n",
       "4   7       Buono Caffe          1       0  Bangkok Metropolitan Region   \n",
       "\n",
       "  zipcode  geohash  new_open  weighted_average_rating  number_of_chains  ...  \\\n",
       "0   75000  w4rh7g3         0                 5.000000               NaN  ...   \n",
       "1   12150  w4rx73h         0                 2.000000               NaN  ...   \n",
       "2   13000  w4x98jk         0                 4.000000               NaN  ...   \n",
       "3   10700  w4rqw9q         0                 0.000000               NaN  ...   \n",
       "4   10220  w4rx4gd         0                 3.738462               NaN  ...   \n",
       "\n",
       "   good_for_groups  good_for_kids  bookable  best_of_wongnai  wifi  \\\n",
       "0              NaN            NaN       NaN              NaN   NaN   \n",
       "1              NaN            NaN       NaN              NaN   NaN   \n",
       "2              NaN            NaN       NaN              NaN   NaN   \n",
       "3              NaN            NaN       NaN              NaN   NaN   \n",
       "4              NaN            NaN       NaN              NaN   1.0   \n",
       "\n",
       "   number_of_seats  music_venue hotel_restaurant  pet_friendly  delivery  \n",
       "0             13.0          NaN              NaN           NaN       NaN  \n",
       "1              NaN          NaN              NaN           NaN       NaN  \n",
       "2              NaN          NaN              NaN           NaN       NaN  \n",
       "3              NaN          NaN              NaN           NaN       NaN  \n",
       "4              2.0          NaN              NaN           NaN       NaN  \n",
       "\n",
       "[5 rows x 32 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "w_rest_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Phra Nakhon Si Ayutthaya'"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "w_rest_df.iloc[2,4]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "โดยการสร้างข้อมูลหลักใหม่ ประกอบด้วย ชื่อ ชื่อเมือง รหัสไปรษณีย์ ชื่อร้าน(ชื่อจังหวัด(รหัสไปรฯ)) ---> name(city_name(zipcode))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "Lwrest = []"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'วัดโลกยสุธาราม (Phra Nakhon Si Ayutthaya(13000))'"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_st = f'{w_rest_df.iloc[2,1]} ({w_rest_df.iloc[2,4]}({w_rest_df.iloc[2,5]}))'\n",
    "new_st"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "f'ปป {  }'คือการเอาตัวแปรที่มีในตารางมา(ดึงมา)\n",
    "\n",
    "range() คือการบอกจำนวนแถวในตาราง(list of number)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "9\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "range(0, 10)"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a = range(10)\n",
    "for x in a:\n",
    "    print (x)\n",
    "a"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(246847, 32)"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "w_rest_df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "new_list = list()\n",
    "for l in range(w_rest_df.shape[0]):\n",
    "    new_st = f'{w_rest_df.iloc[l,1]} ({w_rest_df.iloc[l,4]}({w_rest_df.iloc[l,5]}))'\n",
    "    new_list.append(new_st)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "ลิสต์ใหม่.append(ชื่อลิสต์ที่จะใส่) = เพิ่มrecord in list เรื่อย"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['นครินทร์ทันตกรรม (Samut Songkhram(75000))',\n",
       " 'Corner House (Bangkok Metropolitan Region(12150))',\n",
       " 'วัดโลกยสุธาราม (Phra Nakhon Si Ayutthaya(13000))',\n",
       " 'นันท์คาราโอเกะ (Bangkok Metropolitan Region(10700))',\n",
       " 'Buono Caffe (Bangkok Metropolitan Region(10220))']"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_list[:5]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "ต่อตาราง"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\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>name</th>\n",
       "      <th>domain_id</th>\n",
       "      <th>closed</th>\n",
       "      <th>city_name</th>\n",
       "      <th>zipcode</th>\n",
       "      <th>geohash</th>\n",
       "      <th>new_open</th>\n",
       "      <th>weighted_average_rating</th>\n",
       "      <th>number_of_chains</th>\n",
       "      <th>...</th>\n",
       "      <th>bookable</th>\n",
       "      <th>best_of_wongnai</th>\n",
       "      <th>wifi</th>\n",
       "      <th>number_of_seats</th>\n",
       "      <th>music_venue</th>\n",
       "      <th>hotel_restaurant</th>\n",
       "      <th>pet_friendly</th>\n",
       "      <th>delivery</th>\n",
       "      <th>sumname and locate</th>\n",
       "      <th>name and locate</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2</td>\n",
       "      <td>นครินทร์ทันตกรรม</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>Samut Songkhram</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>13.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>นครินทร์ทันตกรรม (Samut Songkhram(75000))</td>\n",
       "      <td>นครินทร์ทันตกรรม (Samut Songkhram(75000))</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4</td>\n",
       "      <td>Corner House</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>Bangkok Metropolitan Region</td>\n",
       "      <td>12150</td>\n",
       "      <td>w4rx73h</td>\n",
       "      <td>0</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Corner House (Bangkok Metropolitan Region(12150))</td>\n",
       "      <td>Corner House (Bangkok Metropolitan Region(12150))</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>5</td>\n",
       "      <td>วัดโลกยสุธาราม</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>Phra Nakhon Si Ayutthaya</td>\n",
       "      <td>13000</td>\n",
       "      <td>w4x98jk</td>\n",
       "      <td>0</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>วัดโลกยสุธาราม (Phra Nakhon Si Ayutthaya(13000))</td>\n",
       "      <td>วัดโลกยสุธาราม (Phra Nakhon Si Ayutthaya(13000))</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>6</td>\n",
       "      <td>นันท์คาราโอเกะ</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>Bangkok Metropolitan Region</td>\n",
       "      <td>10700</td>\n",
       "      <td>w4rqw9q</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
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       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>นันท์คาราโอเกะ (Bangkok Metropolitan Region(10...</td>\n",
       "      <td>นันท์คาราโอเกะ (Bangkok Metropolitan Region(10...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>7</td>\n",
       "      <td>Buono Caffe</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>Bangkok Metropolitan Region</td>\n",
       "      <td>10220</td>\n",
       "      <td>w4rx4gd</td>\n",
       "      <td>0</td>\n",
       "      <td>3.738462</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Buono Caffe (Bangkok Metropolitan Region(10220))</td>\n",
       "      <td>Buono Caffe (Bangkok Metropolitan Region(10220))</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 34 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   id              name  domain_id  closed                    city_name  \\\n",
       "0   2  นครินทร์ทันตกรรม          2       0              Samut Songkhram   \n",
       "1   4      Corner House          1       0  Bangkok Metropolitan Region   \n",
       "2   5    วัดโลกยสุธาราม          4       0     Phra Nakhon Si Ayutthaya   \n",
       "3   6    นันท์คาราโอเกะ          1       0  Bangkok Metropolitan Region   \n",
       "4   7       Buono Caffe          1       0  Bangkok Metropolitan Region   \n",
       "\n",
       "  zipcode  geohash  new_open  weighted_average_rating  number_of_chains  ...  \\\n",
       "0   75000  w4rh7g3         0                 5.000000               NaN  ...   \n",
       "1   12150  w4rx73h         0                 2.000000               NaN  ...   \n",
       "2   13000  w4x98jk         0                 4.000000               NaN  ...   \n",
       "3   10700  w4rqw9q         0                 0.000000               NaN  ...   \n",
       "4   10220  w4rx4gd         0                 3.738462               NaN  ...   \n",
       "\n",
       "   bookable  best_of_wongnai  wifi  number_of_seats  music_venue  \\\n",
       "0       NaN              NaN   NaN             13.0          NaN   \n",
       "1       NaN              NaN   NaN              NaN          NaN   \n",
       "2       NaN              NaN   NaN              NaN          NaN   \n",
       "3       NaN              NaN   NaN              NaN          NaN   \n",
       "4       NaN              NaN   1.0              2.0          NaN   \n",
       "\n",
       "   hotel_restaurant  pet_friendly delivery  \\\n",
       "0               NaN           NaN      NaN   \n",
       "1               NaN           NaN      NaN   \n",
       "2               NaN           NaN      NaN   \n",
       "3               NaN           NaN      NaN   \n",
       "4               NaN           NaN      NaN   \n",
       "\n",
       "                                  sumname and locate  \\\n",
       "0          นครินทร์ทันตกรรม (Samut Songkhram(75000))   \n",
       "1  Corner House (Bangkok Metropolitan Region(12150))   \n",
       "2   วัดโลกยสุธาราม (Phra Nakhon Si Ayutthaya(13000))   \n",
       "3  นันท์คาราโอเกะ (Bangkok Metropolitan Region(10...   \n",
       "4   Buono Caffe (Bangkok Metropolitan Region(10220))   \n",
       "\n",
       "                                     name and locate  \n",
       "0          นครินทร์ทันตกรรม (Samut Songkhram(75000))  \n",
       "1  Corner House (Bangkok Metropolitan Region(12150))  \n",
       "2   วัดโลกยสุธาราม (Phra Nakhon Si Ayutthaya(13000))  \n",
       "3  นันท์คาราโอเกะ (Bangkok Metropolitan Region(10...  \n",
       "4   Buono Caffe (Bangkok Metropolitan Region(10220))  \n",
       "\n",
       "[5 rows x 34 columns]"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "w_rest_df['name and locate'] = new_list\n",
    "w_rest_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "                                              ~ END  ~"
   ]
  }
 ],
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