Simple 1st Page Data Web Scraping .ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Web Scraping"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "import re\n",
    "import requests\n",
    "from bs4 import BeautifulSoup"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "Name=[]\n",
    "Final_price=[]\n",
    "Original_price=[]\n",
    "Offer=[]\n",
    "Total_rating=[]\n",
    "Rating=[]\n",
    "Dist=[]\n",
    "a=[]\n",
    "Summary=[]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Data Extraction"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "sending request to Web Page by using \n",
    "`requests.get()`\n",
    "\n",
    "`Beautiful Soup` is a Python library for pulling data out of `HTML and XML files`. It works with your favorite `parser` to provide idiomatic ways of navigating, searching, and modifying the `parse tree`. \n",
    "\n",
    "`Beautiful Soup supports the HTML parser`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "     \n",
    "for x in range(10):\n",
    "    \n",
    "    url='https://www.oyorooms.com/hotels-in-hyderabad/?page={}'\n",
    "    data= requests.get(url.format(x))\n",
    "    soup=BeautifulSoup(data.text,\"html.parser\")\n",
    "#data.text\n",
    "#data\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "  ##################### HTML class for different attributes ###################\n",
    "    Hotel_name=data_soup.find_all(class_='listingHotelDescription__hotelName d-textEllipsis')\n",
    "    \n",
    "    Hotel_finalprice=data_soup.find_all(class_='listingPrice__finalPrice')\n",
    "    \n",
    "    Hotel_originalprice=data_soup.find_all(class_='listingPrice__slashedPrice d-body-lg')\n",
    "    \n",
    "    Hotel_offer=data_soup.find_all(class_='listingPrice__percentage')    \n",
    "    \n",
    "    Hotel_dist=data_soup.find_all(class_='listingHotelDescription__distanceText')    \n",
    "    \n",
    "    Hotel_Total_rating=data_soup.find_all('div',class_='hotelRating')\n",
    "    \n",
    "    Hotel_rating=data_soup.find_all('span',class_='hotelRating__rating')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'for j in range(len(Hotel_name)): \\n\\n            if Hotel_name[j]:\\n                n = Hotel_name[j].text\\n            else:\\n                n=np.nan  \\n            Name.append(n)'"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#read Hotel Names \n",
    "\n",
    "Name=[]\n",
    "\n",
    "Hotel_name= soup.find_all(class_='listingHotelDescription__hotelName d-textEllipsis')\n",
    "\n",
    "for item in Hotel_name:\n",
    "    Name.append(item.text)\n",
    "\n",
    "#Method 2\n",
    "\"\"\"for j in range(len(Hotel_name)): \n",
    "\n",
    "            if Hotel_name[j]:\n",
    "                n = Hotel_name[j].text\n",
    "            else:\n",
    "                n=np.nan  \n",
    "            Name.append(n)\"\"\"\n",
    "#Name"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "Final_price= []\n",
    "\n",
    "Hotel_finalprice= soup.find_all(class_='listingPrice__finalPrice')\n",
    "\n",
    "for item in Hotel_finalprice:\n",
    "    Final_price.append(item.text)\n",
    "    \n",
    "#Final_price"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "Original_price=[]\n",
    "\n",
    "Hotel_originalprice= soup.find_all(class_='listingPrice__slashedPrice d-body-lg')\n",
    "\n",
    "for item in Hotel_originalprice:\n",
    "    Original_price.append(item.text)\n",
    "    \n",
    "#Original_price    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "Offer=[]\n",
    "\n",
    "Hotel_offer= soup.find_all(class_='listingPrice__percentage')\n",
    "\n",
    "for item in Hotel_offer:\n",
    "    Offer.append(item.text)\n",
    "    \n",
    "#Offer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "Total_rating=[]\n",
    "\n",
    "Hotel_Total_rating= soup.find_all('div',class_='hotelRating')\n",
    "\n",
    "for item in Hotel_Total_rating:\n",
    "    Total_rating.append(item.text)\n",
    "\n",
    "#Total_rating    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "Rating=[]\n",
    "\n",
    "Hotel_rating= soup.find_all('span',class_='hotelRating__rating')\n",
    "\n",
    "for item in Hotel_rating:\n",
    "    Rating.append(item.text)\n",
    "\n",
    "#Rating"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "Dist= []\n",
    "\n",
    "Hotel_dist= soup.find_all(class_='listingHotelDescription__distanceText') \n",
    "\n",
    "for item in Hotel_dist:\n",
    "    Dist.append(item.text)\n",
    "\n",
    "#Dist"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "#read Hotel Names \n",
    "\n",
    "Name=[]\n",
    "\n",
    "Hotel_name= soup.find_all(class_='listingHotelDescription__hotelName d-textEllipsis')\n",
    "\n",
    "for j in range(len(Hotel_name)):\n",
    "\n",
    "    if Hotel_name[j]:\n",
    "        n = Hotel_name[j].text\n",
    "    else:\n",
    "        n=np.nan\n",
    "    Name.append(n)\n",
    "#Name"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "Final_price= []\n",
    "\n",
    "Hotel_finalprice= soup.find_all(class_='listingPrice__finalPrice')\n",
    "\n",
    "for j in range(len(Hotel_name)):\n",
    "\n",
    "    if Hotel_finalprice[j]:\n",
    "        n = Hotel_finalprice[j].text\n",
    "        n.replace('₹','')\n",
    "    else:\n",
    "        n=np.nan\n",
    "    Final_price.append(n)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "Original_price=[]\n",
    "\n",
    "Hotel_originalprice= soup.find_all(class_='listingPrice__slashedPrice d-body-lg')\n",
    "\n",
    "for j in range(len(Hotel_name)):\n",
    "    if Hotel_originalprice[j]:\n",
    "        n = Hotel_originalprice[j].text\n",
    "        n.replace('₹','')\n",
    "    else:\n",
    "        n=np.nan\n",
    "    Original_price.append(n)\n",
    "    \n",
    "#Original_price    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "Offer=[]\n",
    "\n",
    "Hotel_offer= soup.find_all(class_='listingPrice__percentage')\n",
    "\n",
    "for j in range(len(Hotel_name)):\n",
    "    if Hotel_offer[j]:\n",
    "        n = Hotel_offer[j].text\n",
    "    else:\n",
    "        n=np.nan   \n",
    "    Offer.append(n)\n",
    "    \n",
    "#Offer"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Dist= []\n",
    "\n",
    "Hotel_dist= soup.find_all(class_= 'listingHotelDescription__distanceText')\n",
    "\n",
    "for j in range(len(Hotel_name)):\n",
    "    if Hotel_dist[j]:\n",
    "        n = Hotel_dist[j].text\n",
    "        n.replace('km','')\n",
    "    else:\n",
    "        n=np.nan\n",
    "    Dist.append(n)\n",
    "#Dist"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "Rating=[]\n",
    "\n",
    "Hotel_rating=soup.find_all('span',class_='hotelRating__rating')\n",
    "\n",
    "for j in range(len(Hotel_name)):\n",
    "    if Hotel_rating[j]:\n",
    "        n = Hotel_rating[j].text\n",
    "    else:\n",
    "        n=np.nan\n",
    "    Rating.append(n)\n",
    "#Rating    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "Total_rating=[]\n",
    "\n",
    "Hotel_Total_rating= soup.find_all('div',class_='hotelRating')\n",
    "\n",
    "for k in Hotel_Total_rating:\n",
    "        x = k.text\n",
    "        if len(re.findall(r'[(]\\d+\\s.*[)]',x)) == 1:\n",
    "            Total_rating.append(re.findall(r'[(]\\d+\\s.*[)]',x)[0])\n",
    "        else:\n",
    "            Total_rating.append(np.nan) \n",
    "\n",
    "#Total_rating   "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "a=[]\n",
    "Summary=[]\n",
    "\n",
    "for i in Hotel_Total_rating:\n",
    "        a.append(i.text)\n",
    "\n",
    "for i in a:\n",
    "        #print(i)\n",
    "        if i != 'NEW':\n",
    "            Summary.append(i.split('·')[1])\n",
    "        else:\n",
    "            #Nur.append(np.nan) \n",
    "            Summary.append(np.nan)\n",
    "#a\n",
    "#Summary"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "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>Name</th>\n",
       "      <th>Original_price</th>\n",
       "      <th>Offer</th>\n",
       "      <th>Final_price</th>\n",
       "      <th>Rating</th>\n",
       "      <th>Number of Rating</th>\n",
       "      <th>Summary</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>OYO 70927 Hotel Hyderabad Comfort  Saver</td>\n",
       "      <td>₹2160</td>\n",
       "      <td>40% off</td>\n",
       "      <td>₹1296</td>\n",
       "      <td>NEW</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>OYO 71234 Hotel Shyam  Deluxe</td>\n",
       "      <td>₹3890</td>\n",
       "      <td>40% off</td>\n",
       "      <td>₹2334</td>\n",
       "      <td>NEW</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>OYO 71431 Hotel Polo</td>\n",
       "      <td>₹2267</td>\n",
       "      <td>40% off</td>\n",
       "      <td>₹1360</td>\n",
       "      <td>3.0</td>\n",
       "      <td>(16 Ratings)</td>\n",
       "      <td>Fair</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>OYO 71611 Krupa Service Apartments</td>\n",
       "      <td>₹1690</td>\n",
       "      <td>40% off</td>\n",
       "      <td>₹1014</td>\n",
       "      <td>NEW</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>OYO 71244 Hotel Saba Service Apartments</td>\n",
       "      <td>₹1533</td>\n",
       "      <td>40% off</td>\n",
       "      <td>₹920</td>\n",
       "      <td>NEW</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>SilverKey Executive Stays 24138 Nallagandla</td>\n",
       "      <td>₹2178</td>\n",
       "      <td>40% off</td>\n",
       "      <td>₹1307</td>\n",
       "      <td>4.3</td>\n",
       "      <td>(1846 Ratings)</td>\n",
       "      <td>Very Good</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>OYO 40807 Hotel Meena International</td>\n",
       "      <td>₹2147</td>\n",
       "      <td>40% off</td>\n",
       "      <td>₹1288</td>\n",
       "      <td>4.0</td>\n",
       "      <td>(455 Ratings)</td>\n",
       "      <td>Very Good</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>OYO 60375 Peaceful Stay Villa</td>\n",
       "      <td>₹4105</td>\n",
       "      <td>40% off</td>\n",
       "      <td>₹2463</td>\n",
       "      <td>4.2</td>\n",
       "      <td>(513 Ratings)</td>\n",
       "      <td>Very Good</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>OYO Flagship 18335 Eaglewood Hotels</td>\n",
       "      <td>₹2472</td>\n",
       "      <td>40% off</td>\n",
       "      <td>₹1483</td>\n",
       "      <td>3.4</td>\n",
       "      <td>(2102 Ratings)</td>\n",
       "      <td>Fair</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>OYO Townhouse 113 Green Leaves LB Nagar</td>\n",
       "      <td>₹3817</td>\n",
       "      <td>40% off</td>\n",
       "      <td>₹2290</td>\n",
       "      <td>4.5</td>\n",
       "      <td>(1896 Ratings)</td>\n",
       "      <td>Excellent</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>OYO 16428 Hotel Comfort Inn</td>\n",
       "      <td>₹2890</td>\n",
       "      <td>40% off</td>\n",
       "      <td>₹1734</td>\n",
       "      <td>4.0</td>\n",
       "      <td>(575 Ratings)</td>\n",
       "      <td>Very Good</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>OYO Home 47339 Comfortable Stays</td>\n",
       "      <td>₹2367</td>\n",
       "      <td>40% off</td>\n",
       "      <td>₹1420</td>\n",
       "      <td>4.4</td>\n",
       "      <td>(743 Ratings)</td>\n",
       "      <td>Very Good</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>OYO 30151 Golden Hive</td>\n",
       "      <td>₹2750</td>\n",
       "      <td>40% off</td>\n",
       "      <td>₹1650</td>\n",
       "      <td>4.3</td>\n",
       "      <td>(668 Ratings)</td>\n",
       "      <td>Very Good</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>OYO 16606 Iconic Living</td>\n",
       "      <td>₹2695</td>\n",
       "      <td>40% off</td>\n",
       "      <td>₹1617</td>\n",
       "      <td>4.3</td>\n",
       "      <td>(277 Ratings)</td>\n",
       "      <td>Very Good</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>SPOT ON 68046 Rainbow Hotel SPOT</td>\n",
       "      <td>₹2026</td>\n",
       "      <td>40% off</td>\n",
       "      <td>₹1216</td>\n",
       "      <td>4.1</td>\n",
       "      <td>(423 Ratings)</td>\n",
       "      <td>Very Good</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>OYO 795 Samudra Inn Hotel</td>\n",
       "      <td>₹2456</td>\n",
       "      <td>40% off</td>\n",
       "      <td>₹1474</td>\n",
       "      <td>3.7</td>\n",
       "      <td>(267 Ratings)</td>\n",
       "      <td>Good</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>OYO 45138 Hady Inn</td>\n",
       "      <td>₹3070</td>\n",
       "      <td>40% off</td>\n",
       "      <td>₹1842</td>\n",
       "      <td>3.8</td>\n",
       "      <td>(265 Ratings)</td>\n",
       "      <td>Good</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>OYO 68235 R-residency</td>\n",
       "      <td>₹2382</td>\n",
       "      <td>40% off</td>\n",
       "      <td>₹1429</td>\n",
       "      <td>3.3</td>\n",
       "      <td>(111 Ratings)</td>\n",
       "      <td>Fair</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>Capital O 69508 Halcyon Nest</td>\n",
       "      <td>₹2477</td>\n",
       "      <td>40% off</td>\n",
       "      <td>₹1486</td>\n",
       "      <td>4.0</td>\n",
       "      <td>(154 Ratings)</td>\n",
       "      <td>Very Good</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>OYO 23589 Crown Palace</td>\n",
       "      <td>₹2422</td>\n",
       "      <td>40% off</td>\n",
       "      <td>₹1453</td>\n",
       "      <td>4.1</td>\n",
       "      <td>(495 Ratings)</td>\n",
       "      <td>Very Good</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                           Name Original_price    Offer  \\\n",
       "0      OYO 70927 Hotel Hyderabad Comfort  Saver          ₹2160  40% off   \n",
       "1                 OYO 71234 Hotel Shyam  Deluxe          ₹3890  40% off   \n",
       "2                         OYO 71431 Hotel Polo           ₹2267  40% off   \n",
       "3           OYO 71611 Krupa Service Apartments           ₹1690  40% off   \n",
       "4       OYO 71244 Hotel Saba Service Apartments          ₹1533  40% off   \n",
       "5   SilverKey Executive Stays 24138 Nallagandla          ₹2178  40% off   \n",
       "6           OYO 40807 Hotel Meena International          ₹2147  40% off   \n",
       "7                 OYO 60375 Peaceful Stay Villa          ₹4105  40% off   \n",
       "8           OYO Flagship 18335 Eaglewood Hotels          ₹2472  40% off   \n",
       "9       OYO Townhouse 113 Green Leaves LB Nagar          ₹3817  40% off   \n",
       "10                  OYO 16428 Hotel Comfort Inn          ₹2890  40% off   \n",
       "11             OYO Home 47339 Comfortable Stays          ₹2367  40% off   \n",
       "12                        OYO 30151 Golden Hive          ₹2750  40% off   \n",
       "13                      OYO 16606 Iconic Living          ₹2695  40% off   \n",
       "14             SPOT ON 68046 Rainbow Hotel SPOT          ₹2026  40% off   \n",
       "15                    OYO 795 Samudra Inn Hotel          ₹2456  40% off   \n",
       "16                           OYO 45138 Hady Inn          ₹3070  40% off   \n",
       "17                        OYO 68235 R-residency          ₹2382  40% off   \n",
       "18                 Capital O 69508 Halcyon Nest          ₹2477  40% off   \n",
       "19                       OYO 23589 Crown Palace          ₹2422  40% off   \n",
       "\n",
       "   Final_price Rating Number of Rating    Summary  \n",
       "0        ₹1296    NEW              NaN        NaN  \n",
       "1        ₹2334    NEW              NaN        NaN  \n",
       "2        ₹1360   3.0      (16 Ratings)       Fair  \n",
       "3        ₹1014    NEW              NaN        NaN  \n",
       "4         ₹920    NEW              NaN        NaN  \n",
       "5        ₹1307   4.3    (1846 Ratings)  Very Good  \n",
       "6        ₹1288   4.0     (455 Ratings)  Very Good  \n",
       "7        ₹2463   4.2     (513 Ratings)  Very Good  \n",
       "8        ₹1483   3.4    (2102 Ratings)       Fair  \n",
       "9        ₹2290   4.5    (1896 Ratings)  Excellent  \n",
       "10       ₹1734   4.0     (575 Ratings)  Very Good  \n",
       "11       ₹1420   4.4     (743 Ratings)  Very Good  \n",
       "12       ₹1650   4.3     (668 Ratings)  Very Good  \n",
       "13       ₹1617   4.3     (277 Ratings)  Very Good  \n",
       "14       ₹1216   4.1     (423 Ratings)  Very Good  \n",
       "15       ₹1474   3.7     (267 Ratings)       Good  \n",
       "16       ₹1842   3.8     (265 Ratings)       Good  \n",
       "17       ₹1429   3.3     (111 Ratings)       Fair  \n",
       "18       ₹1486   4.0     (154 Ratings)  Very Good  \n",
       "19       ₹1453   4.1     (495 Ratings)  Very Good  "
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df=pd.DataFrame()\n",
    "df['Name']=Name\n",
    "df['Original_price']=Original_price\n",
    "df['Offer']=Offer\n",
    "df['Final_price']=Final_price\n",
    "#df['Distance']=Dist\n",
    "df['Rating']=Rating\n",
    "df['Number of Rating']=Total_rating\n",
    "df['Summary']=Summary\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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