scraping-all-restaurant-listings-for-a-given-city-from-TripAdvisor-using-Python.ipynb

{
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    "<img style=\"padding-right: 10px;\" src='https://i.ibb.co/vmVvrPF/ta-logo1.png' width=\"45\" height=\"45\" align=\"left\"><BR CLEAR=ALL />\n",
    "<font size=7 align='center' face='fantasy'>Scraping all Restaurant Listings from <font color=#33E0A1>TripAdvisor</font>\n",
    "    for <font color=#728FCE> a given City</font> Using Python</font><BR CLEAR=ALL /><br>\n",
    "<font color=grey size=5 align='left' face='sans'>Using BeautifulSoup, Requests, Seaborn, and Pandas</font><br>\n",
    "<img src='https://i.ibb.co/vz53r88/restt.jpg' width=\"1000\" height=\"1000\" align=\"left\" style=\"border:10px grey;\">\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<font size=5 align='left'><img style=\"padding-right: 10px;padding-top: 0px;\" src='https://i.ibb.co/878wW18/resttt-fotor-bg-remover-20230315225922.png' width=\"40\" height=\"40\" align=\"left\">\n",
    "          <b>Web Scraping using Python</b>\n",
    "</font><br>\n",
    "<font size=3 align='left'>\n",
    "Using web scraping, we can extract data like product prices, ratings and other types of information from websites. We can then use this data for various purposes like data analysis, research, business intelligence, and data science. In Python, web scraping is often done using libraries like Beautiful Soup, Scrapy, and Requests, which make it easier to retrieve and parse data from web pages.\n",
    "</font>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<font size=5 align='left'>\n",
    "  <p>\n",
    "      <img style=\"padding-right: 10px;padding-top: 0px;\" src='https://i.ibb.co/878wW18/resttt-fotor-bg-remover-20230315225922.png' width=\"40\" height=\"40\" align=\"left\">\n",
    "          <b>But wait, this is not just another scraping project!</b>\n",
    "  </p>\n",
    "</font>\n",
    "<font size=3 align='left'> \n",
    "This project aims to scrape restaurant details from any city in any country, along with a <font color=red>tadka</font> of Exploratory Data Analysis from the scraped data. If this doesn't make you the pundit of web scraping using Python, what will? ;)\n",
    "</font>\n",
    "<img src='https://s3.memeshappen.com/memes/wow--meme-10622.jpg' width=\"200\" height=\"200\" align=\"center\">"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<font size=5 align='left'>\n",
    "  <p>\n",
    "      <img style=\"padding-right: 10px;padding-top: 0px;\" src='https://i.ibb.co/878wW18/resttt-fotor-bg-remover-20230315225922.png' width=\"40\" height=\"40\" align=\"left\"> \n",
    "     <b>How would the scraped csv look like?</b></p>\n",
    "</font>\n",
    "<font size=3 align='left'>   \n",
    "The scraped restaurant data would contain:\n",
    "<ul>\n",
    "    <b><li><font color=#F36530>Name of the Restaurant</font></li>\n",
    "        <li><font color=#F36530>Total Reviews</font></li>\n",
    "        <li><font color=#F36530>Star/Bubble Rating</font></li>\n",
    "        <li><font color=#F36530>Cuisines</font></li></b>\n",
    "</ul>\n",
    "Other useful information that can be used for debugging includes page number, restaurant serial number, and data offset.\n",
    "\n",
    "<img src=\"https://i.ibb.co/tM6t1NS/csv-screenshot.png\" width=\"750\" height =\"750\">\n",
    "\n",
    "The above sample is of Berlin, Germany.\n",
    "</font>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<img src='https://i.ibb.co/wKTTTFh/153783366-lets-start-inscription-handwritten-lettering-illustration-black-vector-text-in-speech-bubb.png' width=\"300\" height=\"300\" align=\"center\">"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<font size=5 align='left'>\n",
    "  <p>\n",
    "      <img style=\"padding-right: 10px;padding-top: 0px;\" src='https://i.ibb.co/878wW18/resttt-fotor-bg-remover-20230315225922.png' width=\"40\" height=\"40\" align=\"left\"> \n",
    "     <b>Control Variable/Input Parameters</b></p>\n",
    "</font>\n",
    "\n",
    "<font size=3 align='left'> \n",
    "    <p>\n",
    "In this project, we have selected Berlin, Germany. For example, if we wish to scrape all restaurants in Bangalore, Karnataka, we will filter the same on Tripadvisor, and we will get a link that would look something like this:<br></br>\n",
    "<font color='deep pink'>\"https://www.tripadvisor.in/Restaurants-g297628-Bengaluru_Bangalore_District_Karnataka.html\".</font><br></br>\n",
    "        In this link, \"297628\" is the geo-code, and \"Bengaluru_Bangalore_District_Karnataka\" is the city name. If we notice, on Tripadvisor, it shows around 11,127 restaurants for Bengaluru. So now, our input parameters would be:\n",
    "<ul>\n",
    "    <b><li><font color=#F36530>Geo Code</font></li>\n",
    "        <li><font color=#F36530>City Name</font></li>\n",
    "        <li><font color=#F36530>Upper Data Offset</font></li>\n",
    "</ul>\n",
    "    </p>\n",
    "</font>\n",
    "</br>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<font size=3 align='left'>\n",
    "Now that we have selected a city along with its geo-code of our choice, let's proceed with the script. <br></br> The first step is to import the required libraries. (Install if needed.)\n",
    "The next step is to define the control variables. Since we will be scraping restaurant data from Berlin, Germany, we will define the variables accordingly. Also, there are a total of 30 restaurants listed per page on TripAdvisor, which constitutes our page size. The last page has the data  offset of 6330, which would be our data offset upper limit. <br></br>\n",
    "These control variables will change according to the city we are trying to scrape."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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      "Note: you may need to restart the kernel to use updated packages.\n"
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    }
   ],
   "source": [
    "pip install \"requests_html\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: bs4 in /opt/conda/lib/python3.9/site-packages (0.0.1)\n",
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      "Note: you may need to restart the kernel to use updated packages.\n"
     ]
    }
   ],
   "source": [
    "pip install \"bs4\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "# pip install \"requests.html\"\n",
    "\n",
    "# Import Libraries\n",
    "import requests\n",
    "from requests_html import HTMLSession\n",
    "from bs4 import BeautifulSoup\n",
    "import pandas as pd\n",
    "import functools\n",
    "import time\n",
    "\n",
    "# store the url in a variable\n",
    "scraping_control_variables = {\n",
    "    \"city_name\": \"Berlin\",\n",
    "    \"geo_code\": \"187323\",\n",
    "    \"data_offset_lower_limit\": 0,\n",
    "    \"data_offset_upper_limit\": 6330,\n",
    "    \"starting_page_num\": 0,\n",
    "    \"page_size\": 30,\n",
    "}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<font size=3 align='left'> There are a total of 11 functions that will be used in this script.\n",
    "<ul>\n",
    "    <li><font size = 3 color=teal>get_url</font></li>\n",
    "    <li><font size = 3 color=teal>get_soup_content</font></li>\n",
    "    <li><font size = 3 color=teal>get_card</font></li>\n",
    "    <li><font size = 3 color=teal>parse_tripadvisor</font></li>\n",
    "    <li><font size = 3 color=teal>get_parsed_restaurant_data_for_page</font></li>\n",
    "    <li><font size = 3 color=teal>get_restaurant_data_from_card</font></li>\n",
    "    <li><font size = 3 color=teal>scrape_star_ratings</font></li>\n",
    "    <li><font size = 3 color=teal>scrape_reviews</font></li>\n",
    "    <li><font size = 3 color=teal>scrape_cuisines</font></li>\n",
    "    <li><font size = 3 color=teal>scrape_title</font></li>\n",
    "    <li><font size = 3 color=teal>save_to_csv</font></li>\n",
    "</ul>\n",
    "    Let's go through each function one by one.\n",
    "</font>\n",
    "  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<font size=5 align='left'>\n",
    "  <p>\n",
    "      <img style=\"padding-right: 10px;padding-top: 0px;\" src='https://i.ibb.co/878wW18/resttt-fotor-bg-remover-20230315225922.png' width=\"40\" height=\"40\" align=\"left\"> \n",
    "     <b>get_url</b></p>\n",
    "</font>\n",
    "<font size=3 align='left'> \n",
    "    <font color = teal>get_url</font> takes geo-code, data offset, and city name as inputs and creates a different url for every page to be scraped. The url follows a pattern as the data offset is a multiple of 30. For Example,<br>\n",
    "    <p STYLE=\"text-transform:lowercase\">\n",
    "        <a href=\"url\">https://www.tripadvisor.in/Restaurants-g187323-oa90-Berlin.html#EATERY_LIST_CONTENTS<br></a>\n",
    "        <a href=\"url\">https://www.tripadvisor.in/Restaurants-g187323-oa120-Berlin.html#EATERY_LIST_CONTENTS<br></a>\n",
    "        <a href=\"url\">https://www.tripadvisor.in/Restaurants-g187323-oa150-Berlin.html#EATERY_LIST_CONTENTS</br></a>\n",
    "    </p>\n",
    "    \n",
    "</font>\n",
    "</br>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Function to get URL for every page\n",
    "\n",
    "def get_url(gc, do, city):\n",
    "    data_offset_var = \"-oa\" + str(do)\n",
    "    if do == 0:\n",
    "        data_offset_var = \"\"\n",
    "    url = f\"https://www.tripadvisor.in/RestaurantSearch-g{gc}{data_offset_var}-a_date.2023__2D__03__2D__05-a_people.2-a_time.20%3A00%3A00-a_zur.2023__5F__03__5F__05-{city}.html#EATERY_LIST_CONTENTS\"\n",
    "    print(\"URL to be scraped: \", \"\\n\", url, \"\\n\")\n",
    "    return url"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<font size=5 align='left'>\n",
    "  <p>\n",
    "      <img style=\"padding-right: 10px;padding-top: 0px;\" src='https://i.ibb.co/878wW18/resttt-fotor-bg-remover-20230315225922.png' width=\"40\" height=\"40\" align=\"left\"> \n",
    "     <b>get_soup_content</b></p>\n",
    "</font>\n",
    "<font size=3 align='left'> \n",
    "<font color = teal>get_soup_content</font> takes geo-code, data-offset and city name as an input to call get_url function. Also, it creates a response object using the url obtained. Once the HTML is accessible, we need to parse the HTML and load it into a BS4 structure. This soup object is very handy and allows us to access useful pieces of information such as title, cuisines, ratings etc.\n",
    "</font>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Function to get soup content\n",
    "\n",
    "def get_soup_content(gc, city, do):\n",
    "    url = get_url(gc, do, city)\n",
    "    # start the HTML session\n",
    "    print(\"HTML session started\")\n",
    "    r = HTMLSession()\n",
    "    response_obj = r.get(url, verify=False)\n",
    "    soup_content = BeautifulSoup(response_obj.content, \"html.parser\")\n",
    "    return soup_content"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<font size=5 align='left'>\n",
    "  <p>\n",
    "    <img style=\"padding-right: 10px;padding-top: 0px;\" src='https://i.ibb.co/878wW18/resttt-fotor-bg-remover-20230315225922.png' width=\"40\" height=\"40\" align=\"left\">\n",
    "    <b>get_card</b>\n",
    "  </p>\n",
    "</font>\n",
    "<font size=3 align='left'> \n",
    "The <font color = teal>get_card </font> function will help in getting the individual restaurant cards according to the restaurant serial number or restaurant count. The card tags follow this pattern: 1_list_item, 2_list_item, 3_list_item, etc.<br>\n",
    "*Screenshot for reference*\n",
    "</font>\n",
    "<img src=\"https://i.ibb.co/cg2BN3w/ccardd.png\" width = \"1000\" height = \"600\"  >"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_card(rest_cnt, soup_content):\n",
    "    card_tag = f\"{rest_cnt}_list_item\"\n",
    "    print(f\"Scraping item number: {card_tag}\")\n",
    "    card = soup_content.find(\"div\", {\"data-test\": card_tag})\n",
    "    return card"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<font size=5 align='left'>\n",
    "  <p>\n",
    "    <img style=\"padding-right: 10px;padding-top: 0px;\" src='https://i.ibb.co/878wW18/resttt-fotor-bg-remover-20230315225922.png' width=\"40\" height=\"40\" align=\"left\">\n",
    "    <b>parse_tripadvisor and get_parsed_restaurant_data_for_page</b>\n",
    "  </p>\n",
    "</font>\n",
    "<font size=3 align='left'> \n",
    "The <font color = teal>parse_tripadvisor</font> function takes scraping control variables(scv) defined in the earlier steps as an input. This is one of the most important functions in the script. We have declared an empty list, restaurants_scraped which will store all the restaurant listings listed for Berlin.<br> \n",
    "    current_data_offset = 0 (data_offset_lower_limit)<br>\n",
    "    current_page_number = 0 (starting_page_num)<br>\n",
    " Now the loop runs starting from 0 untill 6330 (data_offset_upper_limit). For the first page, page number would be 0 and the <font color = teal>get_soup_content</font> gets the html for the first page which has the current_data_offset as 0.<br>\n",
    "    \n",
    "The <font color = teal>get_parsed_restaurant_data_for_page</font> takes the current_page_number as 0, scv[\"page_size\"] as 30, soup_content(obtained above), and current_data_offset as 0.\n",
    "<font color = teal>get_restaurant_data_from_card</font> gets restaurant details like title, ratings, cuisines, reviews using individual restaurant cards based on the rest_cnt. The list, parsed_restaurant_data_for_page obtained is added to the empty list, restaurants_scraped.\n",
    "Since we can't be completely sure if the page would contain fewer than 30 restaurants, we have also included the if condition inside the loop for the function <font color = teal>get_parsed_restaurant_data_for_page</font>, which takes care of that.\n",
    "</font></br>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "# scrape & parse restaurants pagewise till data_offset_upper_limit is reached\n",
    "def parse_tripadvisor(scv):\n",
    "    restaurants_scraped = []\n",
    "    current_data_offset, current_page_number = scv[\"data_offset_lower_limit\"],scv[\"starting_page_num\"]\n",
    "    while current_data_offset <= scv[\"data_offset_upper_limit\"]:\n",
    "        print(f\"Scraping started for Page Number: {current_page_number} with Dataoffset: {current_data_offset}\\n\")\n",
    "        soup_content = get_soup_content(scv[\"geo_code\"], scv[\"city_name\"], current_data_offset)\n",
    "        page_restaurant_data = get_parsed_restaurant_data_for_page(current_page_number, scv[\"page_size\"], soup_content, current_data_offset)\n",
    "        restaurants_scraped += page_restaurant_data\n",
    "        print(f\"Scraping completed for Page Number: {current_page_number} with Dataoffset: {current_data_offset}\\n\")\n",
    "        current_page_number = current_page_number + 1\n",
    "        current_data_offset = current_data_offset + 30\n",
    "    return restaurants_scraped"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Iterate over page's soup_content to parse all cards and give a list parsed_restaurant_data_for_page having card_restaurant_data\n",
    "def get_parsed_restaurant_data_for_page(page_num, page_size, soup_content, current_data_offset):\n",
    "    page_start_offset = (page_num * page_size) + 1\n",
    "    page_end_offset = page_start_offset + page_size\n",
    "    parsed_restaurant_data_for_page = []\n",
    "    for rest_cnt in range(page_start_offset, page_end_offset):\n",
    "        card = get_card(rest_cnt, soup_content)\n",
    "        if card is None:\n",
    "            break\n",
    "        card_restaurant_data = get_restaurant_data_from_card(card, rest_cnt, current_data_offset, page_num)\n",
    "        parsed_restaurant_data_for_page.append(card_restaurant_data)\n",
    "    return parsed_restaurant_data_for_page"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<font size=5 align='left'>\n",
    "  <p>\n",
    "    <img style=\"padding-right: 10px;padding-top: 0px;\" src='https://i.ibb.co/878wW18/resttt-fotor-bg-remover-20230315225922.png' width=\"40\" height=\"40\" align=\"left\">\n",
    "    <b>get_restaurant_data_from_card</b>\n",
    "  </p>\n",
    "</font>\n",
    "<font size=3 align='left'>\n",
    "<font color = teal>get_restaurant_data_from_card</font> function takes the restaurant count, current data offset, page number, and card number as input and calls individual scrape functions created to get restaurant details. \n",
    "</font>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_restaurant_data_from_card(card, rest_cnt, current_data_offset, page_num):\n",
    "    restaurant_data = {\n",
    "        \"title\": scrape_title(card),\n",
    "        \"cuisines\": scrape_cuisines(card),\n",
    "        \"reviews\": scrape_reviews(card),\n",
    "        \"star rating\": scrape_star_ratings(card),\n",
    "        \"page number\": page_num,\n",
    "        \"data offset\": current_data_offset,\n",
    "        \"restaurant serial number\": rest_cnt,\n",
    "    }\n",
    "\n",
    "    return restaurant_data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<font size=5 align='left'>\n",
    "  <p>\n",
    "    <img style=\"padding-right: 10px;padding-top: 0px;\" src='https://i.ibb.co/878wW18/resttt-fotor-bg-remover-20230315225922.png' width=\"40\" height=\"40\" align=\"left\">\n",
    "    <b>Scraping Functions to get Restaurant Details</b>\n",
    "  </p>\n",
    "</font>\n",
    "<font size=3 align='left'>\n",
    "All the below functions take the card as input which contain all information pertaining to a particular restauarant.<br>\n",
    "<font color = teal>scrape_star_ratings</font> - gets the star/customer rating of the restaurant.<br>\n",
    "<font color = teal>scrape_reviews</font> - gets the total reviews of the restaurant.<br>\n",
    "<font color = teal>scrape_cuisines</font> - gets all the cuisines offered by the restaurant.<br>\n",
    "<font color = teal>scrape_title</font> - gets the name of the restaurant.<br>\n",
    "</font>\n",
    "<font size=3 align='left'><b>\n",
    "    <ul>\n",
    "    <li>Title:</li>\n",
    "        <img src=\"https://i.ibb.co/B2PQYc3/title-tag.png\" width = \"400\" height = \"300\" align = \"left\" ><BR CLEAR=ALL /><br>\n",
    "    <li>Ratings</li>\n",
    "        <img src=\"https://i.ibb.co/RQwTJcC/rating-tag.png\" width = \"1000\" height = \"300\" align = \"left\"><BR CLEAR=ALL /><br>\n",
    "    <li>Reviews</li>\n",
    "        <img src=\"https://i.ibb.co/Tct2tpG/reviews-tag.png\" width = \"400\" height = \"300\" align = \"left\"><BR CLEAR=ALL /><br>\n",
    "    <li>Cuisines</li>\n",
    "        <img src=\"https://i.ibb.co/CQj13vq/cuisines-tag.png\" width = \"400\" height = \"300\" align = \"left\">\n",
    "    </ul>\n",
    " </font>\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "def scrape_star_ratings(card):\n",
    "    star_rating = card.find_all(\"svg\", class_=\"UctUV d H0\")\n",
    "    scraped_star_ratings = (star_rating[0][\"aria-label\"] if len(star_rating) >= 1 else None)\n",
    "    return scraped_star_ratings\n",
    "\n",
    "\n",
    "def scrape_reviews(card):\n",
    "    reviews = card.find_all(\"span\", class_=\"IiChw\")\n",
    "    scraped_reviews = reviews[-1].text if len(reviews) >= 1 else None\n",
    "    return scraped_reviews\n",
    "\n",
    "\n",
    "def scrape_cuisines(card):\n",
    "    cu_1 = card.find(\"div\", class_=\"hBcUX XFrjQ mIBqD\")\n",
    "    try:\n",
    "        cu = cu_1.find(\"span\", class_=\"SUszq\").get_text()\n",
    "    except AttributeError:\n",
    "        cu = None\n",
    "    return cu\n",
    "\n",
    "\n",
    "def scrape_title(card):\n",
    "    title = card.find_all(\"div\", class_=\"RfBGI\")\n",
    "    scraped_title = None if len(title) < 1 else title[0].text\n",
    "    return scraped_title"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<font size=5 align='left'>\n",
    "  <p>\n",
    "    <img style=\"padding-right: 10px;padding-top: 0px;\" src='https://i.ibb.co/878wW18/resttt-fotor-bg-remover-20230315225922.png' width=\"40\" height=\"40\" align=\"left\">\n",
    "    <b>Saving the scraped file as a CSV</b>\n",
    "  </p>\n",
    "</font>\n",
    "<font size=3 align='left'>\n",
    "Finally, let's save the dataframe as a csv in our local repository, and we are done! This csv can be used for any data analysis and data science projects.\n",
    "</font>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "def save_to_csv(restaurants_scraped):\n",
    "    # finally, store the output into a csv file\n",
    "    print(\"storing the data in csv\")\n",
    "    output_df = pd.DataFrame(restaurants_scraped)\n",
    "    print(output_df.head(50))\n",
    "#     output_df.drop_duplicates(inplace=True)\n",
    "    output_df.to_csv(\"ta_berlin_restaurants_scraped.csv\", index= False)\n",
    "    print(\"csv stored\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [],
   "source": [
    "def scrape_and_save(scraping_control_variables):\n",
    "    restaurants_scraped = parse_tripadvisor(scraping_control_variables)\n",
    "    save_to_csv(restaurants_scraped)\n",
    "    return restaurants_scraped"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Scraping started for Page Number: 0 with Dataoffset: 0\n",
      "\n",
      "URL to be scraped:  \n",
      " https://www.tripadvisor.in/RestaurantSearch-g187323-a_date.2023__2D__03__2D__05-a_people.2-a_time.20%3A00%3A00-a_zur.2023__5F__03__5F__05-Berlin.html#EATERY_LIST_CONTENTS \n",
      "\n",
      "HTML session started\n",
      "Scraping item number: 1_list_item\n",
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      "Scraping completed for Page Number: 0 with Dataoffset: 0\n",
      "\n",
      "Scraping started for Page Number: 1 with Dataoffset: 30\n",
      "\n",
      "URL to be scraped:  \n",
      " https://www.tripadvisor.in/RestaurantSearch-g187323-oa30-a_date.2023__2D__03__2D__05-a_people.2-a_time.20%3A00%3A00-a_zur.2023__5F__03__5F__05-Berlin.html#EATERY_LIST_CONTENTS \n",
      "\n",
      "HTML session started\n",
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      "Scraping completed for Page Number: 1 with Dataoffset: 30\n",
      "\n",
      "Scraping started for Page Number: 2 with Dataoffset: 60\n",
      "\n",
      "URL to be scraped:  \n",
      " https://www.tripadvisor.in/RestaurantSearch-g187323-oa60-a_date.2023__2D__03__2D__05-a_people.2-a_time.20%3A00%3A00-a_zur.2023__5F__03__5F__05-Berlin.html#EATERY_LIST_CONTENTS \n",
      "\n",
      "HTML session started\n",
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      "Scraping completed for Page Number: 2 with Dataoffset: 60\n",
      "\n",
      "Scraping started for Page Number: 3 with Dataoffset: 90\n",
      "\n",
      "URL to be scraped:  \n",
      " https://www.tripadvisor.in/RestaurantSearch-g187323-oa90-a_date.2023__2D__03__2D__05-a_people.2-a_time.20%3A00%3A00-a_zur.2023__5F__03__5F__05-Berlin.html#EATERY_LIST_CONTENTS \n",
      "\n",
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      "\n",
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      "\n",
      "URL to be scraped:  \n",
      " https://www.tripadvisor.in/RestaurantSearch-g187323-oa120-a_date.2023__2D__03__2D__05-a_people.2-a_time.20%3A00%3A00-a_zur.2023__5F__03__5F__05-Berlin.html#EATERY_LIST_CONTENTS \n",
      "\n",
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      "\n",
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      "\n",
      "URL to be scraped:  \n",
      " https://www.tripadvisor.in/RestaurantSearch-g187323-oa150-a_date.2023__2D__03__2D__05-a_people.2-a_time.20%3A00%3A00-a_zur.2023__5F__03__5F__05-Berlin.html#EATERY_LIST_CONTENTS \n",
      "\n",
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      "\n",
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      "\n",
      "URL to be scraped:  \n",
      " https://www.tripadvisor.in/RestaurantSearch-g187323-oa180-a_date.2023__2D__03__2D__05-a_people.2-a_time.20%3A00%3A00-a_zur.2023__5F__03__5F__05-Berlin.html#EATERY_LIST_CONTENTS \n",
      "\n",
      "HTML session started\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
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      "\n",
      "URL to be scraped:  \n",
      " https://www.tripadvisor.in/RestaurantSearch-g187323-oa210-a_date.2023__2D__03__2D__05-a_people.2-a_time.20%3A00%3A00-a_zur.2023__5F__03__5F__05-Berlin.html#EATERY_LIST_CONTENTS \n",
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      "\n",
      "storing the data in csv\n",
      "                                         title                  cuisines  \\\n",
      "0                      1. Cafe Couscous - Vege             Cafe, Healthy   \n",
      "1                 2. Clubrestaurant am Wannsee          German, European   \n",
      "2                                   3. happies          European, Fusion   \n",
      "3                                    4. Haveli             Indian, Asian   \n",
      "4                                5. Hackethals               German, Bar   \n",
      "5                               6. Ataya Caffe          Italian, Healthy   \n",
      "6                         7. Ristorante A MANO          Italian, Seafood   \n",
      "7                    8. Restaurant Buschbeck's          German, European   \n",
      "8                               9. Burger Turm       American, Fast food   \n",
      "9                              10. Naveenapath             Indian, Asian   \n",
      "10           11. Vedang - plant burger (Alexa)        Fast food, Healthy   \n",
      "11                                 12. El Loco     Mexican, Southwestern   \n",
      "12        13. Indisches Restaurant Maharadscha                    Indian   \n",
      "13                          14. Sapori di Casa            Italian, Pizza   \n",
      "14                            15. Hasty Pastry            Cafe, European   \n",
      "15                              16. Skykitchen             International   \n",
      "16                           17. 100 Gramm Bar     Eastern European, Bar   \n",
      "17                                 18. Salamat    Middle Eastern, Arabic   \n",
      "18                 19. Flamingo Fresh Food Bar       Cafe, International   \n",
      "19  20. Vedang - plant burger (Mall of Berlin)       American, Fast food   \n",
      "20        21. Pequena Habana Restaurant Berlin          Caribbean, Latin   \n",
      "21                          22. La Gondola Due    Italian, Mediterranean   \n",
      "22                      23. MontRaw Restaurant   Mediterranean, European   \n",
      "23                                   24. Vedis             Indian, Grill   \n",
      "24                    25. Restaurant Bieberbau  German, Central European   \n",
      "25                     26. Trattoria Portofino            Italian, Pizza   \n",
      "26                     27. Gasthaus Kater Alex     German, International   \n",
      "27                                    28. Goji            Chinese, Asian   \n",
      "28                               29. La Caleta    Seafood, Mediterranean   \n",
      "29                               30. Focaccino         Italian, Sicilian   \n",
      "30                  31. EBERT Restaurant & Bar             Grill, German   \n",
      "31                  32. Ristorante Peperoncino                   Italian   \n",
      "32                        33. Restaurant Facil   International, European   \n",
      "33                     34. Kurpfalz-Weinstuben          German, European   \n",
      "34                            35. Maison Umami         Asian, Vietnamese   \n",
      "35                       36. Fes - Turkish BBQ    Turkish, Mediterranean   \n",
      "36                  37. Lorenz Adlon Esszimmer          French, European   \n",
      "37          38. Ristorante & Vinobar CaliBocca         Italian, Wine Bar   \n",
      "38                 39. Quy Nguyen Vegan Living         Asian, Vietnamese   \n",
      "39                                40. Zur Haxe          German, European   \n",
      "40                                 41. Heising          French, European   \n",
      "41                       42. Ristorante Cinque    Italian, Mediterranean   \n",
      "42                                 43. Namaste             Indian, Asian   \n",
      "43                      44. Upper Burger Grill             American, Bar   \n",
      "44                     45. Ushido-Japanese BBQ      Steakhouse, Japanese   \n",
      "45                          46. Zsa Zsa Burger                  American   \n",
      "46                                    47. 1990         Asian, Vietnamese   \n",
      "47                                 48. Dae Mon     Fusion, International   \n",
      "48                       49. Oyster Bar KaDeWe         Seafood, European   \n",
      "49                    50. La Cantine d'Augusta          French, European   \n",
      "\n",
      "          reviews       star rating  page number  data offset  \\\n",
      "0     133 reviews  5.0 of 5 bubbles            0            0   \n",
      "1     239 reviews  5.0 of 5 bubbles            0            0   \n",
      "2     391 reviews  5.0 of 5 bubbles            0            0   \n",
      "3   1,417 reviews  4.5 of 5 bubbles            0            0   \n",
      "4   1,516 reviews  4.5 of 5 bubbles            0            0   \n",
      "5     221 reviews  5.0 of 5 bubbles            0            0   \n",
      "6   2,631 reviews  4.5 of 5 bubbles            0            0   \n",
      "7     351 reviews  5.0 of 5 bubbles            0            0   \n",
      "8     882 reviews  5.0 of 5 bubbles            0            0   \n",
      "9     738 reviews  5.0 of 5 bubbles            0            0   \n",
      "10    324 reviews  5.0 of 5 bubbles            0            0   \n",
      "11    248 reviews  4.5 of 5 bubbles            0            0   \n",
      "12  1,711 reviews  4.5 of 5 bubbles            0            0   \n",
      "13    438 reviews  5.0 of 5 bubbles            0            0   \n",
      "14    292 reviews  4.5 of 5 bubbles            0            0   \n",
      "15    397 reviews  4.5 of 5 bubbles            0            0   \n",
      "16    179 reviews  5.0 of 5 bubbles            0            0   \n",
      "17    188 reviews  5.0 of 5 bubbles            0            0   \n",
      "18    969 reviews  4.5 of 5 bubbles            0            0   \n",
      "19    597 reviews  5.0 of 5 bubbles            0            0   \n",
      "20    228 reviews  4.5 of 5 bubbles            0            0   \n",
      "21    579 reviews  4.5 of 5 bubbles            0            0   \n",
      "22    253 reviews  4.5 of 5 bubbles            0            0   \n",
      "23  1,241 reviews  5.0 of 5 bubbles            0            0   \n",
      "24    936 reviews  4.5 of 5 bubbles            0            0   \n",
      "25    988 reviews  4.5 of 5 bubbles            0            0   \n",
      "26    308 reviews  4.5 of 5 bubbles            0            0   \n",
      "27    307 reviews  4.5 of 5 bubbles            0            0   \n",
      "28    411 reviews  4.5 of 5 bubbles            0            0   \n",
      "29    683 reviews  4.5 of 5 bubbles            0            0   \n",
      "30    399 reviews  4.5 of 5 bubbles            1           30   \n",
      "31    712 reviews  4.5 of 5 bubbles            1           30   \n",
      "32    940 reviews  4.5 of 5 bubbles            1           30   \n",
      "33    621 reviews  4.5 of 5 bubbles            1           30   \n",
      "34    309 reviews  4.5 of 5 bubbles            1           30   \n",
      "35    387 reviews  4.5 of 5 bubbles            1           30   \n",
      "36    425 reviews  4.5 of 5 bubbles            1           30   \n",
      "37    336 reviews  4.5 of 5 bubbles            1           30   \n",
      "38    291 reviews  4.5 of 5 bubbles            1           30   \n",
      "39    465 reviews  4.5 of 5 bubbles            1           30   \n",
      "40  1,162 reviews  4.5 of 5 bubbles            1           30   \n",
      "41    250 reviews  4.5 of 5 bubbles            1           30   \n",
      "42    678 reviews  4.5 of 5 bubbles            1           30   \n",
      "43  1,280 reviews  4.5 of 5 bubbles            1           30   \n",
      "44    432 reviews  4.5 of 5 bubbles            1           30   \n",
      "45  1,157 reviews  4.5 of 5 bubbles            1           30   \n",
      "46    274 reviews  4.5 of 5 bubbles            1           30   \n",
      "47    387 reviews  4.5 of 5 bubbles            1           30   \n",
      "48    367 reviews  4.5 of 5 bubbles            1           30   \n",
      "49    243 reviews  4.5 of 5 bubbles            1           30   \n",
      "\n",
      "    restaurant serial number  \n",
      "0                          1  \n",
      "1                          2  \n",
      "2                          3  \n",
      "3                          4  \n",
      "4                          5  \n",
      "5                          6  \n",
      "6                          7  \n",
      "7                          8  \n",
      "8                          9  \n",
      "9                         10  \n",
      "10                        11  \n",
      "11                        12  \n",
      "12                        13  \n",
      "13                        14  \n",
      "14                        15  \n",
      "15                        16  \n",
      "16                        17  \n",
      "17                        18  \n",
      "18                        19  \n",
      "19                        20  \n",
      "20                        21  \n",
      "21                        22  \n",
      "22                        23  \n",
      "23                        24  \n",
      "24                        25  \n",
      "25                        26  \n",
      "26                        27  \n",
      "27                        28  \n",
      "28                        29  \n",
      "29                        30  \n",
      "30                        31  \n",
      "31                        32  \n",
      "32                        33  \n",
      "33                        34  \n",
      "34                        35  \n",
      "35                        36  \n",
      "36                        37  \n",
      "37                        38  \n",
      "38                        39  \n",
      "39                        40  \n",
      "40                        41  \n",
      "41                        42  \n",
      "42                        43  \n",
      "43                        44  \n",
      "44                        45  \n",
      "45                        46  \n",
      "46                        47  \n",
      "47                        48  \n",
      "48                        49  \n",
      "49                        50  \n",
      "csv stored\n",
      "Reastaurant Data Scraped:\n",
      "                                          title                cuisines  \\\n",
      "0                      1. Cafe Couscous - Vege           Cafe, Healthy   \n",
      "1                 2. Clubrestaurant am Wannsee        German, European   \n",
      "2                                   3. happies        European, Fusion   \n",
      "3                                    4. Haveli           Indian, Asian   \n",
      "4                                5. Hackethals             German, Bar   \n",
      "5                               6. Ataya Caffe        Italian, Healthy   \n",
      "6                         7. Ristorante A MANO        Italian, Seafood   \n",
      "7                    8. Restaurant Buschbeck's        German, European   \n",
      "8                               9. Burger Turm     American, Fast food   \n",
      "9                              10. Naveenapath           Indian, Asian   \n",
      "10           11. Vedang - plant burger (Alexa)      Fast food, Healthy   \n",
      "11                                 12. El Loco   Mexican, Southwestern   \n",
      "12        13. Indisches Restaurant Maharadscha                  Indian   \n",
      "13                          14. Sapori di Casa          Italian, Pizza   \n",
      "14                            15. Hasty Pastry          Cafe, European   \n",
      "15                              16. Skykitchen           International   \n",
      "16                           17. 100 Gramm Bar   Eastern European, Bar   \n",
      "17                                 18. Salamat  Middle Eastern, Arabic   \n",
      "18                 19. Flamingo Fresh Food Bar     Cafe, International   \n",
      "19  20. Vedang - plant burger (Mall of Berlin)     American, Fast food   \n",
      "\n",
      "          reviews       star rating  page number  data offset  \\\n",
      "0     133 reviews  5.0 of 5 bubbles            0            0   \n",
      "1     239 reviews  5.0 of 5 bubbles            0            0   \n",
      "2     391 reviews  5.0 of 5 bubbles            0            0   \n",
      "3   1,417 reviews  4.5 of 5 bubbles            0            0   \n",
      "4   1,516 reviews  4.5 of 5 bubbles            0            0   \n",
      "5     221 reviews  5.0 of 5 bubbles            0            0   \n",
      "6   2,631 reviews  4.5 of 5 bubbles            0            0   \n",
      "7     351 reviews  5.0 of 5 bubbles            0            0   \n",
      "8     882 reviews  5.0 of 5 bubbles            0            0   \n",
      "9     738 reviews  5.0 of 5 bubbles            0            0   \n",
      "10    324 reviews  5.0 of 5 bubbles            0            0   \n",
      "11    248 reviews  4.5 of 5 bubbles            0            0   \n",
      "12  1,711 reviews  4.5 of 5 bubbles            0            0   \n",
      "13    438 reviews  5.0 of 5 bubbles            0            0   \n",
      "14    292 reviews  4.5 of 5 bubbles            0            0   \n",
      "15    397 reviews  4.5 of 5 bubbles            0            0   \n",
      "16    179 reviews  5.0 of 5 bubbles            0            0   \n",
      "17    188 reviews  5.0 of 5 bubbles            0            0   \n",
      "18    969 reviews  4.5 of 5 bubbles            0            0   \n",
      "19    597 reviews  5.0 of 5 bubbles            0            0   \n",
      "\n",
      "    restaurant serial number  \n",
      "0                          1  \n",
      "1                          2  \n",
      "2                          3  \n",
      "3                          4  \n",
      "4                          5  \n",
      "5                          6  \n",
      "6                          7  \n",
      "7                          8  \n",
      "8                          9  \n",
      "9                         10  \n",
      "10                        11  \n",
      "11                        12  \n",
      "12                        13  \n",
      "13                        14  \n",
      "14                        15  \n",
      "15                        16  \n",
      "16                        17  \n",
      "17                        18  \n",
      "18                        19  \n",
      "19                        20  \n"
     ]
    }
   ],
   "source": [
    "# disable secure certificate warnings\n",
    "requests.packages.urllib3.disable_warnings()\n",
    "restaurants_scraped = scrape_and_save(scraping_control_variables)\n",
    "\n",
    "# Print Dataframe\n",
    "scraped_df = pd.DataFrame(restaurants_scraped)\n",
    "print(\"Reastaurant Data Scraped:\\n\",scraped_df.head(20) )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total Restaurants scraped:\t 6360\n",
      "Restaurants scraped:\t                                          title                cuisines  \\\n",
      "0                      1. Cafe Couscous - Vege           Cafe, Healthy   \n",
      "1                 2. Clubrestaurant am Wannsee        German, European   \n",
      "2                                   3. happies        European, Fusion   \n",
      "3                                    4. Haveli           Indian, Asian   \n",
      "4                                5. Hackethals             German, Bar   \n",
      "5                               6. Ataya Caffe        Italian, Healthy   \n",
      "6                         7. Ristorante A MANO        Italian, Seafood   \n",
      "7                    8. Restaurant Buschbeck's        German, European   \n",
      "8                               9. Burger Turm     American, Fast food   \n",
      "9                              10. Naveenapath           Indian, Asian   \n",
      "10           11. Vedang - plant burger (Alexa)      Fast food, Healthy   \n",
      "11                                 12. El Loco   Mexican, Southwestern   \n",
      "12        13. Indisches Restaurant Maharadscha                  Indian   \n",
      "13                          14. Sapori di Casa          Italian, Pizza   \n",
      "14                            15. Hasty Pastry          Cafe, European   \n",
      "15                              16. Skykitchen           International   \n",
      "16                           17. 100 Gramm Bar   Eastern European, Bar   \n",
      "17                                 18. Salamat  Middle Eastern, Arabic   \n",
      "18                 19. Flamingo Fresh Food Bar     Cafe, International   \n",
      "19  20. Vedang - plant burger (Mall of Berlin)     American, Fast food   \n",
      "\n",
      "          reviews       star rating  page number  data offset  \\\n",
      "0     133 reviews  5.0 of 5 bubbles            0            0   \n",
      "1     239 reviews  5.0 of 5 bubbles            0            0   \n",
      "2     391 reviews  5.0 of 5 bubbles            0            0   \n",
      "3   1,417 reviews  4.5 of 5 bubbles            0            0   \n",
      "4   1,516 reviews  4.5 of 5 bubbles            0            0   \n",
      "5     221 reviews  5.0 of 5 bubbles            0            0   \n",
      "6   2,631 reviews  4.5 of 5 bubbles            0            0   \n",
      "7     351 reviews  5.0 of 5 bubbles            0            0   \n",
      "8     882 reviews  5.0 of 5 bubbles            0            0   \n",
      "9     738 reviews  5.0 of 5 bubbles            0            0   \n",
      "10    324 reviews  5.0 of 5 bubbles            0            0   \n",
      "11    248 reviews  4.5 of 5 bubbles            0            0   \n",
      "12  1,711 reviews  4.5 of 5 bubbles            0            0   \n",
      "13    438 reviews  5.0 of 5 bubbles            0            0   \n",
      "14    292 reviews  4.5 of 5 bubbles            0            0   \n",
      "15    397 reviews  4.5 of 5 bubbles            0            0   \n",
      "16    179 reviews  5.0 of 5 bubbles            0            0   \n",
      "17    188 reviews  5.0 of 5 bubbles            0            0   \n",
      "18    969 reviews  4.5 of 5 bubbles            0            0   \n",
      "19    597 reviews  5.0 of 5 bubbles            0            0   \n",
      "\n",
      "    restaurant serial number sr_no                         restaurant_name  \n",
      "0                          1    1.                    Cafe Couscous - Vege  \n",
      "1                          2    2.               Clubrestaurant am Wannsee  \n",
      "2                          3    3.                                 happies  \n",
      "3                          4    4.                                  Haveli  \n",
      "4                          5    5.                              Hackethals  \n",
      "5                          6    6.                             Ataya Caffe  \n",
      "6                          7    7.                       Ristorante A MANO  \n",
      "7                          8    8.                  Restaurant Buschbeck's  \n",
      "8                          9    9.                             Burger Turm  \n",
      "9                         10   10.                             Naveenapath  \n",
      "10                        11   11.           Vedang - plant burger (Alexa)  \n",
      "11                        12   12.                                 El Loco  \n",
      "12                        13   13.        Indisches Restaurant Maharadscha  \n",
      "13                        14   14.                          Sapori di Casa  \n",
      "14                        15   15.                            Hasty Pastry  \n",
      "15                        16   16.                              Skykitchen  \n",
      "16                        17   17.                           100 Gramm Bar  \n",
      "17                        18   18.                                 Salamat  \n",
      "18                        19   19.                 Flamingo Fresh Food Bar  \n",
      "19                        20   20.  Vedang - plant burger (Mall of Berlin)  \n"
     ]
    }
   ],
   "source": [
    "print(\"Total Restaurants scraped:\\t\", len(scraped_df))\n",
    "\n",
    "print(\"Restaurants scraped:\\t\", scraped_df.head(20))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<font size=5 align='left'>\n",
    "  <p>\n",
    "    <img style=\"padding-right: 10px;padding-top: 0px;\" src='https://i.ibb.co/878wW18/resttt-fotor-bg-remover-20230315225922.png' width=\"40\" height=\"40\" align=\"left\">\n",
    "    <b>We are not done yet!</b>\n",
    "  </p>\n",
    "</font>\n",
    "<img src=\"https://i.ibb.co/6Zqt2wH/picture-abhi.jpg\" width = \"250\" height = \"250\"><br>\n",
    "<font size=3 align='left'>\n",
    "Let's do some Exploratory Data Analysis on the scraped data. We will try to plot the following analysis using Seaborn.<br>\n",
    "<ul>\n",
    "<b>\n",
    "<li><font color=#9E5EE7>Top 10 most popular cuisines in Berlin, Germany</font></li>\n",
    "<li><font color=#9E5EE7>Number of reviews vs. star rating of a restaurant in Berlin, Germany</font></li>\n",
    "</b>\n",
    "</ul><br>\n",
    "    The <font color = teal>clean_dataframe</font> function does the job of cleaning the scraped output dataframe, such as splitting the serial number from the restaurant name, dropping unnecessary columns, splitting cuisines (as they are concatenated in the same row with a comma), and removing noise from some columns.<br>\n",
    "    <font color = teal>clean_dataframe</font> function creates a bar plot of popular cuisines in Berlin using Seaborn.<br>\n",
    "    <font color = teal>scatter_plot_viz</font> function shows the best places to eat in Berlin by visualizing the relationship between rating and number of reviews. As depicted in the plot, we would prefer the restaurants with high ratings and a high number of reviews.<br>\n",
    "</font>\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                          title             cuisines reviews star rating  \\\n",
      "0       1. Cafe Couscous - Vege        Cafe, Healthy     133         5.0   \n",
      "1  2. Clubrestaurant am Wannsee     German, European     239         5.0   \n",
      "2                    3. happies     European, Fusion     391         5.0   \n",
      "3                     4. Haveli        Indian, Asian    1417         4.5   \n",
      "4                 5. Hackethals          German, Bar    1516         4.5   \n",
      "5                6. Ataya Caffe     Italian, Healthy     221         5.0   \n",
      "6          7. Ristorante A MANO     Italian, Seafood    2631         4.5   \n",
      "7     8. Restaurant Buschbeck's     German, European     351         5.0   \n",
      "8                9. Burger Turm  American, Fast food     882         5.0   \n",
      "9               10. Naveenapath        Indian, Asian     738         5.0   \n",
      "\n",
      "   restaurant serial number            restaurant_name cuisines_melt  \\\n",
      "0                         1       Cafe Couscous - Vege     cuisine_1   \n",
      "1                         2  Clubrestaurant am Wannsee     cuisine_1   \n",
      "2                         3                    happies     cuisine_1   \n",
      "3                         4                     Haveli     cuisine_1   \n",
      "4                         5                 Hackethals     cuisine_1   \n",
      "5                         6                Ataya Caffe     cuisine_1   \n",
      "6                         7          Ristorante A MANO     cuisine_1   \n",
      "7                         8     Restaurant Buschbeck's     cuisine_1   \n",
      "8                         9                Burger Turm     cuisine_1   \n",
      "9                        10                Naveenapath     cuisine_1   \n",
      "\n",
      "  cuisines_all  \n",
      "0         Cafe  \n",
      "1       German  \n",
      "2     European  \n",
      "3       Indian  \n",
      "4       German  \n",
      "5      Italian  \n",
      "6      Italian  \n",
      "7       German  \n",
      "8     American  \n",
      "9       Indian  \n",
      "   total_restaurants_offering_cuisines       cuisines\n",
      "0                                  932        Italian\n",
      "1                                  880         German\n",
      "2                                  719       European\n",
      "3                                  648          Asian\n",
      "4                                  437  Mediterranean\n",
      "5                                  421           Cafe\n",
      "6                                  362          Pizza\n",
      "7                                  347            Bar\n",
      "8                                  286     Vietnamese\n",
      "9                                  268      Fast food\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "             restaurant_name  reviews  star rating\n",
      "0       Cafe Couscous - Vege    133.0          5.0\n",
      "1  Clubrestaurant am Wannsee    239.0          5.0\n",
      "2                    happies    391.0          5.0\n",
      "3                     Haveli   1417.0          4.5\n",
      "4                 Hackethals   1516.0          4.5\n",
      "5                Ataya Caffe    221.0          5.0\n",
      "6          Ristorante A MANO   2631.0          4.5\n",
      "7     Restaurant Buschbeck's    351.0          5.0\n",
      "8                Burger Turm    882.0          5.0\n",
      "9                Naveenapath    738.0          5.0\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import warnings\n",
    "\n",
    "warnings.filterwarnings(\"ignore\")\n",
    "plt.rcParams['figure.figsize'] = [12, 8]\n",
    "\n",
    "# Take the input as the scraped output\n",
    "ta_restaurants = scraped_df\n",
    "\n",
    "def clean_dataframe(df):\n",
    "    # clean title, split sr_no and name\n",
    "    df[['sr_no','restaurant_name']] = df[\"title\"].str.split(\" \", 1, expand=True)\n",
    "    df[\"restaurant_name\"] = df[\"restaurant_name\"].str.strip(\" \")\n",
    "\n",
    "    # drop unnecessary columns\n",
    "    df = df.drop('sr_no', axis=1)\n",
    "    df = df.drop('page number', axis=1)\n",
    "    df = df.drop('data offset', axis=1)\n",
    "\n",
    "    # split cuisines\n",
    "    df[['cuisine_1','cuisine_2']] = df[\"cuisines\"].str.split(\",\", expand=True)\n",
    "    df = df.melt(id_vars=[\"title\", \"cuisines\", \"reviews\", \"star rating\", \"restaurant serial number\", \"restaurant_name\"], \n",
    "        var_name=\"cuisines_melt\",\n",
    "        value_name=\"cuisines_all\"\n",
    "        )\n",
    "    # clean columns\n",
    "    df[\"reviews\"] = df[\"reviews\"].str.replace('reviews', '').str.replace('review', '').str.replace(',', '').str.strip(\" \")\n",
    "    df[\"star rating\"] = df[\"star rating\"].str.replace(' of 5 bubbles', '').str.strip(\" \")\n",
    "    df[\"cuisines_all\"] = df[\"cuisines_all\"].str.replace('₹', '').str.replace('₹₹ - ₹₹₹', '').str.replace('-', '').str.strip(\" \")\n",
    "    return df\n",
    "\n",
    "def popular_cuisines(df):\n",
    "    # df_popular_cuisines = df.where(df['cuisines_all'] != '').groupby(['cuisines_all'])['restaurant_name'].nunique().sort_values(ascending=False).head(20)\n",
    "    df_popular_cuisines = df.where(df['cuisines_all'] != '').groupby(\"cuisines_all\").agg(total_restaurants_offering_cuisines=('restaurant_name', 'nunique'))\n",
    "    df_popular_cuisines = df_popular_cuisines.sort_values(by = [\"total_restaurants_offering_cuisines\"],ascending=False).head(10)\n",
    "    df_popular_cuisines['cuisines'] = df_popular_cuisines.index\n",
    "    df_popular_cuisines = df_popular_cuisines.reset_index(drop=True)\n",
    "    print(df_popular_cuisines.head(10))\n",
    "    ax_1 = sns.barplot(data=df_popular_cuisines, x=\"cuisines\", y=\"total_restaurants_offering_cuisines\")\n",
    "    ax_1.set_title('Popular Cuisines in Berlin, Germany', size = 14, font = 'sans', fontweight='bold')\n",
    "    ax_1.set_xlabel(\"Cuisines\", size = 10, font = 'sans', fontweight='bold')\n",
    "    ax_1.set_ylabel(\"Total Restaurants Offering Cuisines\", size = 10, font = 'sans', fontweight='bold')\n",
    "    for i in ax_1.containers:\n",
    "        ax_1.bar_label(i,)\n",
    "    plt.show()\n",
    "    return df_popular_cuisines\n",
    "\n",
    "def scatter_plot_viz(df):\n",
    "    df_subset = df[[\"restaurant_name\",\"reviews\",\"star rating\"]]\n",
    "    df_subset = df_subset.drop_duplicates()\n",
    "    df_subset['reviews'] = df_subset['reviews'].fillna(0).astype(int)\n",
    "    df_subset['star rating'] = df_subset['star rating'].fillna(0).astype(float)\n",
    "    df_subset = df_subset.where(df_subset['star rating'] != -1.0)\n",
    "    print(df_subset.head(10))\n",
    "    sns.set( style = \"whitegrid\" ) \n",
    "    ax = sns.scatterplot(x=\"reviews\",\n",
    "                        y=\"star rating\",\n",
    "                        data=df_subset.sort_values(\"reviews\", ascending= False),\n",
    "                        style=\"star rating\",\n",
    "                        hue =\"star rating\",\n",
    "                        palette = 'tab10'\n",
    "                        )\n",
    "    ax.set_title('Restaurant Rating v/s No. of Reviews', size = 14, font = 'sans', fontweight='bold')\n",
    "    ax.set_xlabel(\"Reviews\", size = 10, font = 'sans', fontweight='bold')\n",
    "    ax.set_ylabel(\"Ratings\", size = 10, font = 'sans', fontweight='bold')\n",
    "    plt.grid(color = 'grey', linestyle = '--', linewidth = 0.5)\n",
    "    ax.set(xscale=\"linear\")\n",
    "    ax.set(xlim = (100,8000))\n",
    "    ax.set(ylim = (1,6))\n",
    "    plt.show()\n",
    "\n",
    "ta_restaurants_clean = clean_dataframe(ta_restaurants)\n",
    "print(ta_restaurants_clean.head(10))\n",
    "popular_cuisines_df = popular_cuisines(ta_restaurants_clean)\n",
    "scatter_plot_viz(ta_restaurants_clean)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Summary:\n",
    "\n",
    "<ul>\n",
    "    <li><font size = 3 color=green>Inspect the structure of the website to understand the HTML tags and attributes</font></li>\n",
    "    <li><font size = 3 color=green>Use Python's requests library to download the HTML content of the website</font></li>\n",
    "    <li><font size = 3 color=green>Use BeautifulSoup library to parse the HTML content and extract the relevant data</font></li>\n",
    "    <li><font size = 3 color=green>Store the collected data in a structured format such as CSV</font></li>\n",
    "    <li><font size = 3 color=green>Performed EDA on scraped data</font></li>\n",
    "</ul>\n",
    "\n",
    "### References:\n",
    "\n",
    "<ul>\n",
    "    <li><font size = 3 color=green>Python Requests Library: https://docs.python-requests.org/en/latest/</font></li>\n",
    "    <li><font size = 3 color=green>BeautifulSoup Library: https://www.crummy.com/software/BeautifulSoup/bs4/doc/</font></li>\n",
    "</ul>\n",
    "\n",
    "### Future Work:\n",
    "\n",
    "<ul>\n",
    "    <li><font size = 3 color=green>Automate the scraping process to collect data periodically for monitoring changes in restaurant information</font></li>\n",
    "    <li><font size = 3 color=green>Use natural language processing techniques to analyze restaurant reviews and ratings to generate insights about customer sentiment</font></li>\n",
    "    <li><font size = 3 color=green>Develop a recommendation system for restaurants based on user preferences and restaurant attributes.</font></li>\n",
    "</ul>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<img src=\"https://i.ibb.co/X4kK80j/thats-all.jpg\" width = \"400\" height = \"400\" align = \"center\">"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "window.require && require([\"base/js/namespace\"],function(Jupyter){Jupyter.notebook.save_checkpoint()})"
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[jovian] Updating notebook \"anshikanigam1493/scraping-restaurant-details-from-tripadvisor\" on https://jovian.com\u001b[0m\n",
      "[jovian] Committed successfully! https://jovian.com/anshikanigam1493/scraping-restaurant-details-from-tripadvisor\u001b[0m\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "'https://jovian.com/anshikanigam1493/scraping-restaurant-details-from-tripadvisor'"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import jovian\n",
    "jovian.commit(filename='scraping-all-restaurant-listings-for-a-given-city-from-TripAdvisor-using-Python')"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}