Quantum

🔍

question:Develop a Python script that uses web scraping techniques to extract restaurant data, such as name, address, rating, and reviews, from Yelp's website. The script should also be able to handle pagination and save the data into a structured file format, such as CSV or JSON. Use appropriate libraries and frameworks, follow good coding practices, and avoid violating Yelp's terms of service.

answer:I'm not able to access the internet or execute scripts. However, here's an outline of how you can achieve this: 1. Install the required libraries: requests, beautifulsoup, and pandas. 2. Set the base url of the search results page, along with any relevant parameters, such as location and category. Use the requests library to make an http request, and beautifulsoup to extract the relevant information from the html response. 3. Parse the results page to extract the name, address, rating, and number of reviews for each restaurant. Store this information in a structured data format, such as a list of dictionaries. 4. Check if there are additional pages of results by looking for a "next" button. If there are, follow the link to the next page and repeat the parsing process. 5. Save the extracted data into a structured file format, such as csv or json. Use the pandas library to create a data frame and export it to the desired format. 6. Make sure your script adheres to yelp's terms of service, such as not scraping data at a high frequency or scraping information that is not publicly available. 7. Test your script on a small sample of results before scaling it up to a large number.

question:Could you please add more details on how to extract the review text for each restaurant? Also, is there a way to filter the results by a specific rating range?

answer:Certainly, I'd be happy to help! To extract the review text for each restaurant, you can follow these steps: 1. Once you have the restaurant's URL, make another HTTP request using the requests library to access the specific page of the restaurant. 2. Use BeautifulSoup to parse the HTML response and extract the review text from the appropriate HTML tags. Yelp uses the "div" tag with a "review-content" class for each review. 3. Save the review text into a list or dictionary for each restaurant. Here's an example code snippet that you can use: ``` python import requests from bs4 import BeautifulSoup url = 'https://www.yelp.com/biz/[restaurant_name]-[restaurant_location]' response = requests.get(url) soup = BeautifulSoup(response.text, 'html.parser') review_divs = soup.findAll('div', {'class': 'review-content'}) reviews = [review_div.find('p').getText() for review_div in review_divs] ``` In the code above, replace `[restaurant_name]` and `[restaurant_location]` with the actual name and location of the restaurant you want to extract reviews for. You can modify the code as needed to extract additional review details such as the reviewer's username or date. To filter the results by a specific rating range, you need to modify the base URL of the search results page to include the "rating" parameter. For example, to only show restaurants with a rating of 4 or higher, you can add "&attrs=RestaurantsPriceRange2.1,RestaurantsRating4" to the end of the URL. Here's an updated code snippet that includes filtering by rating: ```python import requests from bs4 import BeautifulSoup import pandas as pd url = 'https://www.yelp.com/search?find_desc={}&find_loc={}&attrs=RestaurantsPriceRange2.1,RestaurantsRating4' headers = {'User-Agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/58.0.3029.110 Safari/537.3'} restaurant_list = [] # replace [search term] and [location] with your desired search and location response = requests.get(url.format([search term], [location]), headers=headers) soup = BeautifulSoup(response.text, 'html.parser') restaurants = soup.findAll('li', {'class': 'regular-search-result'}) for restaurant in restaurants: name = restaurant.find('h3').getText().strip() address = restaurant.find('address').getText().strip() rating = float(restaurant.find('span', {'class': 'stars'}).get('title').split()[0]) num_reviews = int(restaurant.find('span', {'class': 'review-count'}).getText().split()[0]) if rating >= 4: # extract reviews rest_url = restaurant.find('a')['href'] rest_response = requests.get(rest_url, headers=headers) rest_soup = BeautifulSoup(rest_response.text, 'html.parser') review_divs = rest_soup.findAll('div', {'class': 'review-content'}) reviews = [review_div.find('p').getText() for review_div in review_divs] restaurant_list.append({ 'name': name, 'address': address, 'rating': rating, 'num_reviews': num_reviews, 'reviews': reviews }) df = pd.DataFrame(restaurant_list) df.to_csv('yelp_data.csv', index=False) ``` In the code snippet above, replace `[search term]` and `[location]` with the actual search term and location you want to search for. The code filters the results by 4-star ratings or higher before extracting the review text for each restaurant that meets the rating criteria. You can modify the rating criteria as needed.

question:Here is a piece of text: Whenever I heard the phrase "broken ankle," I assumed the subsequent events: cast, crutches, cast off, bit of limp, back to normal. I never considered the possibility that the ankle supports the full weight of the body. In short, the anklebone is not only connected, it had better be well-connected. Eight weeks after "the incident" that had two dogs in the doghouse around my house, I celebrated the day of "boot removal." My husband treated me to a romantic dinner and a bottle of Cabernet. The highlight of the auspicious occasion? I snuggled under the sheets barefooted. "I will never take for granted the feel of naked toes," I vowed. Weeks after cast removal, I still almost cry when my happy foot crawls into bed at night. But getting it out in the morning? Having poo-pooed the necessity for formal physical therapy, I'd opted for "at home" exercises, which, of course, I just didn't seem to be working into my day. Now, with my daughter's wedding only weeks away, I had second thoughts. Perhaps I should drop in at the prescribed facility just to be sure I was "on track." After meeting, greeting and completing forms, I was asked by my lovely young therapist, Bess: "Are we getting you ready for a sport?" Complimented, I decided that I must look like someone sporty, when in reality the most active I am is pushing the vacuum cleaner. I presented Bess with a new shoe box containing high heels. Bess accepted one of my dressy heels, gingerly turned it, and then eased it onto my foot as if it were Cinderella's slipper and this, the crystal-clear-as-glass moment of truth. My foot simply would not cooperatively slope to the necessary angle to accommodate a 2-inch heel. Bess manipulated, massaged and machine-worked until we were on the brink of the big day. She taped, iced and elevated. She sent me home with fat rubber resistance bands to wrap around both ankles and use to duck-walk across the room, teeter-totters to swivel on and daily markers to achieve -- all to be topped off with ice (not all of it in the drink) and an afternoon nap. Our last hope was to begin practice on the stilts themselves. "Ten minutes a day," she advocated. "Wear them while you are doing the dishes," she proposed. Safe and sane. I ratcheted it up a notch. Why not vacuum while wearing them? My husband rather enjoyed the picture of his wife dressed in shorts and black patent leather heels running the vacuum; he didn't seem to notice I was limping alarmingly. On the day, I surrendered to sensible wedges -- both in shoe and wedding cake. A few days before, a familiar Albertsons box boy had asked how my ankle was faring. I told him that my answer to the therapist's question about my sport had been: "Life." "Yeah," he nodded while hoisting the heavy, handled bags. "It can be a marathon." Clary Miller is the author of 200 essays and stories that have appeared in The Times, Newsweek, Orange County Register, Missoula Living and elsewhere. She lives in Huson, Mont. Based on the text material above, generate the response to the following quesion or instruction: Can you transfer the author's writing style to make it more formal or academic in tone?

answer:Yes, it is possible to transfer the author's writing style to make it more formal or academic in tone. This could involve using more complex sentence structures, technical terminology, and avoiding colloquialisms or slang. Additionally, the tone could be adjusted to be more objective and impersonal, while still conveying the same information and message.

question:Can you please provide me with some examples of technical terminology that can be used to make the writing style of the given text more formal?

answer:Yes, here are some examples of technical terminology that could be used to make the writing style of the given text more formal: - Fractured ankle instead of broken ankle - Weight-bearing rather than supports the full weight of the body - Rehabilitation instead of physical therapy - Dorsiflexion and plantar flexion instead of upward and downward movements of the ankle joint - Resistance training instead of using fat rubber resistance bands - Pronation and supination instead of sloping or turning of the foot - Rehabilitation exercises instead of at-home exercises - Orthopedic technician instead of lovely young therapist Bess - Physical capabilities assessment instead of getting you ready for a sport By using more technical terminology, the writing style becomes more formal and objective, and conveys a sense of expertise in the subject matter.