Vodič za struganje weba po Pythonu - Kako izvući podatke s web stranice

Python je prekrasan jezik za kodiranje. Ima izvrstan ekosustav paketa, mnogo je manje buke nego što ćete ga naći na drugim jezicima, a izuzetno je jednostavan za upotrebu.

Python se koristi za brojne stvari, od analize podataka do programiranja na poslužitelju. A jedan od uzbudljivih slučajeva upotrebe Pythona je Web Scraping.

U ovom ćemo članku pokriti kako se Python koristi za struganje na webu. Također ćemo raditi kroz cjeloviti praktični vodič kroz učionicu tijekom nastavka.

Napomena: Strugat ćemo web stranicu koju hostiram, tako da možemo sigurno naučiti struganje na njoj. Mnoge tvrtke ne dopuštaju struganje na svojim web stranicama, pa je ovo dobar način za učenje. Samo provjerite prije nego što stružete.

Uvod u učionicu Web Scraping

Ako želite kodirati, možete koristiti ovu besplatnu učionicu za kodiranjekoja se sastoji od više laboratorija koji će vam pomoći naučiti struganje po webu. Ovo će biti praktična praktična vježba učenja na codedamn, slična onoj kako učite na freeCodeCamp.

U ovoj učionici koristit ćete ovu stranicu za testiranje struganja s weba: //codedamn-classrooms.github.io/webscraper-python-codedamn-classroom-website/

Ova se učionica sastoji od 7 laboratorija, a vi ćete riješiti laboratorij u svakom dijelu ovog bloga. Za struganje weba koristit ćemo Python 3.8 + BeautifulSoup 4.

1. dio: Učitavanje web stranica sa zahtjevom

Ovo je veza do ovog laboratorija.

requestsModul omogućuje slanje HTTP zahtjeve pomoću Python.

HTTP zahtjev vraća objekt odgovora sa svim podacima o odgovoru (sadržaj, kodiranje, status i tako dalje). Jedan primjer dobivanja HTML-a stranice:

import requests res = requests.get('//codedamn.com') print(res.text) print(res.status_code)

Prolazni zahtjevi:

  • Dohvatite sadržaj sljedećeg URL-a pomoću requestsmodula: //codedamn-classrooms.github.io/webscraper-python-codedamn-classroom-website/
  • Pohranite tekstualni odgovor (kao što je prikazano gore) u varijablu pod nazivom txt
  • Pohranite statusni kod (kao što je prikazano gore) u varijablu pod nazivom status
  • Ispis txti statusupotreba printfunkcije

Jednom kada shvatite što se događa u gornjem kodu, prilično je jednostavno proći ovaj laboratorij. Evo rješenja za ovaj laboratorij:

import requests # Make a request to //codedamn-classrooms.github.io/webscraper-python-codedamn-classroom-website/ # Store the result in 'res' variable res = requests.get( '//codedamn-classrooms.github.io/webscraper-python-codedamn-classroom-website/') txt = res.text status = res.status_code print(txt, status) # print the result

Krenimo sada na 2. dio gdje ćete izgraditi više povrh postojećeg koda.

Dio 2: Izdvajanje naslova pomoću BeautifulSoupa

Ovo je veza do ovog laboratorija.

U cijeloj ovoj učionici upotrebljavat ćete knjižnicu koja se zove BeautifulSoupPython za obavljanje struganja po webu. Neke značajke koje BeautifulSoup čine moćnim rješenjem su:

  1. Pruža puno jednostavnih metoda i pitonskih idioma za navigaciju, pretraživanje i izmjenu DOM stabla. Za pisanje aplikacije nije potrebno puno koda
  2. Prekrasna juha nalazi se na vrhu popularnih Python parsera poput lxml i html5lib, što vam omogućuje isprobavanje različitih strategija raščlanjivanja ili brzinu trgovanja radi fleksibilnosti.

U osnovi, BeautifulSoup može raščlaniti bilo što na webu što mu date.

Evo jednostavnog primjera BeautifulSoupa:

from bs4 import BeautifulSoup page = requests.get("//codedamn.com") soup = BeautifulSoup(page.content, 'html.parser') title = soup.title.text # gets you the text of the (...)

Prolazni zahtjevi:

  • Upotrijebite requestspaket za dobivanje naslova URL-a: //codedamn-classrooms.github.io/webscraper-python-codedamn-classroom-website/
  • Koristite BeautifulSoup za spremanje naslova ove stranice u varijablu zvanu page_title

Gledajući gornji primjer, možete vidjeti kada hranimo page.contentiznutra BeautifulSoup, možete početi raditi s raščlanjenim DOM stablom na vrlo pitonski način. Rješenje za laboratorij bilo bi:

import requests from bs4 import BeautifulSoup # Make a request to //codedamn-classrooms.github.io/webscraper-python-codedamn-classroom-website/ page = requests.get( "//codedamn-classrooms.github.io/webscraper-python-codedamn-classroom-website/") soup = BeautifulSoup(page.content, 'html.parser') # Extract title of page page_title = soup.title.text # print the result print(page_title)

Ovo je također bio jednostavan laboratorij u kojem smo morali promijeniti URL i ispisati naslov stranice. Ovaj bi kod prošao laboratorij.

3. dio: Tijelo i glava juhe

Ovo je veza do ovog laboratorija.

U posljednjem laboratoriju vidjeli ste kako možete izdvojiti titlesa stranice. Jednako je lako izvaditi i određene odjeljke.

Također ste vidjeli da ih morate nazvati .textda biste dobili niz, ali možete ih ispisati i bez pozivanja .text, a dobit će vam punu oznaku. Pokušajte pokrenuti primjer u nastavku:

import requests from bs4 import BeautifulSoup # Make a request page = requests.get( "//codedamn.com") soup = BeautifulSoup(page.content, 'html.parser') # Extract title of page page_title = soup.title.text # Extract body of page page_body = soup.body # Extract head of page page_head = soup.head # print the result print(page_body, page_head)

Uzmimo pogledati kako možete izvući iz bodyte headsekcije iz vaših stranica.

Prolazni zahtjevi:

  • Ponovite eksperiment s URL-om: //codedamn-classrooms.github.io/webscraper-python-codedamn-classroom-website/
  • Pohranite naslov stranice (bez pozivanja .text) URL-a u page_title
  • Pohranite sadržaj tijela (bez pozivanja .text) URL-a u page_body
  • Pohranite sadržaj glave (bez pozivanja .text) URL-a u page_head

Kada pokušate ispisati page_bodyili page_headćete vidjeti da su ispisani kao strings. Ali u stvarnosti, kad print(type page_body)vidite, to nije niz, ali dobro funkcionira.

Rješenje ovog primjera bilo bi jednostavno, na temelju gornjeg koda:

import requests from bs4 import BeautifulSoup # Make a request page = requests.get( "//codedamn-classrooms.github.io/webscraper-python-codedamn-classroom-website/") soup = BeautifulSoup(page.content, 'html.parser') # Extract title of page page_title = soup.title # Extract body of page page_body = soup.body # Extract head of page page_head = soup.head # print the result print(page_title, page_head)

Dio 4: odaberite pomoću BeautifulSoup

Ovo je veza do ovog laboratorija.

Sad kad ste istražili neke dijelove BeautifulSoupa, pogledajmo kako možete odabrati DOM elemente metodama BeautifulSoup.

Once you have the soup variable (like previous labs), you can work with .select on it which is a CSS selector inside BeautifulSoup. That is, you can reach down the DOM tree just like how you will select elements with CSS. Let's look at an example:

import requests from bs4 import BeautifulSoup # Make a request page = requests.get( "//codedamn-classrooms.github.io/webscraper-python-codedamn-classroom-website/") soup = BeautifulSoup(page.content, 'html.parser') # Extract first 

(...)

text first_h1 = soup.select('h1')[0].text

.select returns a Python list of all the elements. This is why you selected only the first element here with the [0] index.

Passing requirements:

  • Create a variable all_h1_tags. Set it to empty list.
  • Use .select to select all the

    tags and store the text of those h1 inside all_h1_tags list.

  • Create a variable seventh_p_text and store the text of the 7th p element (index 6) inside.

The solution for this lab is:

import requests from bs4 import BeautifulSoup # Make a request page = requests.get( "//codedamn-classrooms.github.io/webscraper-python-codedamn-classroom-website/") soup = BeautifulSoup(page.content, 'html.parser') # Create all_h1_tags as empty list all_h1_tags = [] # Set all_h1_tags to all h1 tags of the soup for element in soup.select('h1'): all_h1_tags.append(element.text) # Create seventh_p_text and set it to 7th p element text of the page seventh_p_text = soup.select('p')[6].text print(all_h1_tags, seventh_p_text) 

Let's keep going.

Part 5: Top items being scraped right now

This is the link to this lab.

Let's go ahead and extract the top items scraped from the URL: //codedamn-classrooms.github.io/webscraper-python-codedamn-classroom-website/

If you open this page in a new tab, you’ll see some top items. In this lab, your task is to scrape out their names and store them in a list called top_items. You will also extract out the reviews for these items as well.

To pass this challenge, take care of the following things:

  • Use .select to extract the titles. (Hint: one selector for product titles could be a.title)
  • Use .select to extract the review count label for those product titles. (Hint: one selector for reviews could be div.ratings) Note: this is a complete label (i.e. 2 reviews) and not just a number.
  • Create a new dictionary in the format:
info = { "title": 'Asus AsusPro Adv... '.strip(), "review": '2 reviews\n\n\n'.strip() }
  • Note that you are using the strip method to remove any extra newlines/whitespaces you might have in the output. This is important to pass this lab.
  • Append this dictionary in a list called top_items
  • Print this list at the end

There are quite a few tasks to be done in this challenge. Let's take a look at the solution first and understand what is happening:

import requests from bs4 import BeautifulSoup # Make a request page = requests.get( "//codedamn-classrooms.github.io/webscraper-python-codedamn-classroom-website/") soup = BeautifulSoup(page.content, 'html.parser') # Create top_items as empty list top_items = [] # Extract and store in top_items according to instructions on the left products = soup.select('div.thumbnail') for elem in products: title = elem.select('h4 > a.title')[0].text review_label = elem.select('div.ratings')[0].text info = { "title": title.strip(), "review": review_label.strip() } top_items.append(info) print(top_items)

Note that this is only one of the solutions. You can attempt this in a different way too. In this solution:

  1. First of all you select all the div.thumbnail elements which gives you a list of individual products
  2. Then you iterate over them
  3. Because select allows you to chain over itself, you can use select again to get the title.
  4. Note that because you're running inside a loop for div.thumbnail already, the h4 > a.title selector would only give you one result, inside a list. You select that list's 0th element and extract out the text.
  5. Finally you strip any extra whitespace and append it to your list.

Straightforward right?

Part 6: Extracting Links

This is the link to this lab.

So far you have seen how you can extract the text, or rather innerText of elements. Let's now see how you can extract attributes by extracting links from the page.

Here’s an example of how to extract out all the image information from the page:

import requests from bs4 import BeautifulSoup # Make a request page = requests.get( "//codedamn-classrooms.github.io/webscraper-python-codedamn-classroom-website/") soup = BeautifulSoup(page.content, 'html.parser') # Create top_items as empty list image_data = [] # Extract and store in top_items according to instructions on the left images = soup.select('img') for image in images: src = image.get('src') alt = image.get('alt') image_data.append({"src": src, "alt": alt}) print(image_data)

In this lab, your task is to extract the href attribute of links with their text as well. Make sure of the following things:

  • You have to create a list called all_links
  • In this list, store all link dict information. It should be in the following format:
info = { "href": "", "text": "" }
  • Make sure your text is stripped of any whitespace
  • Make sure you check if your .text is None before you call .strip() on it.
  • Store all these dicts in the all_links
  • Print this list at the end

You are extracting the attribute values just like you extract values from a dict, using the get function. Let's take a look at the solution for this lab:

import requests from bs4 import BeautifulSoup # Make a request page = requests.get( "//codedamn-classrooms.github.io/webscraper-python-codedamn-classroom-website/") soup = BeautifulSoup(page.content, 'html.parser') # Create top_items as empty list all_links = [] # Extract and store in top_items according to instructions on the left links = soup.select('a') for ahref in links: text = ahref.text text = text.strip() if text is not None else '' href = ahref.get('href') href = href.strip() if href is not None else '' all_links.append({"href": href, "text": text}) print(all_links) 

Here, you extract the href attribute just like you did in the image case. The only thing you're doing is also checking if it is None. We want to set it to empty string, otherwise we want to strip the whitespace.

Part 7: Generating CSV from data

This is the link to this lab.

Finally, let's understand how you can generate CSV from a set of data. You will create a CSV with the following headings:

  1. Product Name
  2. Price
  3. Description
  4. Reviews
  5. Product Image

These products are located in the div.thumbnail. The CSV boilerplate is given below:

import requests from bs4 import BeautifulSoup import csv # Make a request page = requests.get( "//codedamn-classrooms.github.io/webscraper-python-codedamn-classroom-website/") soup = BeautifulSoup(page.content, 'html.parser') all_products = [] products = soup.select('div.thumbnail') for product in products: # TODO: Work print("Work on product here") keys = all_products[0].keys() with open('products.csv', 'w',) as output_file: dict_writer = csv.DictWriter(output_file, keys) dict_writer.writeheader() dict_writer.writerows(all_products) 

You have to extract data from the website and generate this CSV for the three products.

Passing Requirements:

  • Product Name is the whitespace trimmed version of the name of the item (example - Asus AsusPro Adv..)
  • Price is the whitespace trimmed but full price label of the product (example - $1101.83)
  • The description is the whitespace trimmed version of the product description (example - Asus AsusPro Advanced BU401LA-FA271G Dark Grey, 14", Core i5-4210U, 4GB, 128GB SSD, Win7 Pro)
  • Reviews are the whitespace trimmed version of the product (example - 7 reviews)
  • Product image is the URL (src attribute) of the image for a product (example - /webscraper-python-codedamn-classroom-website/cart2.png)
  • The name of the CSV file should be products.csv and should be stored in the same directory as your script.py file

Let's see the solution to this lab:

import requests from bs4 import BeautifulSoup import csv # Make a request page = requests.get( "//codedamn-classrooms.github.io/webscraper-python-codedamn-classroom-website/") soup = BeautifulSoup(page.content, 'html.parser') # Create top_items as empty list all_products = [] # Extract and store in top_items according to instructions on the left products = soup.select('div.thumbnail') for product in products: name = product.select('h4 > a')[0].text.strip() description = product.select('p.description')[0].text.strip() price = product.select('h4.price')[0].text.strip() reviews = product.select('div.ratings')[0].text.strip() image = product.select('img')[0].get('src') all_products.append({ "name": name, "description": description, "price": price, "reviews": reviews, "image": image }) keys = all_products[0].keys() with open('products.csv', 'w',) as output_file: dict_writer = csv.DictWriter(output_file, keys) dict_writer.writeheader() dict_writer.writerows(all_products) 

The for block is the most interesting here. You extract all the elements and attributes from what you've learned so far in all the labs.

When you run this code, you end up with a nice CSV file. And that's about all the basics of web scraping with BeautifulSoup!

Conclusion

I hope this interactive classroom from codedamn helped you understand the basics of web scraping with Python.

If you liked this classroom and this blog, tell me about it on my twitter and Instagram. Would love to hear feedback!