A Python egy gyönyörű nyelv, amellyel be lehet kódolni. Remek csomag-ökoszisztémával rendelkezik, sokkal kevesebb a zaj, mint más nyelveken, és nagyon egyszerűen használható.

A Pythont számos dologra használják, az adatok elemzésétől a szerver programozásáig. A Python egyik izgalmas felhasználási esete a Web Scraping.

Ebben a cikkben kitérünk arra, hogyan használhatjuk a Python-ot webes lekaparáshoz. A továbbiakban egy komplett gyakorlati tantermi útmutatón is dolgozunk.

Megjegyzés: Egy olyan weboldalt kaparunk, amelyet én üzemeltetek, így biztonságosan megtanulhatjuk a kaparást. Sok vállalat nem engedélyezi a weboldalak kaparását, így ez jó módszer a tanulásra. Csak feltétlenül ellenőrizze, mielőtt kaparna.

Bevezetés a webkaparós osztályterembe

Ha tovább akar kódolni, használhatja ezt az ingyenes codedamn tantermettöbb laborból áll, amelyek segítenek megtanulni a webes kaparást. Ez egy gyakorlati gyakorlati gyakorlat lesz a codedamn-on, hasonlóan a freeCodeCamp-on való tanuláshoz.

Ebben a tanteremben ezt az oldalt fogja használni a webes kaparás tesztelésére: //codedamn-classrooms.github.io/webscraper-python-codedamn-classroom-website/

Ez a tanterem 7 laborból áll, és a blogbejegyzés minden részében megold egy laboratóriumot. A webes lekaparáshoz Python 3.8 + BeautifulSoup 4-et fogunk használni.

1. rész: Weboldalak betöltése „kéréssel”

Ez a link ehhez a laboratóriumhoz.

A requestsmodul lehetővé teszi HTTP kérések küldését Python használatával.

A HTTP-kérés egy Válaszobjektumot ad vissza az összes válaszadattal (tartalom, kódolás, állapot stb.). Egy példa az oldal HTML-kódjának megszerzésére:

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

Sikeres követelmények:

• Szerezze be a következő URL tartalmát a requestsmodul használatával : //codedamn-classrooms.github.io/webscraper-python-codedamn-classroom-website/
• Tárolja a szöveges választ (a fentiek szerint) az úgynevezett változóban txt
• Tárolja az állapotkódot (a fent látható módon) az úgynevezett változóba status
• Nyomtatás txtés statusa printfunkció használata

Miután megértette, mi történik a fenti kódban, meglehetősen egyszerű átadni ezt a laboratóriumot. Íme a megoldás erre a laborra:

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

Most térjünk át a 2. részre, ahol többet épít a meglévő kód tetejére.

2. rész: Cím kibontása a BeautifulSoup segítségével

Ez a link ehhez a laboratóriumhoz.

Ebben az egész tanteremben egy BeautifulSoupPythonban meghívott könyvtárat fog használni a webes kaparáshoz. Néhány olyan funkció, amely a BeautifulSoup-ot hatékony megoldássá teszi:

1. Nagyon sok egyszerű módszert és Python-féle idiómát kínál a DOM-fában való navigáláshoz, kereséshez és módosításhoz. Az alkalmazás megírásához nem kell sok kód
2. A Gyönyörű leves a népszerű Python-elemzők, például az lxml és a html5lib tetején ül, lehetővé téve a különféle elemzési stratégiák vagy a kereskedelem sebességének kipróbálását a rugalmasság érdekében.

Alapvetően a BeautifulSoup elemezhet bármit az Ön által megadott weben.

Itt található a BeautifulSoup egyszerű példája:

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 (...)

Sikeres követelmények:

• Használja a requestscsomagot az URL címének megszerzéséhez: //codedamn-classrooms.github.io/webscraper-python-codedamn-classroom-website/
• A BeautifulSoup segítségével tárolja az oldal címét az úgynevezett változóba page_title

Looking at the example above, you can see once we feed the page.content inside BeautifulSoup, you can start working with the parsed DOM tree in a very pythonic way. The solution for the lab would be:

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)

This was also a simple lab where we had to change the URL and print the page title. This code would pass the lab.

Part 3: Soup-ed body and head

This is the link to this lab.

In the last lab, you saw how you can extract the title from the page. It is equally easy to extract out certain sections too.

You also saw that you have to call .text on these to get the string, but you can print them without calling .text too, and it will give you the full markup. Try to run the example below:

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)

Let's take a look at how you can extract out body and head sections from your pages.

Passing requirements:

• Repeat the experiment with URL: //codedamn-classrooms.github.io/webscraper-python-codedamn-classroom-website/
• Store page title (without calling .text) of URL in page_title
• Store body content (without calling .text) of URL in page_body
• Store head content (without calling .text) of URL in page_head

When you try to print the page_body or page_head you'll see that those are printed as strings. But in reality, when you print(type page_body) you'll see it is not a string but it works fine.

The solution of this example would be simple, based on the code above:

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)

Part 4: select with BeautifulSoup

This is the link to this lab.

Now that you have explored some parts of BeautifulSoup, let's look how you can select DOM elements with BeautifulSoup methods.

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!