WebスクレイピングPythonチュートリアル–Webサイトからデータをスクレイピングする方法

Pythonはコーディングに適した言語です。優れたパッケージエコシステムを備えており、他の言語よりもノイズが少なく、非常に使いやすい言語です。

Pythonは、データ分析からサーバープログラミングまで、さまざまな目的で使用されます。そして、Pythonのエキサイティングなユースケースの1つは、Webスクレイピングです。

この記事では、Pythonを使用してWebスクレイピングを行う方法について説明します。また、進行中に、完全なハンズオンクラスルームガイドを実行します。

注:私がホストしているウェブページをスクレイピングするので、安全にスクレイピングを学ぶことができます。多くの企業は自社のWebサイトでのスクレイピングを許可していないため、これは学ぶための良い方法です。擦る前に必ず確認してください。

Webスクレイピング教室の紹介

一緒にコーディングしたい場合は、この無料のcodedamn教室を使用できますこれは、Webスクレイピングの学習に役立つ複数のラボで構成されています。これは、freeCodeCampで学習する方法と同様に、codedamnでの実践的な学習演習になります。

この教室では、次のページを使用してWebスクレイピングをテストします。//codedamn-classrooms.github.io/webscraper-python-codedamn-classroom-website/

このクラスルームは7つのラボで構成されており、このブログ投稿の各部分でラボを解決します。WebスクレイピングにはPython3.8 + BeautifulSoup4を使用します。

パート1:「リクエスト」を使用してWebページをロードする

これは、このラボへのリンクです。

このrequestsモジュールを使用すると、Pythonを使用してHTTPリクエストを送信できます。

HTTPリクエストは、すべての応答データ(コンテンツ、エンコーディング、ステータスなど)を含む応答オブジェクトを返します。ページのHTMLを取得する1つの例:

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

合格要件:

  • requestsモジュールを使用して次のURLのコンテンツを取得します://codedamn-classrooms.github.io/webscraper-python-codedamn-classroom-website/
  • (上記のように)テキスト応答をという変数に格納します txt
  • ステータスコード(上記のように)をという変数に格納します status
  • 印刷txtstatus使用print機能

上記のコードで何が起こっているかを理解したら、このラボに合格するのはかなり簡単です。このラボの解決策は次のとおりです。

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

パート2に移りましょう。ここでは、既存のコードをさらに構築します。

パート2:BeautifulSoupでタイトルを抽出する

これは、このラボへのリンクです。

この教室全体ではBeautifulSoup、Pythonで呼び出されたライブラリを使用してWebスクレイピングを行います。BeautifulSoupを強力なソリューションにするいくつかの機能は次のとおりです。

  1. DOMツリーをナビゲート、検索、および変更するための多くの簡単なメソッドとPythonのイディオムを提供します。アプリケーションを書くのに多くのコードは必要ありません
  2. Beautiful Soupは、lxmlやhtml5libなどの人気のあるPythonパーサーの上に配置されているため、さまざまな解析戦略を試したり、速度と柔軟性を交換したりできます。

基本的に、BeautifulSoupは、指定したWeb上のあらゆるものを解析できます。

これがBeautifulSoupの簡単な例です:

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 (...)

合格要件:

  • requestsパッケージを使用して、URLのタイトルを取得します://codedamn-classrooms.github.io/webscraper-python-codedamn-classroom-website/
  • BeautifulSoupを使用して、このページのタイトルをという変数に格納します page_title

上記の例を見ると、page.contentBeautifulSoupの内部にフィードすると、非常にPython的な方法で解析されたDOMツリーの操作を開始できることがわかります。ラボのソリューションは次のとおりです。

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)

これも、URLを変更してページタイトルを印刷する必要がある単純なラボでした。このコードはラボに合格します。

パート3:スープの体と頭

これは、このラボへのリンクです。

前回のラボでtitleは、ページからを抽出する方法を説明しました。特定のセクションを抽出することも同様に簡単です。

また.text、文字列を取得するにはこれらを呼び出す必要があることもわかりましたが、呼び出すことなく印刷すること.textもでき、完全なマークアップが得られます。以下の例を実行してみてください。

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)

あなたが出て抽出できる方法を見てみましょうbodyhead、あなたのページからセクションを。

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!