Webskrabning Python-vejledning - Sådan skrabes data fra et websted

Python er et smukt sprog at kode i. Det har et fantastisk pakkeøkosystem, der er meget mindre støj, end du finder på andre sprog, og det er super nemt at bruge.

Python bruges til en række ting, fra dataanalyse til serverprogrammering. Og en spændende anvendelse af Python er Web Scraping.

I denne artikel vil vi dække, hvordan du bruger Python til webskrabning. Vi gennemgår også en komplet praktisk vejledning i klasseværelset, når vi fortsætter.

Bemærk: Vi skraber en webside, som jeg er vært for, så vi sikkert kan lære at skrabe på den. Mange virksomheder tillader ikke skrabning på deres websteder, så dette er en god måde at lære på. Bare sørg for at kontrollere, før du skraber.

Introduktion til webskrabning klasselokale

Hvis du vil kode sammen, kan du bruge dette gratis codedamn-klasseværelseder består af flere laboratorier, der hjælper dig med at lære webskrabning. Dette vil være en praktisk praktisk læringsøvelse på codedamn, svarende til hvordan du lærer på freeCodeCamp.

I dette klasseværelse bruger du denne side til at teste webskrabning: //codedamn-classrooms.github.io/webscraper-python-codedamn-classroom-website/

Dette klasseværelse består af 7 laboratorier, og du løser et laboratorium i hver del af dette blogindlæg. Vi bruger Python 3.8 + BeautifulSoup 4 til webskrabning.

Del 1: Indlæsning af websider med 'anmodning'

Dette er linket til dette laboratorium.

Det requestsmodul tillader dig at sende HTTP-forespørgsler ved hjælp af Python.

HTTP-anmodningen returnerer et svarobjekt med alle svardataene (indhold, kodning, status osv.). Et eksempel på at få HTML på en side:

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

Bestået krav:

  • Hent indholdet af følgende URL ved hjælp af requestsmodulet: //codedamn-classrooms.github.io/webscraper-python-codedamn-classroom-website/
  • Gem tekstsvaret (som vist ovenfor) i en kaldet variabel txt
  • Gem statuskoden (som vist ovenfor) i en kaldet variabel status
  • Udskriv txtog statusbrug printfunktion

Når du først har forstået, hvad der sker i koden ovenfor, er det ret simpelt at bestå dette laboratorium. Her er løsningen på dette laboratorium:

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

Lad os gå videre til del 2 nu, hvor du bygger mere oven på din eksisterende kode.

Del 2: Uddrag af titel med BeautifulSoup

Dette er linket til dette laboratorium.

I hele dette klasseværelse bruger du et bibliotek kaldet BeautifulSoupPython til at udføre webskrabning. Nogle funktioner, der gør BeautifulSoup til en effektiv løsning, er:

  1. Det giver mange enkle metoder og pythoniske idiomer til at navigere, søge og ændre et DOM-træ. Det tager ikke meget kode at skrive en applikation
  2. Smuk suppe sidder oven på populære Python-parsere som lxml og html5lib, så du kan prøve forskellige parseringsstrategier eller handelshastighed for fleksibilitet.

Dybest set kan BeautifulSoup analysere noget på nettet, du giver det.

Her er et simpelt eksempel på BeautifulSoup:

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 (...)

Bestået krav:

  • Brug requestspakken til at hente titlen på URL: //codedamn-classrooms.github.io/webscraper-python-codedamn-classroom-website/
  • Brug BeautifulSoup til at gemme titlen på denne side i en kaldet variabel page_title

Ser man på eksemplet ovenfor, kan du se, når vi fodrer page.contentindersiden af ​​BeautifulSoup, kan du begynde at arbejde med det parsede DOM-træ på en meget pythonisk måde. Løsningen til laboratoriet vil være:

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)

Dette var også et simpelt laboratorium, hvor vi skulle ændre URL'en og udskrive sidetitlen. Denne kode ville passere laboratoriet.

Del 3: Suppe-krop og hoved

Dette er linket til dette laboratorium.

I det sidste laboratorium så du, hvordan du kan udtrække den titlefra siden. Det er lige så let at udtrække visse sektioner også.

Du så også, at du skal ringe .texttil disse for at få strengen, men du kan udskrive dem uden at ringe .textogså, og det giver dig den fulde markering. Prøv at køre eksemplet nedenfor:

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)

Lad os se på, hvordan du kan udtrække bodyog headsektioner fra dine sider.

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.

Hvis du kunne lide dette klasseværelse og denne blog, så fortæl mig om det på min twitter og Instagram. Vil meget gerne høre feedback!