„Python“ žiniatinklio nuskaitymo pamoka - kaip nuskaityti duomenis iš svetainės

„Python“ yra graži kalba, į kurią reikia koduoti. Ji turi puikią paketų ekosistemą, yra daug mažiau triukšmo, nei rasite kitomis kalbomis, ir ją naudoti yra labai paprasta.

„Python“ naudojamas daugeliui dalykų, nuo duomenų analizės iki serverio programavimo. Ir vienas įdomus „Python“ naudojimo atvejis yra žiniatinklio grandymas.

Šiame straipsnyje aptarsime, kaip naudoti „Python“ žiniatinklio grandymui. Toliau dirbdami naudosime išsamų praktinį klasės vadovą.

Pastaba: mes nurašysime mano priglobtą tinklalapį, kad galėtume saugiai išmokti jį braižyti. Daugelis kompanijų neleidžia kasytis savo interneto svetainėse, todėl tai yra geras būdas mokytis. Tiesiog įsitikinkite, kad patikrinote, kol nesubraižėte.

Įvadas į žiniatinklio grandymo klasę

Jei norite koduoti, galite naudoti šią nemokamą „codedamn“ klasękurį sudaro kelios laboratorijos, padėsiančios išmokti žiniatinklio grandymą. Tai bus praktiškas praktinis mokymasis naudojant „codedamn“, panašus į tai, kaip mokotės „freeCodeCamp“.

Šioje klasėje naudosite šį puslapį, norėdami išbandyti žiniatinklio grandymą: //codedamn-classrooms.github.io/webscraper-python-codedamn-classroom-website/

Šią klasę sudaro 7 laboratorijos, ir jūs išspręsite laboratoriją kiekvienoje šio tinklaraščio įrašo dalyje. Žiniatinklio sąveikai naudosime „Python 3.8 + BeautifulSoup 4“.

1 dalis: Tinklalapių įkėlimas su „užklausa“

Tai nuoroda į šią laboratoriją.

requestsModulis leidžia jums siųsti HTTP užklausas, naudojant Python.

HTTP užklausa pateikia atsakymo objektą su visais atsakymo duomenimis (turiniu, kodavimu, būsena ir pan.). Vienas puslapio HTML gavimo pavyzdys:

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

Sėkmės reikalavimai:

  • Gaukite šio URL turinį naudodami requestsmodulį: //codedamn-classrooms.github.io/webscraper-python-codedamn-classroom-website/
  • Saugokite teksto atsakymą (kaip parodyta aukščiau) kintamajame, vadinamame txt
  • Būsenos kodą (kaip parodyta aukščiau) saugokite kintamajame, vadinamame status
  • Spausdinti txtir statusnaudoti printfunkciją

Kai suprasite, kas vyksta aukščiau esančiame kode, praeiti iš šios laboratorijos yra gana paprasta. Štai šios laboratorijos sprendimas:

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

Dabar pereikime prie 2 dalies, kur sukursite daugiau ant savo esamo kodo.

2 dalis: Pavadinimo ištraukimas naudojant „BeautifulSoup“

Tai nuoroda į šią laboratoriją.

Šioje klasėje naudosite biblioteką, vadinamą BeautifulSoup„Python“, norėdami atlikti žiniatinklio grandymą. Kai kurios funkcijos daro „BeautifulSoup“ galingu sprendimu:

  1. Čia pateikiama daugybė paprastų metodų ir „Pythonic“ idiomų naršant, ieškant ir modifikuojant DOM medį. Parašyti paraišką nereikia daug kodo
  2. „Beautiful Soup“ yra ant populiarių „Python“ analizatorių, pvz., „Lxml“ ir „html5lib“, leidžiančių išbandyti įvairias analizavimo strategijas arba prekybos greitį, kad būtų lankstumas.

Iš esmės „BeautifulSoup“ gali išanalizuoti viską, ką jūs jam suteikiate.

Štai paprastas „BeautifulSoup“ pavyzdys:

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 (...)

Sėkmės reikalavimai:

  • Norėdami requestsgauti URL pavadinimą, naudokite paketą: //codedamn-classrooms.github.io/webscraper-python-codedamn-classroom-website/
  • Norėdami išsaugoti šio puslapio pavadinimą kintamajame, naudokite „BeautifulSoup“ page_title

Žvelgdami į aukščiau pateiktą pavyzdį, galite pamatyti, kai mes tiekiame page.contentvidinę „BeautifulSoup“, galite pradėti dirbti su išanalizuotu DOM medžiu labai pitoniškai. Laboratorijos sprendimas būtų:

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)

Tai taip pat buvo paprasta laboratorija, kurioje turėjome pakeisti URL ir atsispausdinti puslapio pavadinimą. Šis kodas praeis pro laboratoriją.

3 dalis: sriubos kūnas ir galva

Tai nuoroda į šią laboratoriją.

Paskutinėje laboratorijoje matėte, kaip galite išgauti titleiš puslapio. Taip pat lengva išgauti tam tikrus skyrius.

Jūs taip pat matėte, kad turite kreiptis .textį juos, norėdami gauti eilutę, tačiau galite juos atsispausdinti ir nekviesdami .text, ir tai suteiks jums visą žymėjimą. Pabandykite paleisti toliau pateiktą pavyzdį:

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)

Pažvelkime, kaip galite išgauti bodyir headskyrius iš savo puslapių.

Sėkmės reikalavimai:

  • Pakartokite eksperimentą su URL: //codedamn-classrooms.github.io/webscraper-python-codedamn-classroom-website/
  • Laikykite URL puslapio pavadinimą (nekviečiant .text) page_title
  • Saugokite URL turinį (nekviečiant .text) page_body
  • Saugokite URL turinį (nekviečiant .text) page_head

Kai bandote spausdinti page_bodyarba page_headpamatysite, kad tie, spausdinami, taip strings. Tačiau iš tikrųjų, kai print(type page_body)pamatysite, tai nėra eilutė, bet ji veikia puikiai.

Šio pavyzdžio sprendimas būtų paprastas, remiantis aukščiau pateiktu kodu:

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)

4 dalis: pasirinkite naudodami „BeautifulSoup“

Tai nuoroda į šią laboratoriją.

Dabar, kai ištyrėte kai kurias „BeautifulSoup“ dalis, pažiūrėkime, kaip galite pasirinkti DOM elementus naudodami „BeautifulSoup“ metodus.

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.

Jei jums patiko ši klasė ir šis tinklaraštis, papasakokite apie tai savo „Twitter“ ir „Instagram“. Labai norėčiau išgirsti atsiliepimą!