Tīmekļa izgriešanas Python apmācība - kā nokasīt datus no vietnes

Python ir skaista valoda, kurā var kodēt. Tam ir lieliska pakotņu ekosistēma, trokšņu ir daudz mazāk nekā citās valodās, un to ir ļoti viegli izmantot.

Python tiek izmantots daudzām lietām, sākot no datu analīzes līdz servera programmēšanai. Un viens aizraujošs Python izmantošanas gadījums ir Web nokasīšana.

Šajā rakstā mēs aplūkosim, kā izmantot Python tīmekļa nokasīšanai. Turpinot darbu, mēs strādāsim arī ar pilnu praktisku rokasgrāmatu.

Piezīme. Mēs nokasīsim tīmekļa lapu, kuru mitinu, tāpēc mēs varam droši iemācīties tās nokopēšanu. Daudzi uzņēmumi nepieļauj skrāpēšanu savās vietnēs, tāpēc tas ir labs veids, kā mācīties. Vienkārši pārliecinieties, lai pārbaudītu, pirms nokasāt.

Ievads Web nokasīšanas klasē

Ja vēlaties kodēt, varat izmantot šo bezmaksas codedamn klasikas sastāv no vairākām laboratorijām, lai palīdzētu jums iemācīties tīmekļa nokasīšanu. Šis būs praktisks praktisks mācību uzdevums vietnē codedamn, līdzīgi tam, kā jūs mācāties vietnē freeCodeCamp.

Šajā klasē izmantosiet šo lapu, lai pārbaudītu tīmekļa nokasīšanu: //codedamn-classrooms.github.io/webscraper-python-codedamn-classroom-website/

Šajā klasē ir 7 laboratorijas, un jūs atradīsit laboratoriju katrā šī emuāra ziņojuma daļā. Tīmekļa nokasīšanai mēs izmantosim Python 3.8 + BeautifulSoup 4.

1. daļa: Tīmekļa lapu ielāde ar “pieprasījumu”

Šī ir saite uz šo laboratoriju.

requestsModulis ļauj nosūtīt HTTP pieprasījumus, izmantojot Python.

HTTP pieprasījums atgriež atbildes objektu ar visiem atbildes datiem (saturu, kodējumu, statusu un tā tālāk). Viens lapas HTML iegūšanas piemērs:

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

Izturēšanas prasības:

  • Izmantojot requestsmoduli, iegūstiet šī URL saturu : //codedamn-classrooms.github.io/webscraper-python-codedamn-classroom-website/
  • Saglabājiet teksta atbildi (kā parādīts iepriekš) mainīgajā lielumā txt
  • Saglabājiet statusa kodu (kā parādīts iepriekš) mainīgajā lielumā status
  • Izdrukājiet txtun status, izmantojot printfunkciju

Kad esat sapratis, kas notiek iepriekš minētajā kodā, ir diezgan vienkārši iziet šo laboratoriju. Lūk, šīs laboratorijas risinājums:

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

Pārejam uz 2. daļu, kur jūs varat izveidot vairāk uz sava esošā koda.

2. daļa: Nosaukuma iegūšana ar BeautifulSoup

Šī ir saite uz šo laboratoriju.

Šajā visā klasē jūs izmantosiet bibliotēku, ko sauc BeautifulSouppar Python, lai veiktu tīmekļa nokasīšanu. Dažas funkcijas, kas padara BeautifulSoup par spēcīgu risinājumu, ir:

  1. Tas nodrošina daudz vienkāršu metožu un Pythonic idiomu, lai pārvietotos, meklētu un modificētu DOM koku. Lai uzrakstītu lietojumprogrammu, nav nepieciešams daudz koda
  2. Skaista zupa atrodas virs populāriem Python parsētājiem, piemēram, lxml un html5lib, ļaujot elastīgāk izmēģināt dažādas parsēšanas stratēģijas vai tirdzniecības ātrumu.

Būtībā BeautifulSoup var parsēt jebko tīmeklī, kuru jūs tam piešķirat.

Šeit ir vienkāršs BeautifulSoup piemērs:

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 (...)

Izturēšanas prasības:

  • Izmantojiet requestspakotni, lai iegūtu URL nosaukumu: //codedamn-classrooms.github.io/webscraper-python-codedamn-classroom-website/
  • Izmantojiet BeautifulSoup, lai šīs lapas nosaukumu saglabātu mainīgajā page_title

Aplūkojot iepriekš minēto piemēru, jūs varat redzēt, kad mēs page.contentievietosim iekšpusē BeautifulSoup, jūs varat sākt strādāt ar parsēto DOM koku ļoti pitoniskā veidā. Laboratorijas risinājums būtu:

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)

Šī bija arī vienkārša laboratorija, kur mums bija jāmaina URL un jāizdrukā lapas nosaukums. Šis kods tiktu garām laboratorijai.

3. daļa: Zupas ķermenis un galva

Šī ir saite uz šo laboratoriju.

Pēdējā laboratorijā jūs redzējāt, kā jūs varat izvilkt titleno lapas. Ir vienlīdz viegli izdalīt arī atsevišķas sadaļas.

Jūs arī redzējāt, ka jums ir .textjāpiesaucas šiem, lai iegūtu virkni, taču jūs varat tos izdrukāt, arī nepiezvanot .text, un tas dos jums pilnu marķējumu. Mēģiniet izpildīt šo piemēru:

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)

Apskatīsim, kā jūs varat izvilkt bodyun headsadaļas no savām lapām.

Izturēšanas prasības:

  • Atkārtojiet eksperimentu ar URL: //codedamn-classrooms.github.io/webscraper-python-codedamn-classroom-website/
  • Glabājiet URL nosaukumu (neizsaucot .text) vietrāžā URL page_title
  • Uzglabājiet URL ķermeņa saturu (neizsaucot .text) page_body
  • Glabājiet URL galveno saturu (neizsaucot .text) page_head

Mēģinot izdrukāt page_bodyvai page_headjūs redzēsiet, ka tie tiek drukāti strings. Bet patiesībā, kad print(type page_body)redzēsiet, tā nav virkne, bet tā darbojas labi.

Šī piemēra risinājums būtu vienkāršs, pamatojoties uz iepriekš minēto 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. daļa: atlasiet ar BeautifulSoup

Šī ir saite uz šo laboratoriju.

Tagad, kad esat izpētījis dažas BeautifulSoup daļas, apskatīsim, kā jūs varat atlasīt DOM elementus ar BeautifulSoup metodēm.

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.

Ja jums patika šī klase un šis emuārs, pastāstiet man par to savā twitter un Instagram. Labprāt dzirdētu atsauksmes!