From 5e2399b68b04ba129e2e8f020ccc7664af9552ea Mon Sep 17 00:00:00 2001 From: Honza Javorek Date: Tue, 8 Oct 2024 16:38:18 +0200 Subject: [PATCH 01/12] feat: draft further course development --- .../09_getting_links.md | 16 +- .../scraping_basics_python/10_crawling.md | 141 +++++++++++++++++- 2 files changed, 151 insertions(+), 6 deletions(-) diff --git a/sources/academy/webscraping/scraping_basics_python/09_getting_links.md b/sources/academy/webscraping/scraping_basics_python/09_getting_links.md index 69c9bcc48..b880027bf 100644 --- a/sources/academy/webscraping/scraping_basics_python/09_getting_links.md +++ b/sources/academy/webscraping/scraping_basics_python/09_getting_links.md @@ -199,8 +199,11 @@ def export_json(file, data): json.dump(data, file, default=serialize, indent=2) listing_url = "https://warehouse-theme-metal.myshopify.com/collections/sales" -soup = download(listing_url) -data = [parse_product(product) for product in soup.select(".product-item")] +listing_soup = download(listing_url) +data = [ + parse_product(product) + for product in listing_soup.select(".product-item") +] with open("products.csv", "w") as file: export_csv(file, data) @@ -300,9 +303,12 @@ Now we'll pass the base URL to the function in the main body of our program: ```py listing_url = "https://warehouse-theme-metal.myshopify.com/collections/sales" -soup = download(listing_url) -# highlight-next-line -data = [parse_product(product, listing_url) for product in soup.select(".product-item")] +listing_soup = download(listing_url) +data = [ + # highlight-next-line + parse_product(product, listing_url) + for product in listing_soup.select(".product-item") +] ``` When we run the scraper now, we should see full URLs in our exports: diff --git a/sources/academy/webscraping/scraping_basics_python/10_crawling.md b/sources/academy/webscraping/scraping_basics_python/10_crawling.md index 39b5083e8..8f0d1a41f 100644 --- a/sources/academy/webscraping/scraping_basics_python/10_crawling.md +++ b/sources/academy/webscraping/scraping_basics_python/10_crawling.md @@ -1,11 +1,150 @@ --- title: Crawling websites with Python sidebar_label: Crawling websites -description: TODO +description: Lesson about building a Python application for watching prices. Using the HTTPX library to follow links to individual product pages. sidebar_position: 10 slug: /scraping-basics-python/crawling --- +import Exercises from './_exercises.mdx'; + +**In this lesson, we'll follow links to individual product pages. We'll use HTTPX to download them, and BeautifulSoup to process them.** + +--- + +In previous lessons we've managed to download HTML code of a single page, parse it with BeautifulSoup, and extract relevant data from it. We'll do the same now for each of the products. + +Thanks to the refactoring we have functions ready for each of the tasks, so we won't need to repeat ourselves in our code. This is what you should see in your editor now: + +```python +import httpx +from bs4 import BeautifulSoup +from decimal import Decimal +import csv +import json +from urllib.parse import urljoin + +def download(url): + response = httpx.get(url) + response.raise_for_status() + + html_code = response.text + return BeautifulSoup(html_code, "html.parser") + +def parse_product(product, base_url): + title_element = product.select_one(".product-item__title") + title = title_element.text.strip() + url = urljoin(base_url, title_element["href"]) + + price_text = ( + product + .select_one(".price") + .contents[-1] + .strip() + .replace("$", "") + .replace(",", "") + ) + if price_text.startswith("From "): + min_price = Decimal(price_text.removeprefix("From ")) + price = None + else: + min_price = Decimal(price_text) + price = min_price + + return {"title": title, "min_price": min_price, "price": price, "url": url} + +def export_csv(file, data): + fieldnames = list(data[0].keys()) + writer = csv.DictWriter(file, fieldnames=fieldnames) + writer.writeheader() + for row in data: + writer.writerow(row) + +def export_json(file, data): + def serialize(obj): + if isinstance(obj, Decimal): + return str(obj) + raise TypeError("Object not JSON serializable") + + json.dump(data, file, default=serialize, indent=2) + +listing_url = "https://warehouse-theme-metal.myshopify.com/collections/sales" +listing_soup = download(listing_url) +data = [ + parse_product(product, listing_url) + for product in listing_soup.select(".product-item") +] + +with open("products.csv", "w") as file: + export_csv(file, data) + +with open("products.json", "w") as file: + export_json(file, data) +``` + +## Crawling product URLs + +In a new loop below the list comprehension we'll go through the product URLs, download and parse each of them, and extract some new data, e.g. name of the vendor. Then we'll save the data to the `product` dictionary as a new key. + +```python +... + +listing_url = "https://warehouse-theme-metal.myshopify.com/collections/sales" +listing_soup = download(listing_url) +data = [ + parse_product(product, listing_url) + for product in listing_soup.select(".product-item") +] + +# highlight-next-line +for product in data: + # highlight-next-line + product_soup = download(product["url"]) + # highlight-next-line + product["vendor"] = product_soup.select_one(".product-meta__vendor").text.strip() + +... +``` + +If you run the program now, it will take longer to finish, but should produce exports with a new field containing the vendor: + + +```json title=products.json +[ + { + "title": "JBL Flip 4 Waterproof Portable Bluetooth Speaker", + "min_price": "74.95", + "price": "74.95", + "url": "https://warehouse-theme-metal.myshopify.com/products/jbl-flip-4-waterproof-portable-bluetooth-speaker", + "vendor": "JBL" + }, + { + "title": "Sony XBR-950G BRAVIA 4K HDR Ultra HD TV", + "min_price": "1398.00", + "price": null, + "url": "https://warehouse-theme-metal.myshopify.com/products/sony-xbr-65x950g-65-class-64-5-diag-bravia-4k-hdr-ultra-hd-tv", + "vendor": "Sony" + }, + ... +] +``` + + + + :::danger Work in progress This course is incomplete. As we work on adding new lessons, we would love to hear your feedback. You can comment right here under each page or [file a GitHub Issue](https://github.com/apify/apify-docs/issues) to discuss a problem. From a0648f5b924ca378af804a07f1cd371964b9bf37 Mon Sep 17 00:00:00 2001 From: Honza Javorek Date: Mon, 25 Nov 2024 10:37:30 +0100 Subject: [PATCH 02/12] feat: the crawling lesson and more --- .../06_locating_elements.md | 8 ++ .../09_getting_links.md | 20 ++-- .../scraping_basics_python/10_crawling.md | 101 ++++++++++++------ .../11_parsing_variants.md | 47 ++++++++ .../scraping_basics_python/12_framework.md | 33 ++++++ .../scraping_basics_python/images/pdp.png | Bin 0 -> 248563 bytes .../images/variants.png | Bin 0 -> 505794 bytes 7 files changed, 169 insertions(+), 40 deletions(-) create mode 100644 sources/academy/webscraping/scraping_basics_python/11_parsing_variants.md create mode 100644 sources/academy/webscraping/scraping_basics_python/12_framework.md create mode 100644 sources/academy/webscraping/scraping_basics_python/images/pdp.png create mode 100644 sources/academy/webscraping/scraping_basics_python/images/variants.png diff --git a/sources/academy/webscraping/scraping_basics_python/06_locating_elements.md b/sources/academy/webscraping/scraping_basics_python/06_locating_elements.md index 3475fa7f2..78fbda4ab 100644 --- a/sources/academy/webscraping/scraping_basics_python/06_locating_elements.md +++ b/sources/academy/webscraping/scraping_basics_python/06_locating_elements.md @@ -122,6 +122,14 @@ for product in soup.select(".product-item"): This program does the same as the one we already had, but its code is more concise. +:::note Fragile code + +We assume that the selectors we pass to the `select()` or `select_one()` methods return at least one element. If they don't, calling `[0]` on an empty list or `.text` on `None` would crash the program. If you perform type checking on your Python program, the code examples above may even trigger warnings about this. + +Not handling these cases allows us to keep the code examples more succinct. Additionally, if we expect the selectors to return elements but they suddenly don't, it usually means the website has changed since we wrote our scraper. Letting the program crash in such cases is a valid way to notify ourselves that we need to fix it. + +::: + ## Precisely locating price In the output we can see that the price isn't located precisely. For each product, our scraper also prints the text `Sale price`. Let's look at the HTML structure again. Each bit containing the price looks like this: diff --git a/sources/academy/webscraping/scraping_basics_python/09_getting_links.md b/sources/academy/webscraping/scraping_basics_python/09_getting_links.md index b880027bf..32e671ad8 100644 --- a/sources/academy/webscraping/scraping_basics_python/09_getting_links.md +++ b/sources/academy/webscraping/scraping_basics_python/09_getting_links.md @@ -200,10 +200,11 @@ def export_json(file, data): listing_url = "https://warehouse-theme-metal.myshopify.com/collections/sales" listing_soup = download(listing_url) -data = [ - parse_product(product) - for product in listing_soup.select(".product-item") -] + +data = [] +for product in listing_soup.select(".product-item"): + item = parse_product(product) + data.append(item) with open("products.csv", "w") as file: export_csv(file, data) @@ -212,7 +213,7 @@ with open("products.json", "w") as file: export_json(file, data) ``` -The program is much easier to read now. With the `parse_product()` function handy, we could also replace the convoluted loop with a [list comprehension](https://docs.python.org/3/tutorial/datastructures.html#list-comprehensions). +The program is much easier to read now. With the `parse_product()` function handy, we could also replace the convoluted loop with one that only takes up four lines of code. :::tip Refactoring @@ -304,11 +305,12 @@ Now we'll pass the base URL to the function in the main body of our program: ```py listing_url = "https://warehouse-theme-metal.myshopify.com/collections/sales" listing_soup = download(listing_url) -data = [ + +data = [] +for product in listing_soup.select(".product-item"): # highlight-next-line - parse_product(product, listing_url) - for product in listing_soup.select(".product-item") -] + item = parse_product(product, listing_url) + data.append(item) ``` When we run the scraper now, we should see full URLs in our exports: diff --git a/sources/academy/webscraping/scraping_basics_python/10_crawling.md b/sources/academy/webscraping/scraping_basics_python/10_crawling.md index 8f0d1a41f..9c783555f 100644 --- a/sources/academy/webscraping/scraping_basics_python/10_crawling.md +++ b/sources/academy/webscraping/scraping_basics_python/10_crawling.md @@ -70,10 +70,11 @@ def export_json(file, data): listing_url = "https://warehouse-theme-metal.myshopify.com/collections/sales" listing_soup = download(listing_url) -data = [ - parse_product(product, listing_url) - for product in listing_soup.select(".product-item") -] + +data = [] +for product in listing_soup.select(".product-item"): + item = parse_product(product, listing_url) + data.append(item) with open("products.csv", "w") as file: export_csv(file, data) @@ -82,31 +83,77 @@ with open("products.json", "w") as file: export_json(file, data) ``` -## Crawling product URLs +## Extracting vendor name + +Each product URL points to a so-called _product detail page_, or PDP. If we open one of the product URLs in the browser, e.g. the one about [Sony XBR-950G BRAVIA](https://warehouse-theme-metal.myshopify.com/products/sony-xbr-65x950g-65-class-64-5-diag-bravia-4k-hdr-ultra-hd-tv), we can see that it contains a vendor name, [SKU](https://en.wikipedia.org/wiki/Stock_keeping_unit), number of reviews, product images, product variants, stock availability, description, and perhaps more. + +![Product detail page](./images/pdp.png) + +Depending on what's valuable for our use case, we can now use the same techniques as in previous lessons to extract any of the above. As a demonstration, let's scrape the vendor name. In browser DevTools we can see that the HTML around the vendor name has the following structure: + +```html +
+

+ Sony XBR-950G BRAVIA 4K HDR Ultra HD TV +

+
+ ... +
+
+ + + + Sony + + + + SKU: + SON-985594-XBR-65 + +
+ +
+ + 3 reviews +
+
+ ... +
+``` + +It looks like using a CSS selector to locate element having the `product-meta__vendor` class and extracting its text should be enough to get the vendor name as a string: + +```python +vendor = product_soup.select_one(".product-meta__vendor").text.strip() +``` + +But where do we put this line in our program? -In a new loop below the list comprehension we'll go through the product URLs, download and parse each of them, and extract some new data, e.g. name of the vendor. Then we'll save the data to the `product` dictionary as a new key. +## Crawling product detail pages + +In the `data` loop we already go through all the products. Let's expand it so it also includes downloading the product detail page, parsing it, extracting the name of the vendor, and adding it as a new dictionary key to the item: ```python ... listing_url = "https://warehouse-theme-metal.myshopify.com/collections/sales" listing_soup = download(listing_url) -data = [ - parse_product(product, listing_url) - for product in listing_soup.select(".product-item") -] -# highlight-next-line -for product in data: +data = [] +for product in listing_soup.select(".product-item"): + item = parse_product(product, listing_url) # highlight-next-line - product_soup = download(product["url"]) + product_soup = download(item["url"]) # highlight-next-line - product["vendor"] = product_soup.select_one(".product-meta__vendor").text.strip() + item["vendor"] = product_soup.select_one(".product-meta__vendor").text.strip() + data.append(item) ... ``` -If you run the program now, it will take longer to finish, but should produce exports with a new field containing the vendor: +If you run the program now, it will take longer to finish, as it now makes 24 more HTTP requests, but in the end it should produce exports with a new field containing the vendor: ```json title=products.json @@ -129,26 +176,18 @@ If you run the program now, it will take longer to finish, but should produce ex ] ``` - +Looking at [Sony XBR-950G BRAVIA](https://warehouse-theme-metal.myshopify.com/products/sony-xbr-65x950g-65-class-64-5-diag-bravia-4k-hdr-ultra-hd-tv), it's apparent that the listing features only min prices, because some of the products have variants, each with a different price. And different stock availability. And different SKU… +![Morpheus revealing the existence of product variants](images/variants.png) -:::danger Work in progress +In the next lesson we'll scrape the product detail pages in such way that each product variant gets represented as a separate item in our dataset. -This course is incomplete. As we work on adding new lessons, we would love to hear your feedback. You can comment right here under each page or [file a GitHub Issue](https://github.com/apify/apify-docs/issues) to discuss a problem. +--- -This particular page is a placeholder for several lessons which should teach crawling. + -::: +TODO diff --git a/sources/academy/webscraping/scraping_basics_python/11_parsing_variants.md b/sources/academy/webscraping/scraping_basics_python/11_parsing_variants.md new file mode 100644 index 000000000..f31e339f6 --- /dev/null +++ b/sources/academy/webscraping/scraping_basics_python/11_parsing_variants.md @@ -0,0 +1,47 @@ +--- +title: Parsing product variants with Python +sidebar_label: Parsing product variants +description: Lesson about building a Python application for watching prices. Using browser DevTools to figure out how to parse product variants and exporting them as separate items. +sidebar_position: 11 +slug: /scraping-basics-python/parsing-variants +--- + +:::danger Work in progress + +This course is incomplete. As we work on adding new lessons, we would love to hear your feedback. You can comment right here under each page or [file a GitHub Issue](https://github.com/apify/apify-docs/issues) to discuss a problem. + +::: + + + diff --git a/sources/academy/webscraping/scraping_basics_python/12_framework.md b/sources/academy/webscraping/scraping_basics_python/12_framework.md new file mode 100644 index 000000000..bb2a35301 --- /dev/null +++ b/sources/academy/webscraping/scraping_basics_python/12_framework.md @@ -0,0 +1,33 @@ +--- +title: Using a scraping framework with Python +sidebar_label: Using a framework +description: Lesson about building a Python application for watching prices. Using the Crawlee framework to simplify creating a scraper. +sidebar_position: 11 +slug: /scraping-basics-python/framework +--- + +:::danger Work in progress + +This course is incomplete. As we work on adding new lessons, we would love to hear your feedback. You can comment right here under each page or [file a GitHub Issue](https://github.com/apify/apify-docs/issues) to discuss a problem. + +::: + + diff --git a/sources/academy/webscraping/scraping_basics_python/images/pdp.png b/sources/academy/webscraping/scraping_basics_python/images/pdp.png new file mode 100644 index 0000000000000000000000000000000000000000..8f4825b0b1d58353ba3e4532b92d89945fe61357 GIT binary patch literal 248563 zcmcG#WmKC%*Dj0~DK4eBLm)v)ky6|tNC{F1ZpGcDxVr`^4nd0tC|abr2lp0AvEtC; z9Qr)px6b)})_LzASy?kH_ul)OYs*abM88v0z{jP+ML|KqS5lOPprGKwP*AXd*yv9q ze5b1)QBcrP-l=HGJw863U;d#3eMD(n+1&bhcXx+{frXBaX65R0a(j=Df%*6E-=m|W z>+74{o!zsuQvf<9E#@{P~aWqV33`}`6jIoIcA#{xW{olQPeZ1%xhNxK6 z(@0CyXFq=YI6gYFM}5xLGA}3~G&nRoH#hV9_wM@ox(OQj^74u&8nqo7bqWT5EE*SJjXalQb#m?)r`77MJZhHXoHEmL)vS`U8BD z5@NmRG+IpGElZ~CR=0_(rwzMU$FO}!V^@_Y0~Y@u~6X>(V62hYq{S-hox$ z&?Ym7(z5C?JI~s!j^Rq_v^*n-2K5`kq0LO*k9)IiYaMk1T$Tg7C)_kvDt?+zD+Ce4m38f0vpHbU|0iCP*9<` 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zbPEREpTyh1M^Cr-bSORT@7zn=Oc%*1%h!^QQ5FB!sh^&zT5laQI#uU>>gj2J=_!Do zx}WMqncMV~D^GdiDHQc^M`fEN9a?<4e;_>tC%jI`jMZ6vS{^J7g>H(AXx-Gbrl+Q^ yicguIX3mf#$(_V~_uO+&{&y`&k~<0fckK_(yYh<5$q^d>0000 Date: Wed, 9 Oct 2024 18:38:33 +0200 Subject: [PATCH 03/12] feat: lesson about scraping variants --- .../scraping_basics_python/10_crawling.md | 6 +- .../11_parsing_variants.md | 47 --- .../11_scraping_variants.md | 311 ++++++++++++++++++ .../scraping_basics_python/12_framework.md | 1 + 4 files changed, 315 insertions(+), 50 deletions(-) delete mode 100644 sources/academy/webscraping/scraping_basics_python/11_parsing_variants.md create mode 100644 sources/academy/webscraping/scraping_basics_python/11_scraping_variants.md diff --git a/sources/academy/webscraping/scraping_basics_python/10_crawling.md b/sources/academy/webscraping/scraping_basics_python/10_crawling.md index 9c783555f..1cfcfee9d 100644 --- a/sources/academy/webscraping/scraping_basics_python/10_crawling.md +++ b/sources/academy/webscraping/scraping_basics_python/10_crawling.md @@ -16,7 +16,7 @@ In previous lessons we've managed to download HTML code of a single page, parse Thanks to the refactoring we have functions ready for each of the tasks, so we won't need to repeat ourselves in our code. This is what you should see in your editor now: -```python +```py import httpx from bs4 import BeautifulSoup from decimal import Decimal @@ -125,7 +125,7 @@ Depending on what's valuable for our use case, we can now use the same technique It looks like using a CSS selector to locate element having the `product-meta__vendor` class and extracting its text should be enough to get the vendor name as a string: -```python +```py vendor = product_soup.select_one(".product-meta__vendor").text.strip() ``` @@ -135,7 +135,7 @@ But where do we put this line in our program? In the `data` loop we already go through all the products. Let's expand it so it also includes downloading the product detail page, parsing it, extracting the name of the vendor, and adding it as a new dictionary key to the item: -```python +```py ... listing_url = "https://warehouse-theme-metal.myshopify.com/collections/sales" diff --git a/sources/academy/webscraping/scraping_basics_python/11_parsing_variants.md b/sources/academy/webscraping/scraping_basics_python/11_parsing_variants.md deleted file mode 100644 index f31e339f6..000000000 --- a/sources/academy/webscraping/scraping_basics_python/11_parsing_variants.md +++ /dev/null @@ -1,47 +0,0 @@ ---- -title: Parsing product variants with Python -sidebar_label: Parsing product variants -description: Lesson about building a Python application for watching prices. Using browser DevTools to figure out how to parse product variants and exporting them as separate items. -sidebar_position: 11 -slug: /scraping-basics-python/parsing-variants ---- - -:::danger Work in progress - -This course is incomplete. As we work on adding new lessons, we would love to hear your feedback. You can comment right here under each page or [file a GitHub Issue](https://github.com/apify/apify-docs/issues) to discuss a problem. - -::: - - - diff --git a/sources/academy/webscraping/scraping_basics_python/11_scraping_variants.md b/sources/academy/webscraping/scraping_basics_python/11_scraping_variants.md new file mode 100644 index 000000000..bc6a95a7f --- /dev/null +++ b/sources/academy/webscraping/scraping_basics_python/11_scraping_variants.md @@ -0,0 +1,311 @@ +--- +title: Scraping product variants with Python +sidebar_label: Scraping product variants +description: Lesson about building a Python application for watching prices. Using browser DevTools to figure out how to extract product variants and exporting them as separate items. +sidebar_position: 11 +slug: /scraping-basics-python/scraping-variants +--- + +import Exercises from './_exercises.mdx'; + +**In this lesson, we'll scrape the product detail pages to represent each product variant as a separate item in our dataset.** + +--- + +We'll need to figure out how to extract variants from the product detail page, and then change the way we add items to the data list, so that we can add multiple items after scraping one product URL. + +## Locating variants + +First let's extract information about the variants. If we go to [Sony XBR-950G BRAVIA](https://warehouse-theme-metal.myshopify.com/products/sony-xbr-65x950g-65-class-64-5-diag-bravia-4k-hdr-ultra-hd-tv) and open the DevTools, we can see that the buttons for switching between variants look like this: + +```html +

    +
    + + +
    +
    + + +
    +
    +``` + +Nice, we can extract names of the variants! But we also need to extract price for each of the variants. Clicking on the buttons, we can see that the HTML changes dynamically though. This means the page uses JavaScript to display information about the variants. + +If we can't find a workaround, we'd need our scraper to run JavaScript. That's not impossible - scrapers can spin up their own browser instance and automate clicking on buttons, but it's slow and resource-intensive. Ideally, we want to stick to plain HTTP requests and Beautiful Soup as much as possible. + +After a bit of detective work, we can notice that not far below the `block-swatch-list` there's also a block of HTML with a class `no-js`, which contains all the data! + +```html +
    + +
    + +
    +
    +``` + +These elements aren't visible to a regular visitor. They're there just for the eventuality that JavaScript fails to work, otherwise they're hidden. This is a great find which allows us to stay lean with our scraper. + +## Extracting variants + +Using our knowledge of Beautiful Soup we can locate the options and extract the data we need: + +```py +... + +listing_url = "https://warehouse-theme-metal.myshopify.com/collections/sales" +listing_soup = download(listing_url) + +data = [] +for product in listing_soup.select(".product-item"): + item = parse_product(product, listing_url) + product_soup = download(item["url"]) + vendor = product_soup.select_one(".product-meta__vendor").text.strip() + + if variants := product_soup.select(".product-form__option.no-js option"): + for variant in variants: + data.append(item | {"variant_name": variant.text.strip()}) + else: + item["variant_name"] = None + data.append(item) + +... +``` + +The CSS selector `.product-form__option.no-js` matches elements with both `product-form__option` and `no-js` classes. Then we're using the [descendant combinator](https://developer.mozilla.org/en-US/docs/Web/CSS/Descendant_combinator) to actually match all `option` elements, which are somewhere inside the `.product-form__option.no-js` wrapper. + +Python dictionaries are mutable, so if we assigned the variant with `item["variant_name"] = ...`, we would always overwrite the values. Instead of saving an item for each variant we'd always get the last variant, several times. To avoid this pitfall, we create a new dictionary for each variant and merge it with the `item` data before adding to `data`. In case we don't find any variants, we add the `item` as is, with the `variant_name` key left empty. + +:::tip Python syntax you might not know + +Since Python 3.8 you can use `:=` to simplify checking if an assignment resulted in a non-empty value. It's called _assignment expression_ or _walrus_ and you can learn more about it in the [docs](https://docs.python.org/3/reference/expressions.html#assignment-expressions) or in the [proposal document](https://peps.python.org/pep-0572/). + +Since Python 3.9 you can use `|` to merge two dictionaries. If [docs](https://docs.python.org/3/library/stdtypes.html#dict) don't feel explanatory enough, there's again a whole [proposal document](https://peps.python.org/pep-0584/) about it. + +::: + +If you run the program, you should see 34 items in total. Some items should have no variant: + + +```json title=products.json +[ + ... + { + "variant_name": null, + "title": "Klipsch R-120SW Powerful Detailed Home Speaker - Unit", + "min_price": "324.00", + "price": "324.00", + "url": "https://warehouse-theme-metal.myshopify.com/products/klipsch-r-120sw-powerful-detailed-home-speaker-set-of-1", + "vendor": "Klipsch" + }, + ... +] +``` + +Some products where we're missing the actual price should now have several variants: + + +```json title=products.json +[ + ... + { + "variant_name": "Red - $178.00", + "title": "Sony XB-950B1 Extra Bass Wireless Headphones with App Control", + "min_price": "128.00", + "price": null, + "url": "https://warehouse-theme-metal.myshopify.com/products/sony-xb950-extra-bass-wireless-headphones-with-app-control", + "vendor": "Sony" + }, + { + "variant_name": "Black - $178.00", + "title": "Sony XB-950B1 Extra Bass Wireless Headphones with App Control", + "min_price": "128.00", + "price": null, + "url": "https://warehouse-theme-metal.myshopify.com/products/sony-xb950-extra-bass-wireless-headphones-with-app-control", + "vendor": "Sony" + }, + ... +] +``` + +However, some products with variants will have the `price` field set. That's because the shop sells all these variants for the same price, so the product listing displayed the price as an exact number: + + +```json title=products.json +[ + ... + { + "variant_name": "Red - $74.95", + "title": "JBL Flip 4 Waterproof Portable Bluetooth Speaker", + "min_price": "74.95", + "price": "74.95", + "url": "https://warehouse-theme-metal.myshopify.com/products/jbl-flip-4-waterproof-portable-bluetooth-speaker", + "vendor": "JBL" + }, + ... +] +``` + +## Parsing price + +The items now contain the variant as a text, which is good for a start, but it would be more useful if we could set the price to the `price` key. Let's introduce a new function which will take care of that: + +```py +def parse_variant(variant): + text = variant.text.strip() + name, price_text = text.split(" - ") + price = Decimal( + price_text + .replace("$", "") + .replace(",", "") + ) + return {"variant_name": name, "price": price} +``` + +First we split the text in two parts, then we parse the price as a decimal number. That part is similar to what we already have for parsing the product listing prices. The function then returns a dictionary which we can merge with `item`. + +## Saving price + +Now if we use our new function, we should finally get a program which is able to scrape exact prices for all products, even if they have variants. The whole code should look like this now: + +```py +import httpx +from bs4 import BeautifulSoup +from decimal import Decimal +import csv +import json +from urllib.parse import urljoin + +def download(url): + response = httpx.get(url) + response.raise_for_status() + + html_code = response.text + return BeautifulSoup(html_code, "html.parser") + +def parse_product(product, base_url): + title_element = product.select_one(".product-item__title") + title = title_element.text.strip() + url = urljoin(base_url, title_element["href"]) + + price_text = ( + product + .select_one(".price") + .contents[-1] + .strip() + .replace("$", "") + .replace(",", "") + ) + if price_text.startswith("From "): + min_price = Decimal(price_text.removeprefix("From ")) + price = None + else: + min_price = Decimal(price_text) + price = min_price + + return {"title": title, "min_price": min_price, "price": price, "url": url} + +def parse_variant(variant): + text = variant.text.strip() + name, price_text = text.split(" - ") + price = Decimal( + price_text + .replace("$", "") + .replace(",", "") + ) + return {"variant_name": name, "price": price} + +def export_csv(file, data): + fieldnames = list(data[0].keys()) + writer = csv.DictWriter(file, fieldnames=fieldnames) + writer.writeheader() + for row in data: + writer.writerow(row) + +def export_json(file, data): + def serialize(obj): + if isinstance(obj, Decimal): + return str(obj) + raise TypeError("Object not JSON serializable") + + json.dump(data, file, default=serialize, indent=2) + +listing_url = "https://warehouse-theme-metal.myshopify.com/collections/sales" +listing_soup = download(listing_url) + +data = [] +for product in listing_soup.select(".product-item"): + item = parse_product(product, listing_url) + product_soup = download(item["url"]) + vendor = product_soup.select_one(".product-meta__vendor").text.strip() + + if variants := product_soup.select(".product-form__option.no-js option"): + for variant in variants: + # highlight-next-line + data.append(item | parse_variant(variant)) + else: + item["variant_name"] = None + data.append(item) + +with open("products.csv", "w") as file: + export_csv(file, data) + +with open("products.json", "w") as file: + export_json(file, data) +``` + +Run the scraper and see for yourself if all items in the data contains prices: + + +```json title=products.json +[ + ... + { + "variant_name": "Red", + "title": "Sony XB-950B1 Extra Bass Wireless Headphones with App Control", + "min_price": "128.00", + "price": "178.00", + "url": "https://warehouse-theme-metal.myshopify.com/products/sony-xb950-extra-bass-wireless-headphones-with-app-control", + "vendor": "Sony" + }, + { + "variant_name": "Black", + "title": "Sony XB-950B1 Extra Bass Wireless Headphones with App Control", + "min_price": "128.00", + "price": "178.00", + "url": "https://warehouse-theme-metal.myshopify.com/products/sony-xb950-extra-bass-wireless-headphones-with-app-control", + "vendor": "Sony" + }, + ... +] +``` + +Success! We managed to build a Python application for watching prices! + +Is this the end? Maybe! In the next lesson we'll use scraping framework to build the same application, but with less code, faster requests, and visibility into what's actually happening when you wait for the program to finish. + +--- + + + +TODO + diff --git a/sources/academy/webscraping/scraping_basics_python/12_framework.md b/sources/academy/webscraping/scraping_basics_python/12_framework.md index bb2a35301..6b8188940 100644 --- a/sources/academy/webscraping/scraping_basics_python/12_framework.md +++ b/sources/academy/webscraping/scraping_basics_python/12_framework.md @@ -29,5 +29,6 @@ caveats and reasons for framework: - a lot of boilerplate code - anti-scraping protection - browser crawling support +- error handling --> From 16ea03997e122b1b88ddc89c172527f43a78b9d3 Mon Sep 17 00:00:00 2001 From: Honza Javorek Date: Thu, 10 Oct 2024 16:37:03 +0200 Subject: [PATCH 04/12] feat: prepare next lesson stubs --- .../scraping_basics_python/12_framework.md | 12 ++++-------- .../scraping_basics_python/13_platform.md | 13 +++++++++++++ 2 files changed, 17 insertions(+), 8 deletions(-) create mode 100644 sources/academy/webscraping/scraping_basics_python/13_platform.md diff --git a/sources/academy/webscraping/scraping_basics_python/12_framework.md b/sources/academy/webscraping/scraping_basics_python/12_framework.md index 6b8188940..4845c025b 100644 --- a/sources/academy/webscraping/scraping_basics_python/12_framework.md +++ b/sources/academy/webscraping/scraping_basics_python/12_framework.md @@ -2,7 +2,7 @@ title: Using a scraping framework with Python sidebar_label: Using a framework description: Lesson about building a Python application for watching prices. Using the Crawlee framework to simplify creating a scraper. -sidebar_position: 11 +sidebar_position: 12 slug: /scraping-basics-python/framework --- @@ -14,16 +14,12 @@ This course is incomplete. As we work on adding new lessons, we would love to he ```json title=products.json @@ -178,13 +178,13 @@ If you run the program now, it will take longer to finish, as it now makes 24 mo ## Extracting price -Being able to scrape vendor name is nice, but the main reason we started peeking at the detail pages in the first place was to figure out how to get a price for each product, because from the product listing we could only scrape the min price. And we're building a Python application for watching prices, remember? +Scraping the vendor's name is nice, but the main reason we started checking the detail pages in the first place was to figure out how to get a price for each product. From the product listing, we could only scrape the min price, and remember—we’re building a Python app to track prices! -Looking at [Sony XBR-950G BRAVIA](https://warehouse-theme-metal.myshopify.com/products/sony-xbr-65x950g-65-class-64-5-diag-bravia-4k-hdr-ultra-hd-tv), it's apparent that the listing features only min prices, because some of the products have variants, each with a different price. And different stock availability. And different SKU… +Looking at the [Sony XBR-950G BRAVIA](https://warehouse-theme-metal.myshopify.com/products/sony-xbr-65x950g-65-class-64-5-diag-bravia-4k-hdr-ultra-hd-tv), it's clear that the listing only shows min prices, because some products have variants, each with a different price. And different stock availability. And different SKUs… ![Morpheus revealing the existence of product variants](images/variants.png) -In the next lesson we'll scrape the product detail pages in such way that each product variant gets represented as a separate item in our dataset. +In the next lesson, we'll scrape the product detail pages so that each product variant is represented as a separate item in our dataset. --- From 0749d5f40c4cb99d2f0bfb7f9d63eebc1bec0473 Mon Sep 17 00:00:00 2001 From: Honza Javorek Date: Thu, 10 Oct 2024 17:27:02 +0200 Subject: [PATCH 06/12] style: better English in the variants lesson --- .../11_scraping_variants.md | 34 +++++++++---------- 1 file changed, 17 insertions(+), 17 deletions(-) diff --git a/sources/academy/webscraping/scraping_basics_python/11_scraping_variants.md b/sources/academy/webscraping/scraping_basics_python/11_scraping_variants.md index bc6a95a7f..fc9c96151 100644 --- a/sources/academy/webscraping/scraping_basics_python/11_scraping_variants.md +++ b/sources/academy/webscraping/scraping_basics_python/11_scraping_variants.md @@ -12,11 +12,11 @@ import Exercises from './_exercises.mdx'; --- -We'll need to figure out how to extract variants from the product detail page, and then change the way we add items to the data list, so that we can add multiple items after scraping one product URL. +We'll need to figure out how to extract variants from the product detail page, and then change how we add items to the data list so we can add multiple items after scraping one product URL. ## Locating variants -First let's extract information about the variants. If we go to [Sony XBR-950G BRAVIA](https://warehouse-theme-metal.myshopify.com/products/sony-xbr-65x950g-65-class-64-5-diag-bravia-4k-hdr-ultra-hd-tv) and open the DevTools, we can see that the buttons for switching between variants look like this: +First, let's extract information about the variants. If we go to [Sony XBR-950G BRAVIA](https://warehouse-theme-metal.myshopify.com/products/sony-xbr-65x950g-65-class-64-5-diag-bravia-4k-hdr-ultra-hd-tv) and open the DevTools, we can see that the buttons for switching between variants look like this: ```html
    @@ -37,11 +37,11 @@ First let's extract information about the variants. If we go to [Sony XBR-950G B
    ``` -Nice, we can extract names of the variants! But we also need to extract price for each of the variants. Clicking on the buttons, we can see that the HTML changes dynamically though. This means the page uses JavaScript to display information about the variants. +Nice! We can extract the variant names, but we also need to extract the price for each variant. Switching the variants using the buttons shows us that the HTML changes dynamically. This means the page uses JavaScript to display information about the variants. -If we can't find a workaround, we'd need our scraper to run JavaScript. That's not impossible - scrapers can spin up their own browser instance and automate clicking on buttons, but it's slow and resource-intensive. Ideally, we want to stick to plain HTTP requests and Beautiful Soup as much as possible. +If we can't find a workaround, we'd need our scraper to run JavaScript. That's not impossible—scrapers can spin up their own browser instance and automate clicking on buttons, but it's slow and resource-intensive. Ideally, we want to stick to plain HTTP requests and Beautiful Soup as much as possible. -After a bit of detective work, we can notice that not far below the `block-swatch-list` there's also a block of HTML with a class `no-js`, which contains all the data! +After a bit of detective work, we notice that not far below the `block-swatch-list` there's also a block of HTML with a class `no-js`, which contains all the data! ```html
    @@ -63,11 +63,11 @@ After a bit of detective work, we can notice that not far below the `block-swatc
    ``` -These elements aren't visible to a regular visitor. They're there just for the eventuality that JavaScript fails to work, otherwise they're hidden. This is a great find which allows us to stay lean with our scraper. +These elements aren't visible to regular visitors. They're there just in case JavaScript fails to work, otherwise they're hidden. This is a great find because it allows us to keep our scraper lightweight. ## Extracting variants -Using our knowledge of Beautiful Soup we can locate the options and extract the data we need: +Using our knowledge of Beautiful Soup, we can locate the options and extract the data we need: ```py ... @@ -91,15 +91,15 @@ for product in listing_soup.select(".product-item"): ... ``` -The CSS selector `.product-form__option.no-js` matches elements with both `product-form__option` and `no-js` classes. Then we're using the [descendant combinator](https://developer.mozilla.org/en-US/docs/Web/CSS/Descendant_combinator) to actually match all `option` elements, which are somewhere inside the `.product-form__option.no-js` wrapper. +The CSS selector `.product-form__option.no-js` matches elements with both `product-form__option` and `no-js` classes. Then we're using the [descendant combinator](https://developer.mozilla.org/en-US/docs/Web/CSS/Descendant_combinator) to match all `option` elements somewhere inside the `.product-form__option.no-js` wrapper. -Python dictionaries are mutable, so if we assigned the variant with `item["variant_name"] = ...`, we would always overwrite the values. Instead of saving an item for each variant we'd always get the last variant, several times. To avoid this pitfall, we create a new dictionary for each variant and merge it with the `item` data before adding to `data`. In case we don't find any variants, we add the `item` as is, with the `variant_name` key left empty. +Python dictionaries are mutable, so if we assigned the variant with `item["variant_name"] = ...`, we'd always overwrite the values. Instead of saving an item for each variant, we'd end up with the last variant repeated several times. To avoid this, we create a new dictionary for each variant and merge it with the `item` data before adding it to `data`. If we don't find any variants, we add the `item` as is, leaving the `variant_name` key empty. :::tip Python syntax you might not know -Since Python 3.8 you can use `:=` to simplify checking if an assignment resulted in a non-empty value. It's called _assignment expression_ or _walrus_ and you can learn more about it in the [docs](https://docs.python.org/3/reference/expressions.html#assignment-expressions) or in the [proposal document](https://peps.python.org/pep-0572/). +Since Python 3.8, you can use `:=` to simplify checking if an assignment resulted in a non-empty value. It's called an _assignment expression_ or _walrus operator_. You can learn more about it in the [docs](https://docs.python.org/3/reference/expressions.html#assignment-expressions) or in the [proposal document](https://peps.python.org/pep-0572/). -Since Python 3.9 you can use `|` to merge two dictionaries. If [docs](https://docs.python.org/3/library/stdtypes.html#dict) don't feel explanatory enough, there's again a whole [proposal document](https://peps.python.org/pep-0584/) about it. +Since Python 3.9, you can use `|` to merge two dictionaries. If the [docs](https://docs.python.org/3/library/stdtypes.html#dict) aren't clear enough, check out the [proposal document](https://peps.python.org/pep-0584/) for more details. ::: @@ -147,7 +147,7 @@ Some products where we're missing the actual price should now have several varia ] ``` -However, some products with variants will have the `price` field set. That's because the shop sells all these variants for the same price, so the product listing displayed the price as an exact number: +However, some products with variants will have the `price` field set. That's because the shop sells all these variants for the same price, so the product listing displays the price as a fixed amount: ```json title=products.json @@ -167,7 +167,7 @@ However, some products with variants will have the `price` field set. That's bec ## Parsing price -The items now contain the variant as a text, which is good for a start, but it would be more useful if we could set the price to the `price` key. Let's introduce a new function which will take care of that: +The items now contain the variant as text, which is good for a start, but it would be more useful to set the price in the `price` key. Let's introduce a new function to handle that: ```py def parse_variant(variant): @@ -181,11 +181,11 @@ def parse_variant(variant): return {"variant_name": name, "price": price} ``` -First we split the text in two parts, then we parse the price as a decimal number. That part is similar to what we already have for parsing the product listing prices. The function then returns a dictionary which we can merge with `item`. +First, we split the text into two parts, then we parse the price as a decimal number. This part is similar to what we already do for parsing product listing prices. The function returns a dictionary we can merge with `item`. ## Saving price -Now if we use our new function, we should finally get a program which is able to scrape exact prices for all products, even if they have variants. The whole code should look like this now: +Now, if we use our new function, we should finally get a program that can scrape exact prices for all products, even if they have variants. The whole code should look like this now: ```py import httpx @@ -273,7 +273,7 @@ with open("products.json", "w") as file: export_json(file, data) ``` -Run the scraper and see for yourself if all items in the data contains prices: +Run the scraper and see for yourself if all the items in the data contain prices: ```json title=products.json @@ -301,7 +301,7 @@ Run the scraper and see for yourself if all items in the data contains prices: Success! We managed to build a Python application for watching prices! -Is this the end? Maybe! In the next lesson we'll use scraping framework to build the same application, but with less code, faster requests, and visibility into what's actually happening when you wait for the program to finish. +Is this the end? Maybe! In the next lesson, we'll use a scraping framework to build the same application, but with less code, faster requests, and better visibility into what's happening while we wait for the program to finish. --- From e0ecf7c97fbbd6075d793e57d71138a39484d120 Mon Sep 17 00:00:00 2001 From: Honza Javorek Date: Thu, 10 Oct 2024 18:34:54 +0200 Subject: [PATCH 07/12] feat: add exercises --- .../scraping_basics_python/10_crawling.md | 114 +++++++++++++++++- 1 file changed, 113 insertions(+), 1 deletion(-) diff --git a/sources/academy/webscraping/scraping_basics_python/10_crawling.md b/sources/academy/webscraping/scraping_basics_python/10_crawling.md index 1e4b8d4db..cb8d30fa8 100644 --- a/sources/academy/webscraping/scraping_basics_python/10_crawling.md +++ b/sources/academy/webscraping/scraping_basics_python/10_crawling.md @@ -190,4 +190,116 @@ In the next lesson, we'll scrape the product detail pages so that each product v -TODO +### Scrape calling codes of African countries + +This is a follow-up to an exercise from the previous lesson, so feel free to reuse code. Scrape links to Wikipedia pages of all African states and territories. Follow the links and for each country extract the calling code, which is in the info table. Print URL and the calling code for all the countries. Start with this URL: + +```text +https://en.wikipedia.org/wiki/List_of_sovereign_states_and_dependent_territories_in_Africa +``` + +Your program should print the following: + +```text +https://en.wikipedia.org/wiki/Algeria +213 +https://en.wikipedia.org/wiki/Angola +244 +https://en.wikipedia.org/wiki/Benin +229 +https://en.wikipedia.org/wiki/Botswana +267 +https://en.wikipedia.org/wiki/Burkina_Faso +226 +https://en.wikipedia.org/wiki/Burundi None +https://en.wikipedia.org/wiki/Cameroon +237 +... +``` + +Hint: Locating cells in tables is sometimes easier if you know how to [go up](https://beautiful-soup-4.readthedocs.io/en/latest/index.html#going-up) in the HTML element soup. + +
    + Solution + + ```py + import httpx + from bs4 import BeautifulSoup + from urllib.parse import urljoin + + def download(url): + response = httpx.get(url) + response.raise_for_status() + return BeautifulSoup(response.text, "html.parser") + + def parse_calling_code(soup): + for label in soup.select("th.infobox-label"): + if label.text.strip() == "Calling code": + data = label.parent.select_one("td.infobox-data") + return data.text.strip() + return None + + listing_url = "https://en.wikipedia.org/wiki/List_of_sovereign_states_and_dependent_territories_in_Africa" + listing_soup = download(listing_url) + for name_cell in listing_soup.select(".wikitable tr td:nth-child(3)"): + link = name_cell.select_one("a") + country_url = urljoin(listing_url, link["href"]) + country_soup = download(country_url) + calling_code = parse_calling_code(country_soup) + print(country_url, calling_code) + ``` + +
    + +### Scrape authors of F1 news articles + +This is a follow-up to an exercise from the previous lesson, so feel free to reuse code. Scrape links to Guardian's latest F1 news. Follow the link for each article and extract both the author's name and the article's title. Print the author's name and the title for all the articles. Start with this URL: + +```text +https://www.theguardian.com/sport/formulaone +``` + +Your program should print something like the following: + +```text +Colin Horgan: The NHL is getting its own Drive to Survive. But could it backfire? +Reuters: US GP ticket sales ‘took off’ after Max Verstappen stopped winning in F1 +Giles Richards: Liam Lawson gets F1 chance to replace Pérez alongside Verstappen at Red Bull +PA Media: Lewis Hamilton reveals lifelong battle with depression after school bullying +Giles Richards: Red Bull must solve Verstappen’s ‘monster’ riddle or Norris will pounce +... +``` + +Hints: + +- You can use [attribute selectors](https://developer.mozilla.org/en-US/docs/Web/CSS/Attribute_selectors) to select HTML elements based on values of their attributes. +- Notice that sometimes a person authors the article, but sometimes it's a contribution by a news agency. + +
    + Solution + + ```py + import httpx + from bs4 import BeautifulSoup + from urllib.parse import urljoin + + def download(url): + response = httpx.get(url) + response.raise_for_status() + return BeautifulSoup(response.text, "html.parser") + + def parse_author(article_soup): + link = article_soup.select_one('aside a[rel="author"]') + if link: + return link.text.strip() + address = article_soup.select_one('aside address') + if address: + return address.text.strip() + return None + + listing_url = "https://www.theguardian.com/sport/formulaone" + listing_soup = download(listing_url) + for item in listing_soup.select("#maincontent ul li"): + link = item.select_one("a") + article_url = urljoin(listing_url, link["href"]) + article_soup = download(article_url) + title = article_soup.select_one("h1").text.strip() + author = parse_author(article_soup) + print(f"{author}: {title}") + ``` + +
    From f88abc83bb0fd1b9f71f2fcb847f6029ce8bbcfe Mon Sep 17 00:00:00 2001 From: Honza Javorek Date: Fri, 11 Oct 2024 12:14:07 +0200 Subject: [PATCH 08/12] feat: exercises --- .../04_downloading_html.md | 7 +- .../scraping_basics_python/10_crawling.md | 2 +- .../11_scraping_variants.md | 109 +++++++++++++++++- 3 files changed, 112 insertions(+), 6 deletions(-) diff --git a/sources/academy/webscraping/scraping_basics_python/04_downloading_html.md b/sources/academy/webscraping/scraping_basics_python/04_downloading_html.md index 4928fec65..e13f33daa 100644 --- a/sources/academy/webscraping/scraping_basics_python/04_downloading_html.md +++ b/sources/academy/webscraping/scraping_basics_python/04_downloading_html.md @@ -142,10 +142,10 @@ Letting our program visibly crash on error is enough for our purposes. Now, let' ### Scrape Amazon -Download HTML of a product listing page, but this time from a real world e-commerce website. For example this page with Amazon search results: +Download HTML of a product listing page, but this time from a real world e-commerce website. For example this page with AliExpress search results: ```text -https://www.amazon.com/s?k=darth+vader +https://www.aliexpress.com/w/wholesale-darth-vader.html ```
    @@ -154,13 +154,12 @@ https://www.amazon.com/s?k=darth+vader ```py import httpx - url = "https://www.amazon.com/s?k=darth+vader" + url = "https://www.aliexpress.com/w/wholesale-darth-vader.html" response = httpx.get(url) response.raise_for_status() print(response.text) ``` - If you get `Server error '503 Service Unavailable'`, that's just Amazon's anti-scraping protections. You can learn about how to overcome those in our [Anti-scraping protections](../anti_scraping/index.md) course.
    ### Save downloaded HTML as a file diff --git a/sources/academy/webscraping/scraping_basics_python/10_crawling.md b/sources/academy/webscraping/scraping_basics_python/10_crawling.md index cb8d30fa8..909bec101 100644 --- a/sources/academy/webscraping/scraping_basics_python/10_crawling.md +++ b/sources/academy/webscraping/scraping_basics_python/10_crawling.md @@ -256,11 +256,11 @@ https://www.theguardian.com/sport/formulaone Your program should print something like the following: ```text +Daniel Harris: Sports quiz of the week: Johan Neeskens, Bond and airborne antics Colin Horgan: The NHL is getting its own Drive to Survive. But could it backfire? Reuters: US GP ticket sales ‘took off’ after Max Verstappen stopped winning in F1 Giles Richards: Liam Lawson gets F1 chance to replace Pérez alongside Verstappen at Red Bull PA Media: Lewis Hamilton reveals lifelong battle with depression after school bullying -Giles Richards: Red Bull must solve Verstappen’s ‘monster’ riddle or Norris will pounce ... ``` diff --git a/sources/academy/webscraping/scraping_basics_python/11_scraping_variants.md b/sources/academy/webscraping/scraping_basics_python/11_scraping_variants.md index fc9c96151..de3352f51 100644 --- a/sources/academy/webscraping/scraping_basics_python/11_scraping_variants.md +++ b/sources/academy/webscraping/scraping_basics_python/11_scraping_variants.md @@ -307,5 +307,112 @@ Is this the end? Maybe! In the next lesson, we'll use a scraping framework to bu -TODO +### Build a scraper for watching Python jobs +You're now able to build a scraper, are you? Let's build another one, then! Python's official website features a [job board](https://www.python.org/jobs/). Scrape job postings which match the following criteria: + +- Tagged as Database +- Not older than 60 days + +For each job posting found, use [`pp()`](https://docs.python.org/3/library/pprint.html#pprint.pp) to print a dictionary containing the following data: + +- Job title +- Company +- URL to the job posting +- Date of posting + +Your program should print something like the following: + +```text +{'title': 'Senior Full Stack Developer', + 'company': 'Baserow', + 'url': 'https://www.python.org/jobs/7705/', + 'posted_on': datetime.date(2024, 9, 16)} +{'title': 'Senior Python Engineer', + 'company': 'Active Prime', + 'url': 'https://www.python.org/jobs/7699/', + 'posted_on': datetime.date(2024, 9, 5)} +... +``` + +In Python's [`datetime`](https://docs.python.org/3/library/datetime.html) module you should find everything you need for manipulating time: `date.today()`, `datetime.fromisoformat()`, `datetime.date()`, `timedelta()`. + +
    + Solution + + After inspecting how the job board works, we can notice that job postings tagged as Database have their own URL. We'll use it as the starting point, as it'll save us from needing to scrape and check the tags. + + ```py + from pprint import pp + import httpx + from bs4 import BeautifulSoup + from urllib.parse import urljoin + from datetime import datetime, date, timedelta + + today = date.today() + jobs_url = "https://www.python.org/jobs/type/database/" + response = httpx.get(jobs_url) + response.raise_for_status() + soup = BeautifulSoup(response.text, "html.parser") + + for job in soup.select(".list-recent-jobs li"): + link = job.select_one(".listing-company-name a") + + time = job.select_one(".listing-posted time") + posted_at = datetime.fromisoformat(time["datetime"]) + posted_on = posted_at.date() + posted_ago = today - posted_on + + if posted_ago <= timedelta(days=60): + title = link.text.strip() + company = list(job.select_one(".listing-company-name").stripped_strings)[-1] + url = urljoin(jobs_url, link["href"]) + pp({"title": title, "company": company, "url": url, "posted_on": posted_on}) + ``` + +
    + +### Find the shortest CNN article which made it to the Sports homepage + +Scrape the [CNN Sports](https://edition.cnn.com/sport) homepage. For each linked article, calculate its length in characters: + +- Locate element which holds the main content of the article. +- Use [`get_text()`](https://beautiful-soup-4.readthedocs.io/en/latest/index.html#get-text) to get all its content as a plain text. +- Use `len()` to calculate the length. + +Skip pages without text, e.g. those which contain only a video. Sort the results and print URL to the shortest article which made it to the homepage. + +At the time of writing this exercise, the shortest article which made it to the CNN Sports homepage is [one about a donation to the Augusta National Golf Club](https://edition.cnn.com/2024/10/03/sport/masters-donation-hurricane-helene-relief-spt-intl/). It's just 1,642 characters long. + +
    + Solution + + ```py + import httpx + from bs4 import BeautifulSoup + from urllib.parse import urljoin + + def download(url): + response = httpx.get(url) + response.raise_for_status() + return BeautifulSoup(response.text, "html.parser") + + listing_url = "https://edition.cnn.com/sport" + listing_soup = download(listing_url) + + data = [] + for card in listing_soup.select(".layout__main .card"): + link = card.select_one(".container__link") + article_url = urljoin(listing_url, link["href"]) + article_soup = download(article_url) + if content := article_soup.select_one(".article__content"): + length = len(content.get_text()) + data.append((length, article_url)) + + data.sort() + shortest_item = data[0] + item_url = shortest_item[1] + print(item_url) + ``` + +
    From 7680d36484cd5d186dd63dd2e283b5e20f4cdb7b Mon Sep 17 00:00:00 2001 From: Honza Javorek Date: Fri, 11 Oct 2024 12:24:57 +0200 Subject: [PATCH 09/12] style: better English --- .../scraping_basics_python/10_crawling.md | 12 +++++----- .../11_scraping_variants.md | 22 +++++++++---------- 2 files changed, 17 insertions(+), 17 deletions(-) diff --git a/sources/academy/webscraping/scraping_basics_python/10_crawling.md b/sources/academy/webscraping/scraping_basics_python/10_crawling.md index 909bec101..4de67279f 100644 --- a/sources/academy/webscraping/scraping_basics_python/10_crawling.md +++ b/sources/academy/webscraping/scraping_basics_python/10_crawling.md @@ -192,7 +192,7 @@ In the next lesson, we'll scrape the product detail pages so that each product v ### Scrape calling codes of African countries -This is a follow-up to an exercise from the previous lesson, so feel free to reuse code. Scrape links to Wikipedia pages of all African states and territories. Follow the links and for each country extract the calling code, which is in the info table. Print URL and the calling code for all the countries. Start with this URL: +This is a follow-up to an exercise from the previous lesson, so feel free to reuse your code. Scrape links to Wikipedia pages for all African states and territories. Follow each link and extract the calling code from the info table. Print the URL and the calling code for each country. Start with this URL: ```text https://en.wikipedia.org/wiki/List_of_sovereign_states_and_dependent_territories_in_Africa @@ -211,7 +211,7 @@ https://en.wikipedia.org/wiki/Cameroon +237 ... ``` -Hint: Locating cells in tables is sometimes easier if you know how to [go up](https://beautiful-soup-4.readthedocs.io/en/latest/index.html#going-up) in the HTML element soup. +Hint: Locating cells in tables is sometimes easier if you know how to [navigate up](https://beautiful-soup-4.readthedocs.io/en/latest/index.html#going-up) in the HTML element soup.
    Solution @@ -247,13 +247,13 @@ Hint: Locating cells in tables is sometimes easier if you know how to [go up](ht ### Scrape authors of F1 news articles -This is a follow-up to an exercise from the previous lesson, so feel free to reuse code. Scrape links to Guardian's latest F1 news. Follow the link for each article and extract both the author's name and the article's title. Print the author's name and the title for all the articles. Start with this URL: +This is a follow-up to an exercise from the previous lesson, so feel free to reuse your code. Scrape links to the Guardian's latest F1 news articles. For each article, follow the link and extract both the author's name and the article's title. Print the author's name and the title for all the articles. Start with this URL: ```text https://www.theguardian.com/sport/formulaone ``` -Your program should print something like the following: +Your program should print something like this: ```text Daniel Harris: Sports quiz of the week: Johan Neeskens, Bond and airborne antics @@ -266,8 +266,8 @@ PA Media: Lewis Hamilton reveals lifelong battle with depression after school bu Hints: -- You can use [attribute selectors](https://developer.mozilla.org/en-US/docs/Web/CSS/Attribute_selectors) to select HTML elements based on values of their attributes. -- Notice that sometimes a person authors the article, but sometimes it's a contribution by a news agency. +- You can use [attribute selectors](https://developer.mozilla.org/en-US/docs/Web/CSS/Attribute_selectors) to select HTML elements based on their attribute values. +- Sometimes a person authors the article, but other times it's contributed by a news agency.
    Solution diff --git a/sources/academy/webscraping/scraping_basics_python/11_scraping_variants.md b/sources/academy/webscraping/scraping_basics_python/11_scraping_variants.md index de3352f51..9a2c3f49c 100644 --- a/sources/academy/webscraping/scraping_basics_python/11_scraping_variants.md +++ b/sources/academy/webscraping/scraping_basics_python/11_scraping_variants.md @@ -309,10 +309,10 @@ Is this the end? Maybe! In the next lesson, we'll use a scraping framework to bu ### Build a scraper for watching Python jobs -You're now able to build a scraper, are you? Let's build another one, then! Python's official website features a [job board](https://www.python.org/jobs/). Scrape job postings which match the following criteria: +You're able to build a scraper now, aren't you? Let's build another one! Python's official website has a [job board](https://www.python.org/jobs/). Scrape the job postings that match the following criteria: -- Tagged as Database -- Not older than 60 days +- Tagged as "Database" +- Posted within the last 60 days For each job posting found, use [`pp()`](https://docs.python.org/3/library/pprint.html#pprint.pp) to print a dictionary containing the following data: @@ -321,7 +321,7 @@ For each job posting found, use [`pp()`](https://docs.python.org/3/library/pprin - URL to the job posting - Date of posting -Your program should print something like the following: +Your output should look something like this: ```text {'title': 'Senior Full Stack Developer', @@ -335,12 +335,12 @@ Your program should print something like the following: ... ``` -In Python's [`datetime`](https://docs.python.org/3/library/datetime.html) module you should find everything you need for manipulating time: `date.today()`, `datetime.fromisoformat()`, `datetime.date()`, `timedelta()`. +You can find everything you need for working with dates and times in Python's [`datetime`](https://docs.python.org/3/library/datetime.html) module, including `date.today()`, `datetime.fromisoformat()`, `datetime.date()`, and `timedelta()`.
    Solution - After inspecting how the job board works, we can notice that job postings tagged as Database have their own URL. We'll use it as the starting point, as it'll save us from needing to scrape and check the tags. + After inspecting the job board, you'll notice that job postings tagged as "Database" have a dedicated URL. We'll use that as our starting point, which saves us from having to scrape and check the tags manually. ```py from pprint import pp @@ -376,13 +376,13 @@ In Python's [`datetime`](https://docs.python.org/3/library/datetime.html) module Scrape the [CNN Sports](https://edition.cnn.com/sport) homepage. For each linked article, calculate its length in characters: -- Locate element which holds the main content of the article. -- Use [`get_text()`](https://beautiful-soup-4.readthedocs.io/en/latest/index.html#get-text) to get all its content as a plain text. -- Use `len()` to calculate the length. +- Locate the element that holds the main content of the article. +- Use [`get_text()`](https://beautiful-soup-4.readthedocs.io/en/latest/index.html#get-text) to extract all the content as plain text. +- Use `len()` to calculate the character count. -Skip pages without text, e.g. those which contain only a video. Sort the results and print URL to the shortest article which made it to the homepage. +Skip pages without text (like those that only have a video). Sort the results and print the URL of the shortest article that made it to the homepage. -At the time of writing this exercise, the shortest article which made it to the CNN Sports homepage is [one about a donation to the Augusta National Golf Club](https://edition.cnn.com/2024/10/03/sport/masters-donation-hurricane-helene-relief-spt-intl/). It's just 1,642 characters long. +At the time of writing, the shortest article on the CNN Sports homepage is [about a donation to the Augusta National Golf Club](https://edition.cnn.com/2024/10/03/sport/masters-donation-hurricane-helene-relief-spt-intl/), which is just 1,642 characters long.
    Solution From 3cf28939b607e283b554c14b74358696eaaa6735 Mon Sep 17 00:00:00 2001 From: Honza Javorek Date: Fri, 11 Oct 2024 18:52:28 +0200 Subject: [PATCH 10/12] fix: heading Co-authored-by: Ondra Urban <23726914+mnmkng@users.noreply.github.com> --- .../webscraping/scraping_basics_python/04_downloading_html.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/sources/academy/webscraping/scraping_basics_python/04_downloading_html.md b/sources/academy/webscraping/scraping_basics_python/04_downloading_html.md index e13f33daa..e664de2af 100644 --- a/sources/academy/webscraping/scraping_basics_python/04_downloading_html.md +++ b/sources/academy/webscraping/scraping_basics_python/04_downloading_html.md @@ -140,7 +140,7 @@ Letting our program visibly crash on error is enough for our purposes. Now, let' -### Scrape Amazon +### Scrape AliExpress Download HTML of a product listing page, but this time from a real world e-commerce website. For example this page with AliExpress search results: From 017d2ae8849efad910d648738333af80fc898ccc Mon Sep 17 00:00:00 2001 From: Honza Javorek Date: Fri, 11 Oct 2024 18:52:49 +0200 Subject: [PATCH 11/12] style: split sentences for better readability Co-authored-by: Ondra Urban <23726914+mnmkng@users.noreply.github.com> --- .../webscraping/scraping_basics_python/11_scraping_variants.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/sources/academy/webscraping/scraping_basics_python/11_scraping_variants.md b/sources/academy/webscraping/scraping_basics_python/11_scraping_variants.md index 9a2c3f49c..df506dd31 100644 --- a/sources/academy/webscraping/scraping_basics_python/11_scraping_variants.md +++ b/sources/academy/webscraping/scraping_basics_python/11_scraping_variants.md @@ -39,7 +39,7 @@ First, let's extract information about the variants. If we go to [Sony XBR-950G Nice! We can extract the variant names, but we also need to extract the price for each variant. Switching the variants using the buttons shows us that the HTML changes dynamically. This means the page uses JavaScript to display information about the variants. -If we can't find a workaround, we'd need our scraper to run JavaScript. That's not impossible—scrapers can spin up their own browser instance and automate clicking on buttons, but it's slow and resource-intensive. Ideally, we want to stick to plain HTTP requests and Beautiful Soup as much as possible. +If we can't find a workaround, we'd need our scraper to run JavaScript. That's not impossible. Scrapers can spin up their own browser instance and automate clicking on buttons, but it's slow and resource-intensive. Ideally, we want to stick to plain HTTP requests and Beautiful Soup as much as possible. After a bit of detective work, we notice that not far below the `block-swatch-list` there's also a block of HTML with a class `no-js`, which contains all the data! From 30a75bdddc7ad5da29d78f86fa6265cc37470327 Mon Sep 17 00:00:00 2001 From: Honza Javorek Date: Fri, 18 Oct 2024 10:10:34 +0200 Subject: [PATCH 12/12] fix: part with samples more comprehensible, add gif of variants --- .../11_scraping_variants.md | 10 ++++++---- .../images/variants-js.gif | Bin 0 -> 1322486 bytes 2 files changed, 6 insertions(+), 4 deletions(-) create mode 100644 sources/academy/webscraping/scraping_basics_python/images/variants-js.gif diff --git a/sources/academy/webscraping/scraping_basics_python/11_scraping_variants.md b/sources/academy/webscraping/scraping_basics_python/11_scraping_variants.md index df506dd31..677414b31 100644 --- a/sources/academy/webscraping/scraping_basics_python/11_scraping_variants.md +++ b/sources/academy/webscraping/scraping_basics_python/11_scraping_variants.md @@ -39,6 +39,8 @@ First, let's extract information about the variants. If we go to [Sony XBR-950G Nice! We can extract the variant names, but we also need to extract the price for each variant. Switching the variants using the buttons shows us that the HTML changes dynamically. This means the page uses JavaScript to display information about the variants. +![Switching variants](images/variants-js.gif) + If we can't find a workaround, we'd need our scraper to run JavaScript. That's not impossible. Scrapers can spin up their own browser instance and automate clicking on buttons, but it's slow and resource-intensive. Ideally, we want to stick to plain HTTP requests and Beautiful Soup as much as possible. After a bit of detective work, we notice that not far below the `block-swatch-list` there's also a block of HTML with a class `no-js`, which contains all the data! @@ -103,7 +105,7 @@ Since Python 3.9, you can use `|` to merge two dictionaries. If the [docs](https ::: -If you run the program, you should see 34 items in total. Some items should have no variant: +If you run the program, you should see 34 items in total. Some items don't have variants, so they won't have a variant name. However, they should still have a price set—our scraper should already have that info from the product listing page. ```json title=products.json @@ -121,7 +123,7 @@ If you run the program, you should see 34 items in total. Some items should have ] ``` -Some products where we're missing the actual price should now have several variants: +Some products will break into several items, each with a different variant name. We don't know their exact prices from the product listing, just the min price. In the next step, we should be able to parse the actual price from the variant name for those items. ```json title=products.json @@ -147,7 +149,7 @@ Some products where we're missing the actual price should now have several varia ] ``` -However, some products with variants will have the `price` field set. That's because the shop sells all these variants for the same price, so the product listing displays the price as a fixed amount: +Perhaps surprisingly, some products with variants will have the price field set. That's because the shop sells all variants of the product for the same price, so the product listing shows the price as a fixed amount, like _$74.95_, instead of _from $74.95_. ```json title=products.json @@ -167,7 +169,7 @@ However, some products with variants will have the `price` field set. That's bec ## Parsing price -The items now contain the variant as text, which is good for a start, but it would be more useful to set the price in the `price` key. Let's introduce a new function to handle that: +The items now contain the variant as text, which is good for a start, but we want the price to be in the `price` key. Let's introduce a new function to handle that: ```py def parse_variant(variant): diff --git a/sources/academy/webscraping/scraping_basics_python/images/variants-js.gif b/sources/academy/webscraping/scraping_basics_python/images/variants-js.gif new file mode 100644 index 0000000000000000000000000000000000000000..f1f982afa52c8d70f0168f2f9251a0621f1dc202 GIT binary patch literal 1322486 zcmd2?g8%PpPNImu>sIWO$};p4vDBivauo20tgfdU_(Nn`fL!u z{T#9e-3_2$gRTJd?-BYn_b3Dc1%>KEp-2P*Id_koL!jjV2%A0>-O7eUup#fE$T}c;uXblpAwhuwFp%oEG z1OV+6Ek{9W&_y2r)vrMVhN5cN0CNDeDZm^X63T`i7hn#}7z7Pof9^jm002E4G@DTL z8C8QW2-F;#J^+p69$oYiXhQz;jz)s4L5~#e9W93dAPB%cR3Cw627nHMK3fe6dXIw6 zp{W4eqm7|Su0gjVp-{AU0NWg@1}#Si184M-z^!xku}wLx!dob&s9^ zbLh?h=o}h3nqCy!fA;m~0LZx-02^9`SKeYc{CIB21 zfE|aP69~d22b1HaSy%+hU|_MxPi*GRl=$K`D`px`m3~&PGI-f4C0RzQg}5E+J)Mgs*$Sh$Qh(jh2zNJ z=0>^dESbk$$-N`RhL8k*SxJT8zq^?Zv*^qPfi!dzO=taq3$q?R$-60M$B*ktGz`aws8O2SA3F97}4^`B@A#qrn}m*)fIv8r8^vKAurl+^LW(DBU}vyV4tadyigy{FOS<3RkJRrLGO-y3nAjvF@Y)=e~d 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