style: better English in the crawling lesson

Author: honzajavorek

sources/academy/webscraping/scraping_basics_python/10_crawling.md

@@ -8,13 +8,13 @@ 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 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.
+In previous lessons we've managed to download the 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:
+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:
 
 ```py
 import httpx
@@ -89,7 +89,7 @@ Each product URL points to a so-called _product detail page_, or PDP. If we open
 
 ![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:
+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
 <div class="product-meta">
@@ -123,7 +123,7 @@ Depending on what's valuable for our use case, we can now use the same technique
 </div>
 ```
 
-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:
+It looks like using a CSS selector to locate the element with the `product-meta__vendor` class, and then extracting its text, should be enough to get the vendor name as a string:
 
 ```py
 vendor = product_soup.select_one(".product-meta__vendor").text.strip()
@@ -133,7 +133,7 @@ But where do we put this line in our program?
 
 ## 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:
+In the `data` loop we're already going through all the products. Let's expand it to include downloading the product detail page, parsing it, extracting the vendor's name, and adding it as a new key in the item's dictionary:
 
 ```py
 ...
@@ -153,7 +153,7 @@ for product in listing_soup.select(".product-item"):
 ...
 ```
 
-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:
+If you run the program now, it'll take longer to finish since it's making 24 more HTTP requests. But in the end, it should produce exports with a new field containing the vendor's name:
 
 <!-- eslint-skip -->
 ```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.
 
 ---