style: use Markdown's automatic numbering

sources/academy/webscraping/scraping_basics_javascript2/08_saving_data.md

@@ -231,8 +231,8 @@ Open the `products.csv` file we created in the lesson using a spreadsheet applic
   Let's use [Google Sheets](https://www.google.com/sheets/about/), which is free to use. After logging in with a Google account:
 
   1. Go to **File > Import**, choose **Upload**, and select the file. Import the data using the default settings. You should see a table with all the data.
-  2. Select the header row. Go to **Data > Create filter**.
-  3. Use the filter icon that appears next to `minPrice`. Choose **Filter by condition**, select **Greater than**, and enter **500** in the text field. Confirm the dialog. You should see only the filtered data.
+  1. Select the header row. Go to **Data > Create filter**.
+  1. Use the filter icon that appears next to `minPrice`. Choose **Filter by condition**, select **Greater than**, and enter **500** in the text field. Confirm the dialog. You should see only the filtered data.
 
   ![CSV in Google Sheets](images/csv-sheets.png)
 

sources/academy/webscraping/scraping_basics_python/08_saving_data.md

@@ -215,8 +215,8 @@ Open the `products.csv` file we created in the lesson using a spreadsheet applic
   Let's use [Google Sheets](https://www.google.com/sheets/about/), which is free to use. After logging in with a Google account:
 
   1. Go to **File > Import**, choose **Upload**, and select the file. Import the data using the default settings. You should see a table with all the data.
-  2. Select the header row. Go to **Data > Create filter**.
-  3. Use the filter icon that appears next to `min_price`. Choose **Filter by condition**, select **Greater than**, and enter **500** in the text field. Confirm the dialog. You should see only the filtered data.
+  1. Select the header row. Go to **Data > Create filter**.
+  1. Use the filter icon that appears next to `min_price`. Choose **Filter by condition**, select **Greater than**, and enter **500** in the text field. Confirm the dialog. You should see only the filtered data.
 
   ![CSV in Google Sheets](images/csv-sheets.png)
 

sources/academy/webscraping/scraping_basics_python/12_framework.md

@@ -66,12 +66,12 @@ if __name__ == '__main__':
 In the code, we do the following:
 
 1. We import the necessary modules and define an asynchronous `main()` function.
-2. Inside `main()`, we first create a crawler object, which manages the scraping process. In this case, it's a crawler based on Beautiful Soup.
-3. Next, we define a nested asynchronous function called `handle_listing()`. It receives a `context` parameter, and Python type hints show it's of type `BeautifulSoupCrawlingContext`. Type hints help editors suggest what we can do with the object.
-4. We use a Python decorator (the line starting with `@`) to register `handle_listing()` as the _default handler_ for processing HTTP responses.
-5. Inside the handler, we extract the page title from the `soup` object and print its text without whitespace.
-6. At the end of the function, we run the crawler on a product listing URL and await its completion.
-7. The last two lines ensure that if the file is executed directly, Python will properly run the `main()` function using its asynchronous event loop.
+1. Inside `main()`, we first create a crawler object, which manages the scraping process. In this case, it's a crawler based on Beautiful Soup.
+1. Next, we define a nested asynchronous function called `handle_listing()`. It receives a `context` parameter, and Python type hints show it's of type `BeautifulSoupCrawlingContext`. Type hints help editors suggest what we can do with the object.
+1. We use a Python decorator (the line starting with `@`) to register `handle_listing()` as the _default handler_ for processing HTTP responses.
+1. Inside the handler, we extract the page title from the `soup` object and print its text without whitespace.
+1. At the end of the function, we run the crawler on a product listing URL and await its completion.
+1. The last two lines ensure that if the file is executed directly, Python will properly run the `main()` function using its asynchronous event loop.
 
 Don't worry if some of this is new. We don't need to know exactly how [`asyncio`](https://docs.python.org/3/library/asyncio.html), decorators, or type hints work. Let's stick to the practical side and observe what the program does when executed: