The read_html() function in the pandas the library is a convenient way to scrape data from HTML tables and convert the data into a pandas DataFrame. Here are some key things to know about using read_html():

1. read_html() returns a list of DataFrames, one for each table found in the HTML.

2. read_html() can be used to scrape data from a local HTML file or from a URL that points to an HTML page.

3. read_html() uses the lxml library to parse the HTML, so you'll need to have lxml installed in your environment.

4. read_html() can be used with optional arguments to customize the way that the data is parsed. For example, you can use the attrs argument to specify which HTML tags and attributes should be used to identify the tables that you want to scrape.

5. If the HTML table uses rowspan or colspan attributes, read_html() will create a multi-index in the resulting DataFrame.

   Here's an example of how to use read_html() to scrape data from an HTML table:

    import pandas as pd
   
    # Scrape data from a URL
    url = 'https://en.wikipedia.org/wiki/List_of_countries_by_GDP_(nominal)'
    tables = pd.read_html(url)
   
    # Select the first table
    df = tables[0]
   
    # Print the data
    print(df)
   

   This example scrapes the first table from the Wikipedia page on GDP by country and stores the data in a DataFrame. You can then use the usual pandas functions to manipulate and analyze the data.