The Ultimate Guide To Using Pandas Read HTML Function And Why Proxies Matter

If you’ve decided to perform internet research with a web scraper, you’ve probably heard about Pandas or have seen the phrase Pandas read html. It’s an incredibly popular Python library that’s great for collecting and analyzing data efficiently.

Still, if you’re new to Python, Pandas can be a little confusing. You need to structure your program correctly if you want Pandas to interact with HTML code. Keep reading to learn how Pandas works, how to read HTML in Pandas, and make sure your Pandas-based scraper works consistently.

What Is Pandas?

Pandas is an open-source library that anyone can use to write data analysis programs. The library offers multiple invaluable features, such as:

• A DataFrame that allows for data manipulation and indexing
• Data set reshaping
• Cross-format read and write tools
• Time series
• Merging, joining, and grouping of data sets
• Powerful read_html feature to college information in the first place

If you want to do an in-depth analysis of the data your scraper collects, Pandas is an essential tool to include in any Python scraper. Pandas will help you extract valuable information from HTML and then analyze it for you, all in one program.

How To Start Using Pandas

Because it’s an open-source library, you can get started with Pandas in minutes. All you need is an IDE and the Python language. Visual Studio, PyCharm, and Jupyter are great options if you don’t have an IDE.

Once you’ve got an IDE, you can go to your command line to install Pandas. Installation with the pip command is easy. Just type in:

pip3 install pandas

After this runs, you can open a new Python file in your chosen IDE. Pandas is ready to go, so you can start writing your program to read HTML Pandas-style.

How To Read HTML in Pandas

The read html command is fundamental to writing a successful Python scraper with Pandas. Here’s how to use Pandas to read in the HTML file by extracting the information into data frames.

How to extract HTML tables from files

The first step for having Pandas read HTML is to extract information from the HTML and turn it into a Pandas data frame. Let’s look at HTML tables as common data-rich HTML structures that are sometimes tricky to extract information from.

The command you’ll need to use is the “read_html()” function. This function specifically checks through the target URL and converts it into a data frame that you can manipulate. For instance, you could find all the HTML tables of the Wikipedia page “List of musicals: A to L” with this code:

tables = pd.read_html(‘https://en.wikipedia.org/wiki/List_of_musicals:_A_to_L’)

df = tables[0]

This Pandas read HTML table code defines those tables into a Pandas data frame. Once you’ve run this, you’ll be able to use Pandas’ excellent analytical tools to interact with the information in all the tables on that page.

How to get Pandas to_html to be read in HTML

You can also convert a Pandas data frame into HTML to display your information in an easily-presentable format. In this case, you take the data frame you’ve generated with your scraping and tell Pandas to convert it into HTML with the “DataFrame.to_html()” command. This would look like the following:

output = pd.read_html(‘https://en.wikipedia.org/wiki/List_of_musicals:_A_to_L’)

Print(output.to_html())

This would strip all the HTML tables from the Wikipedia page, then print them into a plain HTML table.

Examples of How To Read HTML in Pandas

Now that you know how Pandas read HTML function works, it’s time to learn how to use it in your web scraping program. Let’s say you only want to collect the names of musicals, and you don’t care about the other information on the page. This Pandas read HTML example would only display production titles:

titles = pd.read_html(‘https://en.wikipedia.org/wiki/List_of_musicals:_A_to_L’, match=’Production’)

print(titles)

Similarly, you could combine multiple tables on the same page into one large data frame to analyze by using the “concat” function, like this:

all_info = pd.read_html(‘https://en.wikipedia.org/wiki/List_of_musicals:_A_to_L’)

df = pd.concat(all_info).reset_index(drop=True)

This combines all the tables on the page into one massive data frame, then removes the original tables to free up memory. You can also investigate the Pandas documentation to learn additional ways to refine your program’s Pandas read HTML attempts.

Why Proxies Matter When Using Pandas

No matter what you’re researching with Pandas and Python, you should also use the third P: proxies. Well-designed web scrapers integrate proxies to protect themselves from overzealous website security programs.

When your scraper visits URLs to collect data with Pandas, it can get your IP address blocked if the site determines it’s not a human visitor. Proxies prevent this by shielding your IP address behind a “proxy” IP.

• Data center: These proxies are housed in data centers, so they’re inexpensive but relatively obvious to websites. You can use data center proxies for fast scrapes or get them in bulk and rapidly switch them out.
• ISP: These proxies are also housed in data centers, but they’re linked to ISPs (internet service providers) to look more convincing to websites. They’re more expensive than other proxy options, but they’re also more convincing to websites. ISP proxies are a great option for moderate scrapes and usage.
• Residential: These proxies are connected to residential addresses, so they’re indistinguishable from real people outside of their behavior. They’re a big investment but more secure for high-importance scrapes due to the residential proxy’s human-like behavior and high success rates.

You don’t need to implement proxies on your own, though. You don’t even need to understand how to integrate them with Pandas. Instead, you can work with Rayobyte’s Web Scraping API to have a safe, secure custom scraper written on your behalf. The expert team at Rayobyte’s Web Scraping API already integrates Rayobyte’s industry-leading proxies and does the web scraping for you! All you have to do is analyze the data.

Final Thoughts

Whether you write your own web scraper or make your life easier by working with Rayobyte’s Web Scraping API, the Pandas library is an invaluable tool. Integrating Rayobyte’s proxies into your web scraper ensures that the work you put into learning the Pandas read HTML feature doesn’t go to waste.

Learn more about how proxies can protect your web scraper from getting banned by websites and how to make web scraping stress-free by getting in touch with the Rayobyte team today.

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