Web Scraping Using Pandas

This Wikipedia page contains lists of Largest recorded music markets from 2004 to 2017. We will scrape data for the year 2017. There are two ways to save data to CSV and Excel. Using CSV module. Using Pandas Dataframe. I personally prefer to use pandas data frame as it is much easier to use and we can instantly visualize and clean the. While web-based data collection can be a challenging task via a manual approach, a lot of automated solutions have cropped up courtesy open-source contributions from software developers. The technical term for this is web scraping or web extraction. With the use of automated solutions for scraping the web, data scientists can. Find the best information and most relevant links on all topics related toThis domain may be for sale!

Web Scraping is the technique of automatically extracting data from websites using software/script. Many popular Python libraries are used for this procedure, like BeautifulSoup, Scrapy, or Selenium. Mastering these libraries is a precious skill for programmers in the long run. However, there are cases when you can take a much easier approach: Pandas

Yes, I’m talking about the Pandas library which is usually used for data manipulation and analysis. You can take advantage of the read_html() function and scrape tabular data from any website with only a single line of code.

In the following part, I will show you how to use this function. For this example, I will use a code I’ve written to pull data from the popular football statistics website, fbref.com.

Web scraping through pandas

As I mentioned before, this method works only for tabular data (tabular data includes <table> tag in the HTML code). As the screenshot shows below, fbref is perfect for this purpose.

The code

Here you can see the code, I used the read_html() function which returns a list of DataFrame objects – thus you have to use indexing.

After running the code, you already have access to a DataFrame looking like this:

Data Cleaning

When using this method, you usually have to do some de-cluttering and data cleaning to reach the required format for data analysis. In this case, the first thing we have to fix is the headers. This table uses multiple level headers, which are hard to address, so let’s collapse them.

Another necessary step is removing in-table headers. I don’t know the exact term for these, but they are repeatedly placed into the table and skewing our data, so we should get rid of them.

Working with the dataset requires some other steps of data cleaning such as converting data into the correct data types, deciding how to handle NaN values, renaming columns, and more. I won’t go into details on that topic, because it’s mostly personal preference, but here is a link to my GitHub repo, you can take a look at the whole code there.

What’s next?

You can try this approach on any site that uses tabular data, a few examples are Wikipedia sites, weather data, or demographics of countries.


After cleaning the data properly, you can run data analysis on it with Pandas, or export it to .csv or Excel and put it into a visualization software like Tableau or Power BI. If you wrote a full code including data cleaning and formatting, you can even automate your visualizations and dashboards.

@pathi_shilpa Thank you Shilpa!

Read More

Web Scraping Using Pandas Python

@BMooreWasTaken Thanks a lot, Brian!

Read More

@prem_prasann @MarkBradbourne Thanks Prasann!

PandasRead More

Pandas makes it easy to scrape a table (<table> tag) on a web page. After obtaining it as a DataFrame, it is of course possible to do various processing and save it as an Excel file or csv file.

Web Scraping With Pandas And Beautifulsoup

In this article you’ll learn how to extract a table from any webpage. Sometimes there are multiple tables on a webpage, so you can select the table you need.

Related course:Data Analysis with Python Pandas

Pandas web scraping

Install modules

It needs the modules lxml, html5lib, beautifulsoup4. You can install it with pip.


You can use the function read_html(url) to get webpage contents.

The table we’ll get is from Wikipedia. We get version history table from Wikipedia Python page:

Web Scraping Using Pandas

This outputs:

Because there is one table on the page. If you change the url, the output will differ.
To output the table:

You can access columns like this:

Web scraping with pandas

Web Scraping Using Pandas Tutorial

Pandas Web Scraping

Once you get it with DataFrame, it’s easy to post-process. If the table has many columns, you can select the columns you want. See code below:

Web Scraping Using Pandas Using

Then you can write it to Excel or do other things:

Web Scraping Through Pandas

Related course:Data Analysis with Python Pandas