Web Scraping Using Pandas



Loading web scraping results into Pandas DataFrame. I am new to coding/scraping so any help will greatly appreciated. Thanks in advance for your time and effort! Python json pandas dataframe web-scraping. Improve this question. Follow asked Oct 29 '18 at 17:34. 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.

APIs are not always available. Sometimes you have to scrape data from a webpage yourself. Luckily the modules Pandas and Beautifulsoup can help!

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Web scraping

Pandas has a neat concept known as a DataFrame. A DataFrame can hold data and be easily manipulated. We can combine Pandas with Beautifulsoup to quickly get data from a webpage.

Web scraping Pandas has a neat concept known as a DataFrame. A DataFrame can hold data and be easily manipulated. We can combine Pandas with Beautifulsoup to quickly get data from a webpage. Find the best information and most relevant links on all topics related toThis domain may be for sale!

If you find a table on the web like this:

We can convert it to JSON with:

And in a browser get the beautiful json output:

Converting to lists

Rows can be converted to Python lists.
We can convert it to a dataframe using just a few lines:

Pretty print pandas dataframe

Scraping

You can convert it to an ascii table with the module tabulate.
This code will instantly convert the table on the web to an ascii table:
This will show in the terminal as:

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.

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

Web Scraping Using Pandas

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.

Web Scraping Using Pandas

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?

Web Scraping With Pandas And Beautifulsoup

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! Fasribblog.

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