Web Scraping Using Pandas

Web Scraping Using Pandas Java
Web Scraping Through Pandas

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.

Pandas Web Scraping. Pandas makes it easy to scrape a table ( tag) on a web page.After obtaining it as a DataFrame, it is of course possible to do various. Open a new Jupyter notebook. You do have it installed, don’t you? You didn’t just skip the advice at. 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. Python Web Scraping using Python 3.6 and BeautifulSoup library and analyzing them using the Pandas library. Install dependencies - pip install -r requirements.txt. Example 1 - IMDB Movies List. Here we have done the data scraping from a webpage by using the BeautifulSoup library to find and print the movie title, list of genres, runtime and scores of all movies.

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

Related Course:Complete Python Programming Course & Exercises

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.

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

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

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.