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. I'm Azhar and welcome to my new video series on Python Pandas. In this series I'm going to teach you about Pandas one of the most downloaded lib.

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.

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

Web Scraping With Pandas

Pandas web scraping

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. Find the best information and most relevant links on all topics related toThis domain may be for sale! 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.

Install modules

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

pands.read_html()

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 Tutorial

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:

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:

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

Web Scraping Using Pandas

Related course:Data Analysis with Python Pandas

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:

Web Scraping Using Pandas Java

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

Web Scraping Through Pandas

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: