- Python Web Scraping Tutorial
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- Python Web Scraping Resources
Python Web Servers
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Web scraping, also called web data mining or web harvesting, is the process of constructing an agent which can extract, parse, download and organize useful information from the web automatically.
This tutorial will teach you various concepts of web scraping and makes you comfortable with scraping various types of websites and their data.
This tutorial will be useful for graduates, post graduates, and research students who either have an interest in this subject or have this subject as a part of their curriculum. The tutorial suits the learning needs of both a beginner or an advanced learner.
Python Web Scraper Code
The reader must have basic knowledge about HTML, CSS, and Java Script. He/she should also be aware about basic terminologies used in Web Technology along with Python programming concepts. If you do not have knowledge on these concepts, we suggest you to go through tutorials on these concepts first.
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
Pandas web scraping
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:
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:
Related course:Data Analysis with Python Pandas