Pandas

Introduction:

In this article, we will explore how to extract domain names and ending strings from a list of URLs using Python and the Pandas library. Extracting these components from URLs can be useful for various tasks, such as analyzing website data or organizing URLs based on their domains and endings. We will walk through the process step-by-step, covering string manipulation techniques and creating a Pandas DataFrame to store the extracted information. Throughout the process, I gained insights into fundamental concepts of Pandas.

Problem Description:

Given a list of URLs, we need to extract the domain names and ending strings from each URL. The domain name refers to the main part of a URL that identifies a specific website, while the ending string represents the file extension or the last part of the URL.

Solution:

We start by initializing a list of URLs. Each URL is then processed individually to extract the domain name and ending string. We use Python's string split() method to split the URL into different components. The domain name is obtained by splitting the URL using the "//" delimiter and extracting the relevant portion. The ending string is extracted by splitting the URL based on "/" and then extracting the last part using various split operations.

Once we have extracted the domain names and ending strings, we create two separate lists to store this information. We iterate over each URL in the list and append the extracted domain name and ending string to their respective lists. We can then use these lists to create a Pandas DataFrame using the zip() function. The zip() function combines the two lists element-wise into tuples, which can be directly passed to the DataFrame constructor. Finally, we specify the column names and display the resulting DataFrame.

Conclusion:

Extracting domain names and ending strings from URLs can be accomplished using string manipulation techniques in Python. By splitting the URLs and extracting the desired components, we can create structured data for further analysis or organization. The Pandas library provides powerful tools for working with tabular data, allowing us to create a DataFrame to store the extracted information conveniently. Understanding how to extract and process components from URLs is a valuable skill for various data analysis and web-related tasks.