Mainly Handling EDA Web scraping, also known as web data extraction, is the process of collecting information from websites. This can be done manually, but it is often done using automated tools and software. The data collected can be used for a variety of purposes, such as price comparison, data analysis, and research.
There are a number of libraries and frameworks available in various programming languages, such as Python, Java, and JavaScript, that can be used for web scraping. Some popular Python libraries include Beautiful Soup and Scrapy, while Java developers can use JSoup and web scraping frameworks such as Selenium.
When using web scraping tools or software, it is important to ensure that you are in compliance with the website's terms of service and privacy policy. Many websites prohibit or limit the use of web scraping, so it's important to be aware of any restrictions and to get permission if necessary.
Additionally, consider the ethical implications of web scraping. Some website owner may not appreciate if their website is scraped too often, causing their server load and could lead to blocking your IPs. It is important to limit the scraping rate and to identify yourself and your intentions when scraping a website.
In summary, web scraping is a powerful technique for extracting information from websites, but it is important to use it responsibly and in compliance with the website's terms of service and privacy policy.
Working with data can be challenging: it often doesn’t come in the best format for analysis, and understanding it well enough to extract insights requires both time and the skills to filter, aggregate, reshape, and visualize it. This session will equip you with the knowledge you need to effectively use pandas – a powerful library for data analysis in Python – to make this process easier.
Pandas makes it possible to work with tabular data and perform all parts of the analysis from collection and manipulation through aggregation and visualization. While most of this session focuses on pandas, during our discussion of visualization, we will also introduce at a high level Matplotlib (the library that pandas uses for its visualization features, which when used directly makes it possible to create custom layouts, add annotations, etc.) and Seaborn (another plotting library, which features additional plot types and the ability to visualize long-format data).
We will begin by introducing the Series, DataFrame, and Index classes, which are the basic building blocks of the pandas library, and showing how to work with them. By the end of this section, you will be able to create DataFrames and perform operations on them to inspect and filter the data.
To prepare our data for analysis, we need to perform data wrangling. In this section, we will learn how to clean and reformat data (e.g., renaming columns and fixing data type mismatches), restructure/reshape it, and enrich it (e.g., discretizing columns, calculating aggregations, and combining data sources).
The human brain excels at finding patterns in visual representations of the data; so in this section, we will learn how to visualize data using pandas along with the Matplotlib and Seaborn libraries for additional features. We will create a variety of visualizations that will help us better understand our data.
We will practice all that you’ve learned in a hands-on lab. This section features a set of analysis tasks that provide opportunities to apply the material from the previous sections.