parsel VS Pandas

An alternative for beautifulsoup is https://github.com/scrapy/parsel also from the scrapy team.

variable.css(".X5PpBb::text").get() # returns a text value variable.css(".gs_a").xpath("normalize-space()").get() # https://github.com/scrapy/parsel/issues/192#issuecomment-1042301716 variable.css(".gSGphe img::attr(srcset)").get() # returns a attribute value variable.css(".I9Jtec::text").getall() # returns a list of strings values variable.xpath('th/text()').get() # returns text value using xpath

Something I don't see discussed when this topic is brought up is that Scrapy's HTML parsing library, parsel, can be installed separately from scrapy itself. You can use it in place of beautifulsoup and, imo, it's much easier to use.

Python is a natural fit for serverless development. It boasts a vast array of libraries, including Powertools for AWS and robust libraries for data engineers. Its versatility and excellent developer experience make it a top choice for serverless projects, offering a seamless and enjoyable development experience.

In data analysis, managing the structure and layout of data before analyzing them is crucial. Python offers versatile tools to manipulate data, including the often-used Pandas reset_index() method.

Dash is a Python framework that enables you to build interactive frontend applications without writing a single line of Javascript. Internally and in projects we like to use it in order to build a quick proof of concept for data driven applications because of the nice integration with Plotly and pandas. For this post, I'm going to assume that you're already familiar with Dash and won't explain that part in detail. Instead, we'll focus on what's necessary to make it run serverless.

there is pull request to integrate in both pydantic extra types and into pandas cose [1]

[1]: https://github.com/pandas-dev/pandas/issues/53999

Last year I worked through the challenges using VisiData, Datasette, and Pandas. I walked through my thought process and solutions in a series of posts.

pandas: A library for data analysis in Python

Data analysis involves scrutinizing datasets for class imbalances or protected features and understanding their correlations and representations. A classical tool like pandas would be my obvious choice for most of the analysis, and I would use OpenCV or Scikit-Image for image-related tasks.

Pandas, a powerful data manipulation library in Python, has become an essential tool for data scientists and analysts. One of its key functions is read_csv(), which allows users to read data from CSV (Comma-Separated Values) files into a Pandas DataFrame. In this tutorial, brought to you by CodesWithPankaj.com, we will explore the intricacies of read_csv() with clear examples to help you harness its full potential.