Sinatra VS Pandas

Compared to Ruby on Rails, a full-stack web framework, Sinatra is a very lean micro-framework originally developed by Blake Mizerany to help Ruby developers build applications with "minimal effort".

a Sinatra app hosted on Heroku on the domain http://example.com

Let's say I have the simplest single-file Sinatra app. The hello world on their homepage will do. I want to run it under Apache with Phusion Passenger, AKA mod_rails.

Sinatra is the oldest competitor of Rails. Although it's not that popular anymore, it's still used in some simple and performance focused services.

Sinatra is fine as long as you have a small number of routes, because its router is O(n): https://github.com/sinatra/sinatra/blob/ac5404dbd897a4b3c5b3921d2f89fecd3b540ed3/lib/sinatra/base.rb#L1008-L1010

https://github.com/sinatra/sinatra A classy web-development dressed in a DSL (official / canonical repo)

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.