osdc-2023-01-public VS Pandas

Welcome back dear reader. Let me remind you that I am Silent-Mobius, also known as Alex M. Schapelle, your faithful narrator. As you might remember, I have applied to short journey of sort, whose main orchestrator is @szabgab, under a code name of #OSCD. Despite the fact the journey is still under its first steps, I've been willing to remember it with any possible tool I can have under my utility belt. That is why I've decided, to use #git. Git is a free and open source distributed version control system designed to handle everything from small to very large projects with speed and efficiency.

As part of the fourth assignment of the OSDC we had to practice GitHub and create a new project.

I am taking Open Source Development Course given by Gabor Szabo in order to gain a better understanding of this field. In the second session we learned about github pages which is an easy way for building a website. Anyone can do it, even if they don't have programming experience. All you need is a github account!

Through a shared colleague, I recently got to know Gabor Szabo. Luckily, he invited me to join his Open Source Development Course.

And he also created a practice website which we can update

I have done my second assignment for the Open Source Development course (OSDC). The assignment was to practice markdowns in GitHub.

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.