serverless-offline VS Pandas

Using local emulators like sam local, serverless-offline, localstack, etc.

Hi, I'm the creator of serverless-offline (https://github.com/dherault/serverless-offline), a NPM package for local serveless development on AWS.

I'm building a cool product and intend to launch Q1 2023. To gain traction from my target customers, I plan to place a discrete ad at the launch of every serverless-offline instance.

Does it seem like an ok move to you? Or is it something that repels you?

Best,

As the creator of serverless-offline, I am well placed to tell you that this is the time-effective solution. Plus, it costs way less than other solutions at smaller scales. But, again, going Kubernetes or otherwise will be a problem for the future.

https://github.com/dherault/serverless-offline https://github.com/lambci/docker-lambda

What if I didn’t need to push code to AWS every time I wanted to test something? All heroes don’t wear capes. Like a knight in shining armor, Serverless Offline comes barging in to save the day! At least now I can test all my code locally before pushing it to AWS. That’s a relief.

With classic Express servers, you can use a simple node script to get the server up and running to test locally. Serverless wants to be run in the AWS ecosystem making it. Lucky for us, David Hérault has built and continues to maintain serverless-offline allowing us to emulate our functions locally before we deploy.

With the serverless offline plugin you can speed up local dev is by emulating AWS lambda and API Gateway locally when developing your Serverless project.

Those mocks are by definition not real services so there are some behavior differences between the local environment and the cloud provider. For example, AWS API Gateway emulated by serverless-offline doesn't handle VTL locally the same as AWS does.

serverless-offline (https://github.com/dherault/serverless-offline) is a great tool to use for local development of serverless applications.

It's not a complete mirror image of what you get but it's close enough in my experience.

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.

So, what I'd like to do is write a documentation package in Python to recreate what I've lost. I plan to build upon the fantastic python-docx and docxtpl packages, and I'll probably rely on pandas from much of the tabular stuff. Here are the features I intend to include:

Weirdly most of my time spent with data analysis was in the C layers in pandas.

Explore here.