Commander.js VS Pandas

Visit the Commander.js reference.

In the previous article, I looked at a Commander to configure and accept cli options and assign it to a variable called program.

You can write a command line utility using zx or commander.js. Hit a public api, spit stuff out in the console, etc.

In your index.js you can do whatever you want, even create an interactive CLI (check commander).

There is clearly a whole load of repetition, so this should be fairly easy to build and parameterise. Essentially this will all just be string building so we won't need to use any particular libraries for most of this script. We will need a way to call ffmpeg though - and ffmpeg will need to be present too, of course. To call a CLI command we can use the package commander.

Commander.js is an NPM package that makes it easier to build CLI tools. You can find its documentation over here

You can also use npm package commander to make more complex command line tool with lot of options and sub commands.

commander - npm - Required. A library that lets you define the commands and their arguments, options, help, etc.

Throughout my time writing and updating my static-site generator, I've been using npm from the very foundation I use an npm package called commander. Therefore, it is obvious that for the tool that I will be using to publish my ssg, I will do so with npm.

The command line has thousands of tools, such as awk, sed, grep, and find available at your disposal that cut development time and automate tedious tasks. Creating a command line tool in Node.js isn't very complicated, thanks to a powerful library like Commander.js.

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.