Pylint VS Pandas

A little introduction about pylint. Pylint is a static code analyzer, it analyses your code without actually running it. Pylint looks for potential errors, gives suggestions on coding standards that your code is not adhering to, potential places where refactoring might help, and also warnings about smelly code.

Pylint, on the other hand, focuses on code analysis and style checking. It offers extensive customization options and supports various coding standards. Pylint is known for its comprehensive reports and ability to detect a wide range of code issues.

In my opinion, the best way to expose configuration options is to read and parse them from the project's pyproject.toml file. Here's how Pylint handles it.

I even contributed to Pylint by submitting a new rule a few years ago : implicit-str-concat.

Implemented code formatting with Black and linting with Pylint in my CI pipeline. Here is my updated GitHub Actions Workflow file: ci.yml

One last thing to do before running the hooks is to create a config file, just like we did with flake8. For this you are going to create a pylintrc file at the roor of your project and copy the contents of the pylintrc file from the pylint repo (here is the link to it).

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.