nifi VS Pandas

Apache NIFI (https://nifi.apache.org/).

It uses the concept of Flow-based programming. Also its so underacknolged but this tool is very flexible. I have used as an Event Bus all the 3rd-Party Integrations.

Presently setting up Apache Nifi + Apache MiNiFi for the ETL portion of my work. NiFi was easy enough to figure out; but the docs for MiNiFi have been a pain due to differences between the Java and C++ versions. I then entirely configured it with the Java version so that it was easier to search for answers for the MiNiFi yaml syntax.

Found it

Apache Camel Apache Nifi Spring Cloud

NIFI, like most Apache projects does most of its discussion on its mailing lists, but also has a slack.

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.