ipykernel VS Pandas

The way I work is to use an interactive IPython REPL. It has a lot of features to make interactive development comfortable. I solve a problem incrementally, attempting to get a little piece of code right many times, changing it a little each time to fix bugs. Then I go back and copypaste the lines into a file and tidy it up a little. Then I go back to IPython and solve the next little stage of the problem. Maybe this style of development will be more fitting for you?

IPython

If you're talking about history in the python shell, you're probably looking for IPython.

For this kind of thing I mostly use Jupyter and the IPython kernel, with data analysis packages like numpy and pandas and matplotlib for charts.

Otherwise you should be able to convert the .ipynb's to .py using the ipython libraries:

Nice product, I noticed that you are updating the next Jupyter cell; what was your solution to doing that reliably since `set_next_input` is so damn flakey?

I personally grew so frustrated with the state of GUI development in Jupyter that I tried to fix it in such a way that would allow proper message passing between cells and python code (because you can't wait on Comm events).

> https://github.com/ipython/ipykernel/pull/589

But sadly the priorities of big open source projects don't always match your own. So I had to extract that logic into my own kernel.

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.

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.