ipython VS Pandas

If you’re already using ipython, this isn’t a problem because you’ll already need to download most of these dependencies anyway. But if you’re not using ipython… you’ll still need to download those dependencies.

!pip install is error-prone, it is better to use %pip install, ipython even warns about this, https://github.com/ipython/ipython/pull/12954/

The git history (here is the git blame) shows it has not been updated in 9 year. Looks like a documentation issue that you should open an issue against.

Do you know IPython? It is a modern Python console that extends the capabilities of the classic builtin Python shell by offering introspection, tab completion, syntaxing coloring, as well as history. If you don't know it, I can't recommend it enough. More information can be found in its GitHub page.

There is an IPython ticket on GitHub on the topic, but it's unclear if the problem has been solved.

For further documentation on ipython using the CLI, you can refer to the GitHub link or the documentation page.

> ??????_ Traceback (most recent call last): File "/home/nvaughn4/bin/miniconda3/envs/newprime/bin/ipython", line 11, in sys.exit(start_ipython()) File "/home/nvaughn4/bin/miniconda3/envs/newprime/lib/python3.6/site-packages/IPython/__init__.py", line 126, in start_ipython return launch_new_instance(argv=argv, **kwargs) File "/home/nvaughn4/bin/miniconda3/envs/newprime/lib/python3.6/site-packages/traitlets/config/application.py", line 658, in launch_instance app.start() File "/home/nvaughn4/bin/miniconda3/envs/newprime/lib/python3.6/site-packages/IPython/terminal/ipapp.py", line 356, in start self.shell.mainloop() File "/home/nvaughn4/bin/miniconda3/envs/newprime/lib/python3.6/site-packages/IPython/terminal/interactiveshell.py", line 563, in mainloop self.interact() File "/home/nvaughn4/bin/miniconda3/envs/newprime/lib/python3.6/site-packages/IPython/terminal/interactiveshell.py", line 554, in interact self.run_cell(code, store_history=True) File "/home/nvaughn4/bin/miniconda3/envs/newprime/lib/python3.6/site-packages/IPython/core/interactiveshell.py", line 2858, in run_cell raw_cell, store_history, silent, shell_futures) File "/home/nvaughn4/bin/miniconda3/envs/newprime/lib/python3.6/site-packages/IPython/core/interactiveshell.py", line 2880, in _run_cell elif self.should_run_async(raw_cell): File "/home/nvaughn4/bin/miniconda3/envs/newprime/lib/python3.6/site-packages/IPython/core/interactiveshell.py", line 2918, in should_run_async return _should_be_async(cell) File "/home/nvaughn4/bin/miniconda3/envs/newprime/lib/python3.6/site-packages/IPython/core/async_helpers.py", line 161, in _should_be_async code = compile(cell, "<>", "exec") UnicodeEncodeError: 'utf-8' codec can't encode characters in position 537-542: surrogates not allowed If you suspect this is an IPython 7.15.0 bug, please report it at: https://github.com/ipython/ipython/issues or send an email to the mailing list at [email protected] You can print a more detailed traceback right now with "%tb", or use "%debug" to interactively debug it. Extra-detailed tracebacks for bug-reporting purposes can be enabled via: %config Application.verbose_crash=True sys:1: RuntimeWarning: coroutine 'InteractiveShell.run_cell_async' was never awaited

There’s one I’ve come across recently here where you’re fighting against syntax highlighting with extra error context. https://github.com/ipython/ipython/issues/13446#issuecomment...

I see. https://github.com/ipython/ipython/issues/13529

You can automate setup for developers using this simple script:

https://github.com/ipython/ipython/pull/12091/files

And here’s a GitLab issue requesting support for blame-ignore:

https://gitlab.com/gitlab-org/gitlab/-/issues/31423

I don’t think there’s a corresponding GitHub request, but maybe if GitLab adds this feature GitHub will have some incentive to follow suit.

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.