JRuby VS Pandas

Another way to look at it based on coming across it in enterprise:

How did he build something adopted by so many enterprises?

It does some things at scale very well and has been afforded the performance improvements of very smart people for 30y.

It’s not to say the language isn’t verbose, one of my favourite features was the ability to write code in other languages right inside the a Java app pretty well in-line by using the JVM, thanks to JSR-223.

It was possible to write Ruby or Python code via Jruby or Jython and run it in the JVM.

https://www.jython.org/

https://www.jruby.org/

https://docs.oracle.com/javase/8/docs/technotes/guides/scrip...

As someone who has looked at Shoes several times but never dove in, it's confusing how Shoes 4 has been the "preview version" of Shoes for, like, a decade or more. It made me actively avoid getting invested in Shoes 3 (the release promoted on the linked website) because Shoes 4 requires JRuby and I am happy with CRuby (the Ruby interpreter most people think of when they hear "Ruby").

https://github.com/shoes/shoes4/

http://www.rubydoc.info/github/shoes/shoes4

No disrespect to the developers but to me it feels like taking over a GUI toolkit created "to teach programming to everyone" (to quote the Shoes 4 readme) and making it depend upon a super-complicated enterprise-focused Ruby was sort of Missing The Point™ in a huge way.

Heck I couldn't even switch to JRuby if I wanted to because I <3 Ractors and JRuby still lacks CRuby 3.0 feature parity: https://github.com/jruby/jruby/issues/7459

__callee__ now properly returns the name under which a method was called, which will be the new name in the case of aliased methods. #2305, #7702

Issue tracker: https://github.com/jruby/jruby/issues

rdoc has been updated to 6.3.3 to fix all known CVEs. (#7396, #7404)

Altering the visibility of an included module method no longer changes what super method gets called. (#7240, #7343, #7344, #7356)

It looks like the readme is copy pasta from jruby: https://github.com/jruby/jruby

Homepage: https://www.jruby.org/

👉 https://pandas.pydata.org/

Libraries for data science and deep learning that are always changing

# Read the content of nda.txt try: import os, types import pandas as pd from botocore.client import Config import ibm_boto3 def __iter__(self): return 0 # @hidden_cell # The following code accesses a file in your IBM Cloud Object Storage. It includes your credentials. # You might want to remove those credentials before you share the notebook. cos_client = ibm_boto3.client(service_name='s3', ibm_api_key_id='api-generated', ibm_auth_endpoint="https://iam.cloud.ibm.com/identity/token", config=Config(signature_version='oauth'), endpoint_url='https://s3.direct.us-south.cloud-object-storage.appdomain.cloud') bucket = 'your-bucket-referenced-here' object_key = 'nda__da__crxq8b2hmy.txt' # load data of type "text/plain" into a botocore.response.StreamingBody object. # Please read the documentation of ibm_boto3 and pandas to learn more about the possibilities to load the data. # ibm_boto3 documentation: https://ibm.github.io/ibm-cos-sdk-python/ # pandas documentation: http://pandas.pydata.org/ streaming_body_1 = cos_client.get_object(Bucket=bucket, Key=object_key)['Body'] with open("nda.txt", "r") as f: nda_content = f.read() print("Content of nda.txt has been read.") except FileNotFoundError: print("Error: nda.txt not found in the current directory.") nda_content = "" # Initialize knowledge source content_source = CrewDoclingSource( file_paths=["..."] )

As with any web scraping or data processing project, I had to write a fair amount of code to clean this up and shape it into a format I needed for further analysis. I used a combination of Pandas and regular expressions to clean it up (full code here).

Can this be merged into pandas?

Pandas does not currently install tqdm by default.

pandas-dev/pandas//pyproject.toml [project.optional-dependencies] https://github.com/pandas-dev/pandas/blob/main/pyproject.tom...

Dask solves for various adjacent problems; IDK if pandas, dask, or dask-cudf would be faster with async?

Dask docs > Scheduling > Dask Distributed (local) https://docs.dask.org/en/stable/scheduling.html#dask-distrib... :

> Asynchronous Futures API

Dask docs > Deploy Dask Clusters; local multiprocessing poll, k8s (docker desktop, podman-desktop,), public and private clouds, dask-jobqueue (SLURM,), dask-mpi:

They are when the only reason they are flagged as security updates is because some a single group deems a very rare, obscure edge case as a HIGH severity vuln when in practice it rarely is => this leads to having to upgrade a minor version of a library that ends up causing breaking changes.

This is the recent thread I'm down. Pandas 2.2 broke SQLalchemy backwards compatibility: https://stackoverflow.com/questions/38332787/pandas-to-sql-t... + https://github.com/pandas-dev/pandas/issues/57049#issuecomme...

Python’s Growth in Data Work and AI: Python continues to lead because of its easy-to-read style and the huge number of libraries available for tasks from data work to artificial intelligence. Tools like TensorFlow and PyTorch make it a must-have. Whether you’re experienced or just starting, Python’s clear style makes it a good choice for diving into machine learning. Actionable Tip: If you’re new to Python, try projects that combine data with everyday problems. For example, build a simple recommendation system using Pandas and scikit-learn.

This tutorial provides a concise and foundational guide to exploring a dataset, specifically the Sample SuperStore dataset. This dataset, which appears to originate from a fictional e-commerce or online marketplace company's annual sales data, serves as an excellent example for learning and how to work with real-world data. The dataset includes a variety of data types, which demonstrate the full range of capabilities that the Pandas library, written in Python can offer.

Data preprocessing and manipulation: Libraries like Pandas solve for the messy, real-world challenge of efficiently wrangling and cleaning large datasets. Without it, you'd be reinventing functionality for basic tasks like merging, filtering, or aggregating data.