regression-js VS Pandas

Modeling: We are currently exploring this space so our findings are not final yet but let me share what we've found so far. TensorFlow.js is absolutely amazing, it provides a native port from TensorFlow to JavaScript with CPU, WebGL, WebAssembly and NodeJS backends. We were able to write an LSTM to do time series prediction, so far so good. However, we started hitting issues when we started to do simpler models, like a linear regression. We tried Regression.js but we found it falls short, so we wrote our own script to handle single-variable regressions using TF.js and gradient decent. It actually worked quite well but exposed a flaw in this approach; basically, there is a lot of work to be done to bring many models into the web. We also found out Arquero.js does not play nicely with TF.js since well, Arquero.js does not support tensors; so we went on to explore Danfo.js, which integrates great with TF.js but we found out it's hard to scale it's transformations to +1M rows and found other rough edges. Since then, well, we started exploring Pyodide and perhaps contributing to Danfo.js, or perhaps involve more server-side compute, but that will be a story for another time.

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.

The AI Service will be built using aiohttp (asynchronous Python web server) and integrates PyTorch, Hugging Face Transformers, numpy, pandas, and scikit-learn for financial data analysis.