CPython VS Pandas

The Python function is implemented in C and uses a faster algorithm [1]. It was a strange choice for a demo.

[1] https://github.com/python/cpython/blob/0d9d48959e050b66cb37a...

In another incident, I pasted a multi-page Python stack trace into Forge and it quickly isolated which import was failing. For enterprise-scale projects with tangled dependencies, having an AI assistant sift through logs and point at the faulty module is a huge time-saver.

Shell agents also inherit your shell’s environment settings automatically. They see your PATH, version manager configurations, and any container or virtual environment you’ve loaded. For instance, if you’re on Python 3.313 via pyenv or inside a Docker container, the agent picks that up immediately. It even knows your current Git branch and environment variables like NODE_ENV or DATABASE_URL. As a result, the AI won’t accidentally run code in the wrong interpreter or miss a critical setting, everything matches your actual environment.

Writing comprehensive unit and integration tests is one of the best ways to ensure code quality – but it’s also labor-intensive. AI can help automate this tedious task. New AI assistants (like GitHub Copilot) can analyze Python functions and automatically generate meaningful test cases. As one developer puts it, “progress in AI has opened doors to automated test generation…presenting developers with an innovative method for creating code tests.”. With these tools, you can often click a button or prompt the AI with a function, and it will produce a suite of unit tests covering normal and edge-case inputs. The generated tests can be easily reviewed and tweaked, saving developers hours of manual writing.

Ready to start your Python journey? Visit the official Python website (https://www.python.org) to download the latest version and explore tutorials. Happy coding!

Install Python To start your Python journey, you first need to install the Python interpreter. You can download the latest version from the official Python website (https://www.python.org/). Installation is straightforward, and once completed, you can verify the installation by typing python --version in your command line.

Cool, but looks like it's going to miss capabilities, so not suitable for a full OS backup (see https://github.com/python/cpython/issues/113293)

Download and install Python from python.org. Ensure the option “Add Python to PATH” is selected during installation.

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.