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Get work done, and have seconds added back to your life since you won't be writing second, third or — *gasp!* — fourth line follow-ups to your impeccable typed line numero uno.
Open your shell history before you open a course catalog. The jq filters, the grep -v chains against Zeek logs, the PowerShell one-liners you paste into a ticket every week: that is your toolkit.
In the rapidly evolving landscape of modern data engineering, we have developed an almost reflexive habit of reaching for Apache Spark the moment we need to ingest, move, or process data. Microsoft Fabric provides a robust, pre-configured default Spark runtime that works beautifully out of the box.
RAG retrieves. Agents act. I built both separately, connected them explicitly, and ran the same nine tasks through all three systems. The post RAG Isn't an Agent — I Built the Layer Between Retrieval and Action appeared first on Towards Data Science.
Nine probe notebooks, three export paths, twenty-seven conversion attempts, and a count of what actually survives into each PDF. The LaTeX route keeps every column of a wide DataFrame and drops four of eight Unicode strings; the browser routes do the reverse, and clip a long source line as well.
pip install lightpanda gives you a headless browser and its Python bindings in one command, browser included. Scrape JavaScript-rendered pages and hand the data straight to pandas.
The package imports in your terminal and fails in the notebook. Four virtual environments, nine notebook runs, and the one line of jupyter_client source that decides which Python your kernel actually runs — with the audit script that answers it for your machine.
Machine learning can sound much more complicated than it actually is. You hear words like models, training, features, datasets, predictions, and algorithms, and it can feel like you need a PhD in math
Evaluation measures performance using metrics like precision, recall, and relevancy, providing a clear picture of your pipeline’s strengths and weaknesses using LLMs or ground-truth labels.
A repeatable workflow for building large, realistic vector test datasets: CC News to Parquet to local embeddings to Pinecone bulk import.
tsb is a from-scratch TypeScript port of pandas, being built almost entirely by Autoloop — one iterative improvement at a time.
Cite GitHub code, Zenodo releases, PyPI & CRAN packages in MLA 9, APA 7, Chicago & IEEE — with worked examples and how to get a Zenodo DOI.
Step-by-step RDKit tutorial for chemists. Install RDKit, draw molecules, calculate properties, and run similarity searches in Python — no coding experience required.
Run DESeq2 differential expression analysis entirely in Python with PyDESeq2. Covers count matrices, normalization, statistical testing, LFC shrinkage, and visualization.
Learn how to delete columns in a pandas DataFrame using drop(), del, pop(), column filtering, and loc/iloc. Includes code examples and a comparison table.
We are proud to introduce the {mall} package. With {mall}, you can use a local LLM to run NLP operations across a data frame. (sentiment, summarization, translation, etc). {mall} has been simultaneously released to CRAN and PyPi (as an extension to Polars).
In this tutorial, we look at the TextAttack framework for NLP data augmentation, adversarial training, and adversarial attacks.
In this article, we will show you how to use Haystack to evaluate the performance of a RAG pipeline. Note that the code in this article is meant to be illustrative and may not run as is; if you want to run the code, please refer to the python script.
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