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Source channel @githubtrending · Post #15438 · Jan 26
#python#agents#ai#ai_engineer#ai_engineering#copilot#data_science#data_scientist#generative_ai#gpt#machine_learning#ml_engineer#ml_engineering#openai AI Data Science Team is a free Python library with AI agents that speed up your data work 10X by handling loading, cleaning, visualization, EDA, feature engineering, modeling, and SQL tasks. Its flagship AI Pipeline Studio app creates visual, reproducible pipelines you can run with Streamlit after easy install (Python 3.10+, OpenAI or Ollama). This saves you hours on repetitive jobs, boosts accuracy, and lets you focus on insights and business results. https://github.com/business-science/ai-data-science-team
Search: #sequential
@djangoproject · Post #98 · 07/11/2016, 12:22 PM
https://docs.python.org/3/library/asyncio.html #asyncio #Asynchronous programming is more complex than classical “#sequential” programming: see the Develop with asyncio page which lists common traps and explains how to avoid them. Enable the debug mode during development to detect common issues.
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@djangoproject · Post #153 · 09/03/2016, 08:20 PM
http://wla.berkeley.edu/~cs61a/fa11/lectures/streams.html In this chapter, we continue our discussion of real-world applications by developing new tools to process #sequential#data. In Chapter 2, we introduced a sequence interface, implemented in Python by built-in data types such as #tuple and #list. #Sequences supported two operations: querying their length and accessing an element by index. In Chapter 3, we developed a user-defined implementations of the sequence interface, the Rlist class for representing recursive lists. These sequence types proved effective for representing and accessing a wide variety of sequential #datasets.