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Source channel @githubtrending · Post #14936 · Jul 9

#jupyter_notebook This course guides you through building and deploying your own AI agents using popular tools like OpenAI Agents SDK, CrewAI, LangGraph, AutoGen, and MCP over six weeks. You’ll learn to create agents that can think, act, and work together, with clear setup instructions for Windows, Mac, and Linux, plus support if you get stuck. The benefit is that you gain hands-on experience in the latest AI agent technology, making you ready to develop smart, autonomous systems for real-world tasks, while also connecting with a helpful community and having fun along the way[1][2][3]. https://github.com/ed-donner/agents

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djangoproject

@djangoproject · Post #316 · 04/28/2017, 06:09 AM

https://github.com/blissnd/easyxls Convert any #spreadsheet into a Python internal #dict/#array data structure, for easy processing. Can also handle pivot tables. For pivot table usage, header_row_start & header_col_start need to be set equal to the top left corner of the pivot table => header_row_start=8, header_col_start='c' in the included example. Column IDs must always be lowercase chars in quotes, e.g. 'a'.

djangoproject

@djangoproject · Post #129 · 08/31/2016, 03:36 PM

https://pypi.python.org/pypi/numpy #NumPy is a general-purpose #array-processing package designed to efficiently manipulate large #multi-dimensional arrays of arbitrary records without sacrificing too much speed for small multi-dimensional #arrays. NumPy is built on the #Numeric code base and adds features introduced by #numarray as well as an extended #C-API and the ability to create arrays of arbitrary type which also makes NumPy suitable for interfacing with general-purpose #data-base applications.