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nrennie.rbind.io | ||
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blog.djnavarro.net
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| | | | | In a multi-language polyglot data science world, it becomes important that we are able to pass large data sets efficiently from one language to another without making unnecessary copies of the data. This post is the story of how I learned to love using reticulate to pass data between R and Python, especially when used together with Apache Arrow which prevents wasteful copying when the handover takes place | |
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jcarroll.com.au
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| | | | | I've been using the reticulate package occasionally for a while now, so I was surprised to see that it had only just been officially released. reticulate: R interface to Python https://t.co/qVWmwoMQAP. Comprehensive set of interoperability tools including R Markdown Python engine #rstats #pydata pic.twitter.com/SuWM6Y3Pk0 - RStudio (@rstudio) March 26, 2018 | |
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www.ardanlabs.com
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| | | | | Series Index Python and Go: Part I - gRPC Python and Go: Part II - Extending Python With Go Python and Go: Part III - Packaging Python Code Python and Go: Part IV - Using Python in Memory Introduction In the previous post we saw how a Go service can call a Python service using gRPC. Using gRPC to connect a Go and Python program together can be a great choice, but there's a complexity price that goes with it. | |
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blogditifet.com
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