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Hi. Even if currently its not an acceptable norm, correct and rigorous calculations in most cases require some form of uncertainty processing and may prove very beneficial if become a standard part of any workflow. Considering computational and "logistical" challenges (API, compatibility, etc) associated with treating the uncertainties as a separate task, I think that it must be integrated into the core libraries, like Efforts to support of uncertainties in What do you think about that? Are you aware of similar views or discussions among contributors or developers of core libraries like |
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I have been searching for a place where GUM users discuss GUM usage, but haven't found one. Does anyone know of such a place? |
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Hello, |
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What do you need to do exactly? What works already is to create a Pandas DataFrame with numbers with uncertainties (for example by initializing it through a NumPy array with uncertainties, as created for instance with Also, using, for example, |
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