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Persisting ML/AI models (or model serialization) allows users to save models that they’ve trained so that they can be reloaded and reused. Model persistence is important as it can save time and allows models to be shared with other users without the need to retrain the model (which can be computationally expensive and time consuming) or share the training data set (which can be large).
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Many of Pandas read functions have the ability to read files from the internet. This can be helpful you’re writing tutorials or examples and want them to work even if users don’t have the data. Or if you want to quickly look at data without downloading it.
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Having problems accessing Anaconda from VS Code (Python: Select Interpreter)?
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Often when working in science/research we are rapidly developing and modifying code (and maybe not following the very best practices for coding). In these instances we may not be writing, developing, or installing Python modules which can be accessed by the Python interpreter. Rather we may have a directory with set of scripts, functions, or classes. In Python in order to access these without installing a module we need to add the file path of our directory to the Python path. This can be easily acomplished using the sys module.
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Environments in Anaconda and Python allow you to create a named and isolated copy of Python. Within these environments you can work with specefic versions of Python and packages without affecting your base installation. These enviroments make it easy to:
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Experiencing weird scaling issues with Spyder?
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Ever been actively editing a module while also using it? IPython (and JUPYTER) have some automagic to help with this.
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Have you found your Anaconda enviroments stalling when trying to install new packages via conda?