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* nxviz version: 0.6.0 #540
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*nxviz version: 0.6.0 Description Requirement already satisfied: nxviz in c:\users\nicole wheatley\appdata\roaming\python\python37\site-packages (0.6.0) Since it seemed to be installed, I tried importing nxviz into jupyter, but error says no such module
Is module saved in the correct folder? Why is it in appdata/roaming? and not in normal Python37/Lib ? |
@nwheatle yes, this does look like an issue with the package being installed into the incorrect environment. One thing that is important for Jupyter users is to ensure that the Python kernel from the environment is accessible by Jupyter. To do this:
Naturally, replace The reason for this architectural design is because you, as a Jupyter user, may want to access both an R and Python kernel installed in the same conda environment, in which that conda environment serves as your data science environment. Hence, it makes no sense to assume that that only one compute kernel is associated with each environment. I learned this the hard way 😄. Now, if the aforementioned command doesn't work, I would suggest going upstream to Jupyter for help on how to solve this issue, as this is not an issue pertaining to bugs in nxviz's codebase, but rather issues of discovery of Python kernels for Jupyter. |
Hi Eric, Despite some online search, I cannot figure out the name of my_env_name. I do not use conda or anaconda. My kernel name in jupyter lab notebook is Python 3. @JKDev on stackoverflow shows how to find an environment name from jupyter that is using conda environment, https://stackoverflow.com/questions/37085665/in-which-conda-environment-is-jupyter-executing , but I cannot see any env_name that is supposed to show up ~ unless the envname is '3'. I am following along to your DataCamp course (it's good!). I try to apply the course material to my own computer on jupyter as I go - knowing how to install things, how computers work in general, import the data, etc, is part of the challenge for a biologist entering data science. |
@nwheatle got it. I would advise using an Anaconda installation of Python, as this would allow you to isolate different environments for different projects. On my old blog, you'll read about why this is important. The short story is that I got utterly confused with where things were getting installed back in my early days of Python, and then came to the realization that I had to properly think about isolated environments in order to not get confused. Hope the DataCamp course is useful for you 😄. And welcome to the world of data science. There's lots of superpowers to learn! |
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